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Real-Time Data Warehouse Examples (Real World Applications)

Discover how businesses are leveraging real-time data warehouses to gain actionable insights, make informed decisions, and drive growth..

Gone are the days when organizations had to rely on stale, outdated data for their strategic planning and operational processes. Now,  real-time data warehouses process and analyze data as it is generated, helping overcome the limitations of their traditional counterparts. The impact of real-time data warehousing is far-reaching. From eCommerce businesses to healthcare providers, real-time data warehouse examples and applications span various sectors.

The significance of real-time data warehousing becomes even more evident when we consider the sheer volume of data being generated today. The global data sphere is projected to reach a staggering  180 zettabytes by 2025 . 

With these numbers, it’s no wonder every company is looking for solutions like real-time data warehousing for managing their data efficiently. However, getting the concept of a real-time data warehouse, particularly when compared with a traditional data warehouse, can be quite intimidating, even for the best of us. 

In this guide, with the help of a range of examples and real-life applications, we will explore how real-time data warehousing can help organizations across different sectors overcome the data overload challenge.

What Is A Real-Time Data Warehouse?

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A  Real-Time Data Warehouse (RTDW) is a  modern tool for data processing that provides immediate access to the most recent data. RTDWs use real-time  data pipelines to transport and collate data from multiple data sources to one central hub, eliminating the need for batch processing or outdated information.

Despite similarities with traditional data warehouses, RTDWs are capable of  faster data ingestion and processing speeds . They can detect and rectify errors instantly before storing the data, providing consistent data for an effective decision-making process.

• Real-Time Data Warehouse Vs Traditional Data Warehouse

Traditional data warehouses act as storage centers for  accumulating an organization’s historical data from diverse sources. They combine this varied data into a unified view and provide comprehensive insights into the past activities of the organization. However, these  insights are often outdated by the time they are put to use , as the data could be days, weeks, or even months old.

On the other hand, real-time data warehousing brings a significant enhancement to this model by  continuously updating the data they house. This dynamic process provides a current snapshot of the organization’s activities at any given time, enabling immediate analysis and action. 

Let’s look at some of the major differences between the two.

Complexity & Cost

RTDWs are  more complex and costly to implement and maintain than traditional data warehouses. This is because they require more advanced technology and infrastructure to handle real-time data processing.

Decision-Making Relevance

Traditional data warehouses predominantly assist in long-term strategic planning. However, the real-time data updates in RTDWs make them  suitable for both immediate, tactical decisions and long-term strategic planning.

Correlation To Business Results

Because of fresher data availability, RTDWs make it easier to  connect data-driven insights with real business results and provide immediate feedback.

Operational Requirements

RTDWs demand constant data updates, a process that can be carried out without causing downtime in the data warehouse operations . Typically, traditional warehouses don't need this feature but it becomes crucial when dealing with data updates happening every week.

Data Update Frequency

While the lines between traditional data warehouses and real-time data warehouses are now blurred due to some data warehouses adopting streaming methods to load data, traditionally, the former updated their data in batches on a daily, weekly, or monthly schedule. As a result, the data some of these data warehouses hold may not reflect the most recent state of the business. In contrast, real-time data warehouses  update their data almost immediately as new data arrives.

• 3 Major Types Of Data Warehouses

Let's take a closer look at different types of data warehouses and explore how they integrate real-time capabilities.

Enterprise Data Warehouse (EDW)

An Enterprise Data Warehouse (EDW) is a  centralized repository that stores and manages large volumes of structured and sometimes unstructured data  from various sources within an organization. It serves as a comprehensive and unified data source for business intelligence, analytics, and reporting purposes. The EDW consolidates data from multiple operational systems and transforms it into a consistent and standardized format.

The EDW is designed to  handle and scale with large volumes of data . As the organization's data grows over time, the EDW can accommodate the increasing storage requirements and processing capabilities. It also acts as a  hub for integrating data from diverse sources across the organization . It gathers information from operational systems, data warehouses, external sources, cloud-based platforms, and more.

Operational Data Store (ODS)

An Operational Data Store (ODS) is designed to  support operational processes and provide real-time or near-real-time access to current and frequently changing data. The primary purpose of an ODS is to facilitate operational reporting, data integration, and data consistency across different systems. 

ODS collects data from various sources, like transactional databases and external feeds, and  consolidates it in a more user-friendly and business-oriented format.  It typically stores detailed and granular data that reflects the most current state of the operational environment. 

A  Data Mart is a specialized version of a data warehouse that is  designed to meet the specific analytical and reporting needs of a particular business unit , like sales, marketing, finance, or human resources.

Data Marts provide a more targeted and simplified view of data. It contains a  subset of data that is relevant to the specific business area , organized in a way that facilitates easy access and analysis.

Data Marts are  created by extracting, transforming, and loading (ETL) data from the data warehouse or other data sources and structuring it to support analytical needs. They can include pre-calculated metrics, aggregated data, and specific dimensions or attributes that are relevant to the subject area.

11 Applications Of Real-Time Data Warehouses Across Different Sectors 

The use of RTDWs is now common across many sectors. The rapid access to information they provide significantly improves the operations of many businesses, from online retail to healthcare.

Let’s take a look at some major sectors that benefit from these warehouses for getting up-to-the-minute data.

In the dynamic eCommerce industry, RTDWs facilitate immediate data processing that is used to get insights into customer behavior, purchase patterns, and website interactions. This enables marketers to  deliver personalized content, targeted product recommendations, and swift customer service . Additionally, real-time inventory updates help maintain optimal stock levels, minimizing overstock or stock-out scenarios.

RTDWs empower AI/ML algorithms with new, up-to-date data. This ensures models make predictions and decisions based on the most current state of affairs. For instance, in automated trading systems, real-time data is critical for  making split-second buying and selling decisions.

Manufacturing & Supply Chain

RTDWs support advanced manufacturing processes such as  real-time inventory management, quality control, and predictive maintenance . It provides crucial support for business intelligence operations. You can make swift adjustments in production schedules based on instantaneous demand and supply data to  optimize resource allocation and reduce downtime.

RTDWs in healthcare help improve care coordination. It provides  instant access to patient records, laboratory results, and treatment plans, improving care coordination . They also support real-time monitoring of patient vitals and provide immediate responses to critical changes in patient conditions.

Banking & Finance 

In banking and finance, RTDWs give you the  latest updates on customer transactions, market fluctuations, and risk factors . This real-time financial data analysis helps with immediate fraud detection, instantaneous credit decisions, and real-time risk management.

• Financial Auditing

RTDWs enable continuous auditing and monitoring to give auditors  real-time visibility into financial transactions . It helps identify discrepancies and anomalies immediately to enhance the accuracy of audits and financial reports.

• Emergency Services

RTDWs can keep track of critical data like the  location of incidents, available resources, and emergency personnel status . This ensures an efficient deployment of resources and faster response times, potentially saving lives in critical situations.

• Telecommunications

RTDWs play a vital role in enabling efficient network management and enhancing overall customer satisfaction. They provide  immediate analysis of network performance, customer usage patterns, and potential system issues . This improves service quality, optimizes resource utilization, and proactive problem resolution.

• Online Gaming

RTDWs provide  analytics on player behaviors, game performance, and in-game purchases  to support online gaming platforms. This enables game developers to promptly adjust game dynamics, improve player engagement, and optimize revenue generation.

• Energy Management

In the energy sector, RTDWs provide  instantaneous data on energy consumption, grid performance, and outage situations. This enables efficient energy distribution, quick response to power outages, and optimized load balancing.

• Cybersecurity

RTDWs are crucial for cybersecurity as they provide  real-time monitoring of network activities and immediate detection of security threats. This supports swift countermeasures, minimizes damage, and enhances the overall security posture.

Real-Time Data Warehouse: 3 Real-Life Examples For Enhanced Business Analytics

To truly highlight the importance of real-time data warehouses, let’s discuss some real-life case studies.

• Case Study 1: Beyerdynamic 

Beyerdynamic , an audio product manufacturer from Germany, was facing difficulties with its previous method of analyzing sales data . In this process, they extracted data from their legacy systems into a spreadsheet and then compiled reports, all manually. It was time-consuming and often caused inaccurate reports.  

To overcome these challenges, Beyerdynamic developed a  data warehouse that automatically extracted transactions from its existing ERP and financial accounting systems. This data warehouse was carefully designed to store standard information for each transaction, like product codes, country codes, customers, and regions. 

They also implemented a web-based reporting solution that helped managers create their standard and ad-hoc reports based on the data held in the warehouse.

Supported by an optimized data model, the new system allowed the company to perform detailed sales data analyses and identify trends in different products or markets.

• Production plans could be adjusted quickly based on changing demand , ensuring the company neither produced excessive inventory nor missed out on opportunities to capitalize on increased demand.
• With the new system, the company could use  real-time data for performance measurement and appraisal . Managers compared actual sales with targets by region, assessed the success of promotions, and quickly responded to any adverse variances.
• Sales and distribution strategies could be quickly adapted according to changing demands in the market. For instance, when gaming headphone sales started increasing in Japan, the company promptly responded with tailored promotions and advertising campaigns.
• Case Study 2: Continental Airlines 

Continental Airlines is a major player in the aviation world. It  faced significant issues because of old, manual systems. Their outdated approach slowed down decision-making and blocked easy access to useful data from departments like customer service, flight operations, and financials. Also, the lack of real-time data meant that decisions were often based on outdated information.

They devised a robust plan that hinged on 2 key changes: the  ‘Go Forward’ strategy and a  ‘real-time data warehouse’

• Go Forward Strategy:  This initiative focused on tailoring the airline’s services according to the customer’s preferences. The concept was simple but powerful –  understand what the customer wants and adapt services to fit that mold . In an industry where customer loyalty can swing on a single flight experience, this strategy aims to ensure satisfaction and foster brand loyalty.
• Real-Time Data Warehouse:  In tandem with the new strategy, Continental also implemented an RTDW. This technological upgrade gave the airline quick access to current and historical data. The ability to extract insights from this data served as a vital reference point for strategic decision-making, optimizing operations, and enhancing customer experiences.

The new strategy and technology led to critical improvements:

• The airline could offer a personalized touch by understanding and acting on customer preferences. This  raised customer satisfaction and made the airline a preferred choice for many.
• The introduction of the RTDW brought simplicity and efficiency to the company’s operations. It facilitated quicker access to valuable data which was instrumental in  reducing the time spent on managing various systems. This, in turn, resulted in significant cost savings and increased profitability.
• Case Study 3: D Steel 

D Steel, a prominent steel production company, was facing a unique set of challenges when they aimed to  set up a real-time data warehouse to analyze their operations. While they tried to use their existing streams package for synchronization operations, several obstacles emerged.

The system was near real-time but it  couldn't achieve complete real-time functionality.  The load on the source server was significantly high and synchronization tasks required manual intervention.

More so, it lacked automation for  Data Definition Language (DDL) , compatibility with newer technologies, and had  difficulties with data consistency verification, recovery, and maintenance . These challenges pushed the steel company to seek a new solution.

The Solution

D Steel decided to implement real-time data warehouse solutions that enabled instant data access and analysis. 

The new RTDWs system proved to be extremely successful as it resolved all previous problems. It provided:

• Real-time synchronization
• Implementing DDL automation
• Automated synchronization tasks
• Reduced the load on the source server

The system also introduced a unique function that  compared current year data with that of the previous year  and helped the company in annual comparison analysis.

• Enhancing Real-Time Data Warehousing: The Role of Estuary Flow

Estuary’s Flow is our  data operations platform   that binds various systems by a central  data pipeline . With Flow, you get diverse systems for storage and analysis, like databases and data warehouses. Flow is pivotal in  maintaining synchronization amongst these systems, ensuring that new data feeds into them continuously.

Flow utilizes  real-time data lakes as an integral part of its data pipeline. This serves dual roles. 

First, it works as a transit route for data and facilitates an easy flow and swift redirection to distinct storage endpoints. This feature also helps in backfilling data from these storage points.

The  secondary role of the data lake in Flow is to serve as a reliable storage backbone . You can lean on this backbone without the fear of turning into a chaotic ‘data swamp.’ 

Flow assures automatic organization and management of the  data lake . As data collections move through the pipeline, Flow applies different schemas to them as per the need.

Remember that the data lake in Flow doesn’t replace your ultimate storage solution. Instead, it aims to  synchronize and enhance other storage systems crucial for powering key workflows , whether they're analytical or transactional.

As we have seen with real-time data warehouse examples, this solution transcends industry boundaries. Only those organizations that embrace real-time data warehousing to its fullest can unlock the true potential of their data assets. 

While it can be a little tough to implement, the benefits of real-time data warehousing far outweigh the initial complexities, and the long-term advantages it offers are indispensable in today's data-driven world.

If you’re considering setting up a real-time data warehouse, investing in a top-notch real-time data ingestion pipeline like  Estuary Flow should be your first step. Designed specifically for building real-time data management, Flow provides a no-code solution to synchronize your many data sources and integrate fresh data seamlessly.  Signup for Estuary Flow for free and seize the opportunity today.

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Table of Contents

• Manufacturing & Supply Chain
• Banking & Finance 

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Practical Data Warehousing: Successful Cases

Table of contents:.

No matter how smooth the plan may be in theory, practice will certainly make adjustments. Because each real case has its own characteristics, which in the general case cannot be taken into account. Let's see how the world's leading brands have adapted to their needs a well-known way of storing information — data warehousing. If you think this is your case, then arrange a call .

The Reason for Making Decisions

The need to make business decisions based on data analysis has long been beyond doubt. But to get this data, it needs to be collected, sorted and prepared for analytics .

Operating Supplement

supplier integrations

cost reduction

David Schwarz

DATAFOREST has the best data engineering expertise we have seen on the market in recent years.

This is what data warehousing specialists do. To focus on the best performance, it makes sense to consider how high-quality custom assemblies came out of this constructor.

Data warehousing interacts with a huge amount of data

A data warehousing is a digital storage system that integrates and reconciles large amounts of data from different sources. It helps companies turn data into valuable information and make informed decisions based on it. Data warehousing combines current and historical data and acts as a single source of reliable information for business.

After raw data mining (extract, transform, load) info enters the warehouse from operating systems, such as an enterprise data resource planning system or a customer relationship management system. Sources also include databases, partner operational systems, IoT devices, weather apps, and social media. Infrastructure can be on-premises or cloud-based, with the latter option predominating in recent times.

Data warehousing is necessary not only for storing information, but also for processing structured and unstructured data: video, photos, sensor indicators. Some data warehousing options use built-in analytics and in-memory database data technology (info is stored in RAM rather than on a hard drive). This is necessary to access reliable data in real time.

After data is sorted, it is sent to data marts for further analysis by BI or data science .

Why consider data warehousing cases

Consideration of known options for data warehousing is necessary, first of all, in order not to keep making the same mistakes. Based on a working solution, you can improve your own performance. If you want to always be on the cutting edge of technology, book a call .

• When using data warehouses, executives access data from different sources, they do not have to decide blindly.
• Data warehousing is needed for quick retrieval and analysis. When using warehouses, you can quickly request large amounts of data without involving personnel for this.
• Before uploading to the warehouse, the system creates data cleansing tasks and puts them for further processing, ensuring converting the data into a consistent format for subsequent analyst reports.
• The warehouse contains large amounts of historical data and allows you to study past trends and issues to predict events and improve the business structure.

Blindly repeating other people's decisions is also impossible. Your case is unique and probably requires a custom approach. At best, well-known storage solutions can be taken as a basis. You can do it yourself, or you can contact DATAFOREST specialists for professional services. We have a positive experience and positive customer stories of data warehousing creating and operating.

Case 1: How the Amazon Service Does Data Warehousing

Amazon is one of the world's largest and most successful companies with a diversified business: cloud computing, digital content, and more. As a company that generates vast amounts of data (including data warehousing services), Amazon needs to manage and analyze its data effectively.

Two main businesses

Amazon's data warehousing needs are driven by the company's vast and diverse data sources, which require sophisticated tools and technologies to manage and analyze effectively.

1. One of the main drivers of Amazon's business is its e-commerce platform , which allows customers to purchase a wide range of products through its website and mobile apps. Amazon's data warehousing needs in this area are focused on collecting, storing, and analyzing data related to customer behavior, purchase history, and other metrics. This data is used to optimize Amazon's product recommendations engine, personalize the shopping experience for individual customers, and identify growth strategies.

2. Amazon's other primary business unit is Amazon Web Services (AWS), which offers cloud computing managed services to businesses and individuals. AWS generates significant amounts of data from its cloud data infrastructure, including customer usage and performance data. To manage and analyze this modern data effectively, Amazon relies on data warehousing technologies like Amazon Redshift, which enables AWS to provide real-time analytics and insights to its customers.

3. Beyond these core businesses, Amazon also has significant data warehousing needs in digital content (e.g., video, music, and books). Amazon's advertising business relies on data analysis to identify key demographics and target ads more effectively to specific audiences.

By investing in data warehousing and analytics capabilities, Amazon through digital transformation can maintain its competitive edge and continue to grow and innovate in the years to come.

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Obstacles on the way to the goal.

Amazon faced several specific implementation details and challenges in its data warehousing efforts.

• The brand needed to integrate data from various sources into a centralized data warehouse. It required the development of custom data pipelines to collect and transform data into a standard format.

• Amazon's data warehousing needs are vast and constantly growing, requiring a scalable solution. The company distributed data warehouse architecture center using technologies like Amazon Redshift, allowing petabyte-scale data storage and analysis.

• As a company that generates big data, Amazon would like to ensure that its data warehousing solution could provide real-time data analytics and insights. Achieving high performance requires optimizing data storage, indexing, and querying processes.

• Amazon stores sensitive customer data in its warehouse, prioritizing data security. To protect against security threats, the brand implements various security measures, including encryption, access controls, and threat detection.

• Building and maintaining a data warehousing solution can be expensive. Amazon leverages cloud-based data warehousing solutions (Redshift) to minimize costs, which provide a cost-effective, pay-as-you-go pricing model.

Amazon's data warehousing implementation required careful planning, significant investment in technology and infrastructure, and ongoing optimization and maintenance to ensure high performance and reliability.

Change for the better

When Amazon considered all the needs, found the right tools, and implemented a successful data warehouse, the company got the following main business outcomes:

• Improved data driven decision

• Better customer enablement

• Cost effective decision

• Improved performance

• Competitive advantage

• Scalability

Amazon's data warehousing implementation has driven the company's growth and success. Not surprisingly, a data storage service provider must understand data storage. The cobbler's children don't need to have no shoes.

Case 2: Data Warehousing Adventure with UPS

United Parcel Services (UPS) is an American parcel delivery and supply chain management company founded in 1907 with an annual revenue of 71 billion dollars and logistics services in more than 175 countries. In addition, the brand distributes goods, customs brokerage, postal and consulting services. UPS processes approximately 300 million tracking requests daily. This effect was achieved, among others, thanks to intelligent data warehousing.

One mile for $50 million

In 2013, UPS stated that it hosted the world's largest DB2 relational database in two United States data centers for global operations. Over time, global operations began to increase, as did the amount of semi structured data. The goal was to use different forms of storage data to make better users business decisions.

One of the fundamental problems was route optimization. According to an interview with the UPS CTO, saving 1 mile a day per driver could save 1.5 million gallons of fuel per year or $50 million in total savings.

However, the data was distributed in DB2; some included repositories, some local, and some spreadsheets. UPS needed to solve the data infrastructure problem first and then optimize the route.

Four letters "V."

The big data ecosystem efficiently handles the four "Vs": volume, validity, velocity, and variety. UPS has experimented with Hadoop clusters and integrated its storage details and computing system into this ecosystem. They upgraded data warehousing and computing power to handle petabytes of data, one of UPS's most significant technological achievements.

The following Hadoop components were used:

• HDFS for storage

• Map Reduce for fast processing

• Kafka streaming

• Sqoop (SQL-to-Hadoop) for ingestion

• Hive & Pig for structured queries on unstructured data

• monitoring system for data nodes and names

But that's just speculation because, due to confidentiality, UPS didn't declassify the tools and technologies they used in their big data ecosystem.

Constellation of Orion

The result was a four-year ORION (On-Road Integrated Optimization and Navigation) route optimization project. Costs — about one billion dollars a year. ORION used the results to data stores and calculate big data and got analytics from more than 300 million data points to optimize thousands of routes per minute based on real-time information. In addition to the economic benefits, the Orion project shortened approximately 100 million shipping miles and a 100,000-ton reduction in carbon emissions.

Case 3: 42 ERP Into One Data Warehouse

In general, the topic of specific cases of data warehousing implementation is sufficiently secret. There may be cases of consent and legitimate interests in the contracts. There are open-source examples of work, but the vast majority are on paid libraries. The subject is so relevant that you can earn money from it. Therefore, sometimes there are "open" cases, but the brand name is not disclosed.

Brand X needs help

World leader in industrial pumps, valves, actuators, controls, etc., needed help extracting data from disparate ERP systems. They wanted it from 42 ERP instances, standardized flat files, and collected all the information in one data warehouse. The ERP systems were from different vendors (Oracle, SAP, BAAN, Microsoft, PRMS) to complicate future matters.

The client also wanted a core set of metrics and a central dashboard to combine all the information from different locations worldwide. The project resulted from a surge in demand for corporate data from database management. The company knew its data warehousing needed a central repository for all data from its locations worldwide. Requests often came from top to bottom, and when an administrator required access to the correct data, there were logistical extracting problems. And the project gets started.

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The foundation stone.

The hired third-party developer center has made a roadmap, according to which ERP data was taken from 8 major databases and placed in a corporate data warehouse. It entailed integrating 5 Oracle ERP instances with 3 SAP ERP. Rapid Marts have also been integrated into Oracle ERP systems to improve the project's progress.

One of the main challenges was the need for more standardization of fields or operational data definitions in ERP systems. To solve this problem, the contractor has developed a data service tool that allows access to the back end of the database and displays info suitably. Since then, the customer has known which fields to use and how to set them each time a new ERP instance is encountered. These data definition patterns were the project's foundation stone and completely changed how customer data is handled. It was a point to launch consent.

All roads lead to data warehousing

The company has one common and consistent way to obtain critical indicators. The long-term effect of the project is the ease of obtaining information. What was once a long and inconsistent process of getting relevant information at an aggregate level is now streamlined to store data in one central repository with one team controlling it.

Data Warehousing: Different Cases — General Conclusions

Each data warehouse organization has unique methods and tools because business needs differ. In this case, data warehousing can be compared with a mosaic and a children's constructor. You can make different figures from the same parts, arranging the elements especially. And if one part is lost or broken, you need to make a new one or find another one and "process it with a rasp."

Generalities between different cases of data warehousing

There are several common themes and practices among successful data warehousing implementations, including:

• Successful data warehousing implementations start with clearly understanding the business objectives and how the warehouse (or data lake) can support those objectives.

• The data modeling process is critical to the success of data warehousing.

• The data warehouse is only as good as the data it contains.

• Successful data warehousing requires efficient data integration processes that can operate large volumes of data and ensure consistency and accuracy.

• Data warehousing needs ongoing performance tuning to optimize query performance.

• A critical factor in data warehousing is a user-friendly interface that makes it easy for end users to access the data and perform complex queries and analyses.

• Continuous improvement is essential to ensure the data warehouse remains relevant and valuable to the business.

Competent data warehousing implementations combine technical expertise and a deep understanding of business details and user needs.

Your case is not mentioned anywhere

When solving the problem of organizing data warehousing , one would like to find a description of the same case and do everything according to plan. But the probability of this event is negligible — you will have to adapt to the specifics of the customer's business and consider your knowledge and capabilities, as well as the technical and financial conditions of the project. Then it would help if you took a piece of the puzzle or parts of the constructor and built your data warehouse. Minus — you have to work. Plus — it will be your decision on data storage and only your implementation.

Data Warehousing Is Like a Trampoline

Changes in data warehousing , like any technological and methodological changes, are carried out to improve the data collection, storage, and analysis level. It takes the customer to a new level in his activity and the contractor — to his own. Like a jumper and a trampoline: separately, it is just a gymnast and just equipment, and in combination, they give a certain third quality — the possibility of a sharp rise.

If you are faced with the problem of organizing a new data warehousing system, or you are simply interested in what you read, let's exchange views with DATAFOREST.

What is the benefit of data warehousing for business?

A data warehouse is a centralized repository that contains integrated data from various sources and systems. Data warehousing provides several benefits for businesses: improved decision-making, increased efficiency, better customer insights, operational efficiency, and competitive advantage.

What is the definition of a successful data warehousing implementation?

The specific definition of a successful data warehouse implementation will vary depending on the goals of the organization and the particular use case for data warehousing. Some common characteristics are: meeting business requirements, high data quality, scalability, user adoption, and positive ROI.

What are the general considerations for implementing data warehousing?

Implementing data warehousing involves some general considerations: business objectives, data sources, quality and modeling, technology selection, performance tuning, user adoption, ongoing maintenance, and support.

What are the most famous examples of the implementation of data warehousing?

There are many famous examples of the implementation of data warehousing across industries:

• Walmart has one of the largest data warehousing implementations in the world

• Amazon's data warehousing solution is known as Amazon Redshift

• Netflix uses a data warehouse to store and analyze data from its streaming platform

• Coca-Cola has a warehouse to consolidate data from business units and analyze it

• Bank of America analyzes customer data by data warehousing to improve customer experience

What are the challenges while implementing data warehousing, and how to overcome them?

Based on the experiences of organizations that have implemented data warehousing, some common challenges and solutions are:

• Ensuring the quality of the data that is being stored and analyzed. You must establish data quality standards and implement data validation and cleansing by data types.

• Integrating from disparate data sources. Establishing a clear data integration strategy that considers the different data sources, formats, and protocols involved is vital.

• As the amount of data stored in a data warehouse grows, performance issues may arise. A brand should regularly monitor query performance and optimize the data warehouse to ensure that it remains efficient and effective.

• To ensure that sensitive data stored in the data warehouse is secure. It involves implementing appropriate measures such as access controls, encryption, and regular security audits. They are details of privacy security.

• Significant changes to existing processes and workflows. Solved by establishing a transparent change management process that involves decision-makers and users at all levels.

What is an example of how successful data warehousing has affected a business?

An example of how successful data warehousing has affected Amazon is its recommendation engine. It suggests products to customers based on their browsing and purchasing history. By using artificial intelligence and machine learning algorithms to analyze customer data, Amazon has improved the fully managed accuracy of its recommendations, resulting in increased sales and customer satisfaction.

What role does data integration play in data warehousing?

Data integration is critical to data warehousing, enabling businesses to consolidate and standardize data from multiple sources, ensure data quality, and establish effective data governance practices.

How are data quality and governance tracked in data warehousing?

Data quality and governance are tracked in data warehousing through a combination of data profiling, monitoring, and management processes and establishing data governance frameworks that define policies and procedures for managing data quality and governance. So, businesses can ensure that their data is accurate, consistent, and compliant with regulations, enabling effective decision-making and driving business applications' success.

Are there any measures to the benefits of data warehousing?

The benefits of business data warehousing can be measured through improvements in data quality, efficiency, decision-making, revenue and profitability, and customer satisfaction. By tracking these metrics, businesses can assess the effectiveness of their data warehousing initiatives and make informed decisions about future investments in data management and analytics with cloud services.

How to avoid blunders when warehousing data?

By following the best practices, businesses can avoid common mistakes, minimize the risk of blunders when warehousing data, and ensure their data warehousing initiatives are successful and practical to be analyzed with business intelligence.

Aleksandr Sheremeta

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15 Data Warehouse Project Ideas for Practice with Source Code

Learn how data is processed into data warehouses by gaining hands-on experience on these fantastic solved end-to-end real-time data warehouse projects.

The worldwide data warehousing market is expected to be worth more than $30 billion by 2025. Data warehousing and analytics will play a significant role in a company’s future growth and profitability. Data warehouse solutions will provide every business a considerable advantage by evaluating all of the data they collect and making better decisions. Understanding business data will help make intelligent business decisions that determine whether an organization succeeds or fails. The demand for Big Data and Data Analytics will continue to grow in the coming days, leading to a greater need for Data Warehouse solutions. 

It’s essential to understand why data warehousing projects fail before getting an idea of the different data warehousing projects to explore from beginner to advanced level in your learning path. So let's get started!

Table of Contents

What is data warehousing, why data warehouse projects fail, data warehouse projects for beginners, data warehouse projects for intermediate, data warehouse projects for advanced, data warehouse project tools.

Data warehousing (DW) is a technique of gathering and analyzing data from many sources to get valuable business insights. Typically, a data warehouse integrates and analyzes business data from many sources. The data warehouse is the basis of the business intelligence (BI) system, which can analyze and report on data.

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To put it in other words, Data Warehousing supports a set of frameworks and tools that help businesses organize, understand, and use their data to make strategic decisions.

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The significant roadblocks leading to data warehousing project failures include disconnected data silos, delayed data warehouse loading, time-consuming data preparation processes, a need for additional automation of core data management tasks, inadequate communication between Business Units and Tech Team, etc.

• Delayed Data Warehouse Loading

 Data must first be prepared and cleaned before being placed into the warehouse. Cleaning data is typically time-consuming, so this creates an immediate crisis. IT professionals are often disappointed by the time spent preparing data for loading. The ability of enterprises to quickly move and combine their data is the primary concern. Movement and ease of access to data are essential to generating any form of insight or business value. This often exhausts an organization's time and resources, resulting in a more protracted and expensive project in the end. Furthermore, poor data loading might result in various issues, including inaccuracies and data duplication.

Lower End-User Acceptance Rate

End-user acceptability is another factor that frequently leads to the failure of data warehouse projects. New technologies can be fascinating, but humans are afraid of change, and acceptance may not always be the case. Any project's success depends on how well people are mutually supportive. The first step in encouraging user acceptance and engagement is to create a data-driven mindset. End users should be encouraged to pursue their data-related interests. Non-technical users will benefit from self-service analytics because it will make it easier to access information fast. These transitional efforts will aid the success and utilization of your data warehouse in the long run and lead to better decision-making throughout the organization.

Automation of core management activities

If you carry out a process manually, valuable time, resources, and money are invested instead of automating it, thereby wasting business opportunities. You can automate manual, time-consuming operations, which helps you save money while shortening the time to see results. Automation can accelerate all data management and data warehousing steps, including data collection, preparation, analysis, etc.

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15 Data Warehouse Project Ideas for Practice

This section will cover 15 unique and interesting data warehouse project ideas ranging from beginner to advanced levels.

From Beginner to Advanced level, you will find some data warehouse projects with source code, some Snowflake data warehouse projects, some others based on Google Cloud Platform (GCP), etc.

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Snowflake Real-time Data Warehouse Project

In this Snowflake Data Warehousing Project, you'll learn how to deploy the Snowflake architecture to build a data warehouse in the cloud. This project will guide you on loading data via the web interface, SnowSQL, or Cloud Provider. You will use Snowpipe to stream data and QuickSight for data visualization .

Source code- Snowflake Real-time Data Warehouse Project  

Slowly Changing Dimensions Implementation using Snowflake

 This project depicts the usage of Snowflake Data Warehouse to implement several SCDs. Snowflake offers various services that help create an effective data warehouse with ETL capabilities and support for various external data sources. Use Python's faker library to generate user records and save them in CSV format with the user's name and the current system time for this project. Fake data is made with the faker library and saved as CSV files. NiFi is used to collect data, and Amazon S3 sends the data. New data from S3 is loaded into the staging table using a Snowpipe automation tool. Data manipulation language changes are stored in the staging table using Snowflake streams to determine the operation to be done.  Initiate tasks and stored procedures depending on the changes to implement SCD Type-1 and Type-2.

Source Code- Slowly Changing Dimensions Implementation using Snowflake

New Projects

Fraud Detection using PaySim Financial Dataset

In today's world of electronic monetary transactions, detecting fraudulent transactions is a significant business use case. To overcome this issue, PaySim Simulator is used to create Synthetic Data available on Kaggle. The data contains transaction specifics such as transaction type, transaction amount, client initiating the transaction, old and new balance, i.e., before and after the transaction, and the same in Destination Account along with the target label, and is fraudulent. This data warehouse project uses the PaySim dataset to create a data warehouse and a classification model based on transaction data for detecting fraudulent transactions.

Source Code- Fraud Detection using PaySim Financial Dataset

Anime Recommendation System Data Warehouse Project

The anime recommendation system is one of the most popular data warehousing project ideas. Use the Anime dataset on Kaggle, which contains data on user preferences for 12,294 anime from 73,516 people. Each user can add anime to their completed list and give it a rating. The project aims to develop an effective anime recommendation system based on users' viewing history. Use the Anime dataset to build a data warehouse for data analysis. Once the data has been collected and analyzed, it becomes ready for building the recommendation system.

Source code- Anime Recommendation System Data Warehouse Project

Marketing Data Warehouse for Media Research Company

Customer relationship management and sales systems, for example, might cause marketing data to get diffused across various systems within an organization.

Create a marketing data warehouse for this project, which will serve as a single source of data for the marketing team to work with. You can also combine internal and external data like web analytics tools, advertising channels, and CRM platforms. Use the Nielsen Media Research company dataset for building this data warehouse. All marketers will access the same standardized data due to the data warehouse, allowing them to execute faster and more efficient projects. Such data warehouses enable organizations to understand performance measures, including ROI, lead attribution, and client acquisition costs.

Source Code- Marketing Data Warehouse for Banking Dataset

Data Warehouse Design for E-commerce Environments

You will be constructing a data warehouse for a retail store in this big data project. However, it concentrates on answering a few particular issues about pricing optimization and inventory allocation in terms of design and implementation. In this hive project, you'll be attempting to answer the following two questions:

Were the higher-priced items more prevalent in some markets?

Should inventory be reallocated or prices adjusted based on location?

Source Code- Data Warehouse Design for E-commerce Environments

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Data Warehouse Project for Music Data Analysis

This project involves creating an ETL pipeline that can collect song data from an S3 bucket and modify it for analysis. It makes use of JSON-formatted datasets acquired from the s3 bucket. The project builds a redshift database in the cluster with staging tables that include all the data imported from the s3 bucket. Log data and song data are the two datasets used in the project. The song_data dataset is a part of the Million Song Dataset , and the log_data dataset contains log files generated based on the songs in song_data. Data analysts can use business analytics and visualization software to understand better which songs are most popular on the app.

Source Code- Data Warehouse Project for Music Data Analysis

Global Sales Data Warehouse Project

The primary goal of this Global Sales Data Warehouse project is to minimize raw material manufacturing costs and enhance sales forecasting by identifying critical criteria such as total sales revenue on a monthly and quarterly basis by region and sale amount. The Data Warehousing Project focuses on assessing the entire business process. The data warehouse provides essential information such as daily income, weekly revenue, monthly revenue, total sales, goals, staff information, and vision.

Source Code- Sales Data Warehouse Project  

Data Warehouse Project for B2B Trading Company

This project aims to employ dimensional modeling techniques to build a data warehouse. Determine the business requirements and create a data warehouse design schema to meet those objectives. Using SSRS and R, create reports using data from sources. Based on the data warehouse, create an XML schema. Use Neo4j technologies to design a data warehouse section as a graph database.

Source Code- Data Warehouse Project for B2B Trading Company

Heart Disease Prediction using Data Warehousing

One of the most commonly seen diseases today is heart disease. In this data warehousing project, you'll learn how to create a system that can determine whether or not a patient has heart disease. The data warehouse assists in correlating clinical and financial records to estimate the cost-effectiveness of care. Data mining techniques aid in identifying data trends that may anticipate future individual heart-related issues. Furthermore, the data warehouse aids in the identification of individuals who are unlikely to respond well to various procedures and surgeries.

Source Code- Heart Disease Prediction using Data Warehousing

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GCP Data Ingestion using Google Cloud Dataflow

Data ingestion and processing pipeline on Google cloud platform with real-time streaming and batch loading are part of the project. This project uses the Yelp dataset, primarily used for academic and research reasons. We first create a GCP service account, then download the Google Cloud SDK. In subsequent operations, the Python program and all other dependencies are then downloaded and connected to the GCP account. It downloads the Yelp dataset in JSON format, connects to Cloud SDK through Cloud storage, and connects to Cloud Composer. It publishes the Yelp dataset JSON stream to a PubSub topic. Cloud composer and PubSub outputs connect to Google Dataflow using Apache Beam . Lastly, Google Data Studio is used to visualize the data.

Source Code-   GCP Data Ingestion using Google Cloud Dataflow

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Build Data Pipeline using Dataflow, Apache Beam, Python

This is yet another intriguing GCP project that uses PubSub, Compute Engine, Cloud Storage, and BigQuery. We will primarily explore GCP Dataflow with Apache Beam in this project. The two critical phases of the project are-

Reading JSON encoded messages from the GCS file, altering the message data, and storing the results to BigQuery.

Reading JSON-encoded Pub/Sub messages, processing the data, and uploading the results to BigQuery.

Source Code- Build Data Pipeline using Dataflow, Apache Beam, Python

In this next advanced-level project, we will mainly focus on GCP BigQuery. This project will teach you about Google Cloud BigQuery and how to use Managed Tables and ExternalTables. You'll learn how to leverage Google Cloud BigQuery to explore and prepare data for analysis and transformation. It will also cover the concepts of Partitioning and Clustering in BigQuery. The project necessitates using BQ CLI commands and creating an External BigQuery Table using a GCS Bucket, and it uses Client API to load BigQuery tables.

Source Code- GCP Project to Learn using BigQuery for Exploring Data

Anomaly Detection in IoT-based Security System

IoT devices, or network-connected devices like security cameras, produce vast amounts of data that you may analyze to improve workflow. Data is collected and stored in relational formats to facilitate historical and real-time analysis. Then, using existing data, instant queries are run against millions of events or devices to find real-time abnormalities or predict occurrences and patterns. For this project idea, create a data warehouse that will help this data be consolidated and filtered into fact tables to provide time-trended reports and other metrics.

Source Code- Anomaly Detection in IoT-based Security System

AWS Snowflake Data Pipeline using Kinesis and Airflow

This project will show you how to create a Snowflake Data Pipeline that connects EC2 logs to Snowflake storage and S3 post-transformation and processing using Airflow DAGs . Send customers' data and orders data to Snowflake via Airflow DAG processing and transformation and S3 processed stages in this project. You'll learn how to set up Snowflake stages and create a database in Snowflake.

Source Code- AWS Snowflake Data Pipeline using Kinesis and Airflow

Data warehousing optimizes ease of access, reduces query response times, and enables businesses to gain deeper insights from large volumes of data. Previously, building a data warehouse required a significant investment in infrastructure. The introduction of cloud technology has drastically cut the cost of data warehousing for enterprises.

There are various cloud-based data warehousing tools now available in the market. These tools provide high speed, high scalability, pay-per-use, etc. Since choosing the best Data Warehouse tool for your project can often seem challenging, we have curated a list of the most popular Data Warehouse project tools with their essential features-

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Microsoft Azure

Microsoft's Azure SQL data warehouse is a cloud-based relational database. Microsoft Azure allows developers to create, test, deploy, and manage applications and services using Microsoft-managed data centers. The platform is based on nodes and uses massively parallel computing (MPP). The design is well suited for query optimization for concurrent processing. As a result, you can extract and visualize business information considerably more quickly. Azure is a public cloud computing platform that provides IaaS, PaaS, SaaS, among other services.

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Snowflake is a cloud-based data warehousing platform that runs on Amazon Web Services ( AWS ) or Microsoft Azure cloud architecture. You can use Snowflake to create an enterprise-grade cloud data warehouse. You can use the tool to gather and analyze data from both structured and unstructured sources. It uses SQL to perform data blending, analysis, and transformations on various data structures. Snowflake provides scalable, dynamic computing power at per-usage cost, and it enables you to scale CPU resources following user activity.

Google BigQuery

BigQuery is a cost-efficient serverless data warehouse with built-in machine learning features. It's a platform for ANSI SQL querying. Google BigQuery is a data analysis tool that allows you to process read-only data sets in the cloud and works with SQL-lite syntax to analyze data with billions of rows. You can use it in conjunction with Cloud ML and TensorFlow to build robust AI models . It can also run real-time analytics queries on vast amounts of data in seconds. This cloud-native data warehouse supports geospatial analytics.

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Amazon Redshift

The Amazon Redshift is a cloud-based, fully managed data warehouse. In seconds, the fully managed system can process vast amounts of data. As a result, it's well-suited to high-speed data analytics. Because it is a relational database management system (RDBMS), you can use it with other RDBMS applications. Using SQL-based clients and business intelligence (BI) tools with typical ODBC and JDBC connections, Amazon Redshift facilitates quick querying abilities over structured information. Also, Redshift supports automatic concurrent scaling, and the automation scales up or down query processing resources to match workload demand. You may also scale your cluster or switch between node kinds with Redshift. As a result, you can improve data warehouse performance while lowering operational costs.

Start Building Data Warehousing Projects to Get You a Real-World Data Job

As organizations explore new opportunities and products, data warehouses play a vital role in the process. They're rapidly evolving; especially cloud data warehouses are becoming popular among businesses. They assist companies in streamlining operations and gaining visibility across all areas. Furthermore, cloud data warehouses assist businesses in better serving their clients and expanding their market potential. This makes it even more crucial for data engineers to enhance their data warehousing skills and knowledge to stay ahead of the competition. If we’ve whetted your appetite for more hands-on real-time data warehouse project ideas, we recommend checking out  ProjectPro for Solved End-To-End Big Data and Data Warehousing Projects.

FAQs on Data Warehousing Projects

What is etl in data warehouse.

ETL (extract, transform, and load) is a data integration process that integrates data from several sources into a single, reliable data store that is then loaded into a data warehouse or other destination system. 

How to define business objectives for data warehousing projects?

For any data warehousing project, here are a few things you must keep in mind-

• the scope of the project,
• a data recovery plan,
• compliance needs and regulatory risks,
• the data warehouse's availability in production,
• plan for future and current needs, etc.

About the Author

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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Case Study: Building and maintaining a Data Pipeline and Data Warehouse for the Enterprise

• Data Pipeline

The amount of load that Reflective Data was able to lift from the shoulders of my team was unbeliavable. Without their help, we’d still be building out the pipelines and never gotten to the level of advanced analysis and ML that we’re able to do now. They let us focus on what brings the most value to our business. Melanie, Director of Data Analytics, Frankfurt

At Reflective Data, we’ve worked with companies big and small. This means we have seen all levels of maturity when it comes to the infrastructure and knowledge around data pipelines and data warehouses.

Some of the most challenging projects have been enterprises with quite some infrastructure, legacy pipelines, and of course, opinions. Smaller businesses are just starting to adopt the concept of having all of their data stored in a data warehouse but many enterprises have been doing this for a decade!

The challenge

When many of the enterprises that we’ve worked with started building their data pipelines, they didn’t have tools like Airflow, BigQuery etc. that we use and love today. This means the bulk of it was built in-house. Even the concept of cloud computing was in its early days and most operations were kept on-premise.

The challenge with this kind of setup starts by understanding the existing setup. In some cases, the documentation is close to none and the people that built it are no longer with the company. This alone can take a month or so – mapping everything out, understanding the structure, creating the plan for moving forward.

Another challenge is getting everyone on the team on board. More often than not there are people who value the work that has been put into the old system over the years so much that it blinds them from seeing the obvious benefits of moving to a much more modern infrastructure.

The solution

When working with the enterprise and legacy infrastructure, nothing happens overnight. Below are the phases of a typical project of getting an enterprise client onto a modern cloud-based data infrastructure.

Phase 1: understanding and mapping the existing situation

With most enterprises, it’s not just one team or system that depends on the data infrastructure. More often than not, this is the backbone of the entire business. This means we need to make sure we understand every aspect of the current system, where it gets the data, how it’s being processed and what processes depend on this data.

Phase 2: planning the infrastructure

We do our best to work closely with all teams involved to make sure their needs are taken into account. This means a series of on-hands meetings where we learn about their use cases and problems they’re having with the existing setup. The output of this phase is a clear plan for moving forward, including the tool stack, reporting mechanisms and several feedback rounds to make sure everyones’ needs are taken into account.

Phase 3: implementation

Depending on the in-house knowledge, resources and other aspects, a company can decide to implement the plan themselves and continue using Reflective Data as a consultant or hire us to handle the technical execution as well. By far, the most effective arrangement in our experience has been where we do the bulk of the work while including a few technical people from the client’s side in every step of the process. In some cases, those people is hired specifically for this purpose.

Phase 4: monitoring, reporting and integrations

The whole point of having high-quality data is to make it actionable. Of course, we handle core integrations within the implementation phase but in a sense, data infrastructure is a growing organism that needs constant attention. Reflective Data is here to build long-term relationships with its clients, ready to help whenever there’s a new data source to be added, a report to be built or if a new team member needs training.

Moving away from a legacy data infrastructure is one of the best actions an enterprise can take towards being more data-driven, more effective in managing the infrastructure and, in all reality, keeping up with the competition.

Ending with another quote from the customer here.

I guess you could say we were your average enterprise with LOTS of legacy data infrastructure that had been built over many years. This system was extremely complex and very expensive to maintain. When Reflective Data came in, they acted as true professionals, worked very closely with our IT and came up with the plan that pleased everyone. Today, being on the new cloud-based data infrastructure for almost a year now, I can say with all certainty that this project was a success. Not only are we saving tens of thousands of dollars every month on the infrastructure alone, the amount of hours it takes to maintain the system has gone from hundreds down to a ten or so. This has huge impact on our business. Implementing anything new would’ve taken at least 6 months with the old system, now it’s a matter of week or two to get everything up and running. Julien, VP of Marketing, Austin

It’s feedback like this that makes us love the work we do even more! Get in touch and learn how we can help you, too.

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• Building a data warehouse: a step-by-step guide

May 26, 2022

Table of contents

• What is a data warehouse?
• DWH architecture
• Approaches to building a DWH

Inmon’s approach

Kimball’s approach.

• 5 steps to build a DWH
• 1. Business requirements definition
• 2. DWH solution conceptualization
• 3. DWH design
• 4. DWH development and launch
• 5. After-launch support
• DWH implementation team
• DWH technologies to consider
• DWH success tips

Tatyana Korobeyko

Data Strategist

With the amount of data worldwide forecasted to grow up to 180 zettabytes by 2025, businesses have to deal with two major issues – where to store their data and how to make use of it. In place since the 1980s and constantly having their functionality extended, data warehouses can help deal with both these challenges. However, regardless of the technology’s maturity and the fact that data warehouses are usually developed by experts, the percentage of failed projects is disturbing, according to the research from the independent market research firm Vanson Bourne.

In this article, we will dive into the details of data warehouse implementation by outlining the two fundamental approaches to data warehouse design and data warehouse development steps. We also give advice on a suitable team composition for data warehousing consulting services and recommend technologies for creating a scalable solution.

What is a data warehouse and why build one?

A data warehouse is a system, which consolidates and stores enterprise information from diverse sources in a form suitable for analytical querying and reporting to support business intelligence and data analytics initiatives. The successful implementation of such a repository promises multiple benefits, including:

• Fact-based decisions taken at the speed of business as end-users can effortlessly access and work with a company’s historical information as well as current information collected from disparate heterogeneous systems.
• Decision-making based on high-quality information, because prior to entering a data warehouse, data undergoes comprehensive cleansing and transformation processes. In addition to this, many data management activities become automated, which helps eliminate error-prone manual data aggregation.   
• When a data warehouse is integrated with self-service BI solutions , such as Power BI or Tableau , data culture is adopted naturally across a company. 
• Due to the unified approach to data governance, which besides other things implies solid definition and management of data security policies, the risk of data breaches and leaks is minimized.

3 core components of a data warehouse architecture 

When you create the architecture of your future data warehouse, you have to take into account multiple factors, such as how many data sources will connect to the data warehouse, the amount of information in each of them together with its nature and complexity, your analytics objectives, existing technology environment, and so on. However, stating that each architecture is unique in its kind would be wrong, since practically each of them has the following three components:

• Source systems – operational databases capturing transactions, IoT devices streaming sensor data, SaaS applications, external data sources, etc.
• Data staging area – a zone that temporarily hosts copied data and a set of processes that help you clean and transform it according to the business-defined rules before loading into a data warehouse. With a staging area, you have a historical record of the original data to rely on in case an ETL job fails. Usually, as soon as the ETL job is completed successfully, the information from the staging area is erased. However, you may still save it for a certain period of time for legacy reasons, or archive. This area can be omitted if all data transformations occur in the data warehouse database itself.
• Data storage – a data warehouse database for company-wide information and data marts (DWH subsets), created for specific departments or lines of business. 

Besides these elements, an enterprise data warehousing solution also encompasses a data governance and metadata management component. The extended data warehouse environment may also include OLAP cubes (multidimensional data structures that store aggregated data to enable interactive queries) and a data access layer (tools and applications for end users to access and manipulate the stored information). However, these elements are a part of a bigger ecosystem – a BI architecture , so we won’t explore them here.

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Approaches to building a data warehouse.

The two fundamental design methods, which are used to build a data warehouse, are Inmon’s (Top-down) and Kimball’s (Bottom-up) approaches. 

Within Inmon’s approach, firstly, a centralized repository for enterprise information is designed according to a normalized data model, where atomic data is stored in tables that are grouped together by subject areas with the help of joins. After the enterprise data warehouse is built, the data stored there is used to structure data marts.

Inmon’s approach is more preferable in cases when you need to:

• Get a single source of truth while ensuring data consistency, accuracy and reliability
• Quickly develop data marts with no effort duplication for extracting data from original sources, cleansing, etc.

However, one of the major constraints of this method is that the setup and implementation is more time and resource-consuming compared to Kimball’s approach.

Kimball’s approach suggests that dimensional data marts should be created first, then if required, a company may proceed with creating a logical enterprise data warehouse.

The advocates of this approach point out that since dimensional data marts require minimal normalization, such data warehouse projects take less time and resources.  On the other hand, you may find duplicate data in tables and have to repeat ETL activities, as each data mart is created independently. 

Though the two approaches may seem rather different, they complement each other well, which is proven by the emergence of alternative approaches that combine the principles of both design methods.

A step-by-step guide for building a data warehouse

It is common practice to start a data warehouse initiative with a comprehensive readiness assessment. When evaluating the readiness for a data warehouse project, consider such factors as:

• Availability of strong business sponsors – influential managers who can envision the potential of the initiative and help promote it. 
• Business motivation – whether a data warehouse can help address some critical business problem. 
• Current data maturity across the company – in other words, whether end-users realize the importance of data-driven decision making , high data quality, etc.
• The ability of IT specialists and business users to collaborate.
• Feasibility of the existing technical and data environment.

After you’ve assessed the readiness for the project and are hopefully satisfied with it, you need to develop a framework for project planning and management, and then, eventually, move on to data warehouse development, which starts with the definition of your business requirements.

1. Busines s requirements definition

Business requirements affect almost every decision throughout the data warehouse development process – from what information should be available to how often it should be accessed. Therefore, it’s viable to start with interviewing your business users to define:

• Overall goals of the company as well as goals of particular business units, departments, etc.
• Methods and metrics that are used for measuring success
• Key issues the business faces 
• Types of routine data analysis the company currently performs, including what data is used for that, how often the analysis takes place, what potential improvements it has brought, etc.).

While interviewing business users, you should also set effective communication with your key IT specialists (database administrators, operational source system experts, etc.) to identify if the currently available information is sufficient in meeting such business requirements as:

• Key operational systems 
• Data update frequency
• Availability of historical data
• What processes are set to ensure the delivery of information to business users
• What tools are used to access and analyze information
• What types of insights are routinely generated
• If ad hoc requests for information are handled well, etc.

2. Data warehouse conceptualization and technology selection

The findings from the previous step are used as a foundation for defining the scope of the solution-to-be, so the needs and expectations of your business and IT users should be carefully analyzed and prioritized to draw up the optimal data warehouse feature set. 

After that, you have to identify the architectural approach to building a data warehousing solution, evaluate and select the optimal technology for each of the architectural components – staging area, storage area, etc. While drawing up the tech stack, consider such factors as:

• Your current technological environment
• Planned strategic technological directions
• Technical competencies of the in-house IT team members
• Specific data security requirements, etc.

By this time, you also should define the deployment option – on-premises, cloud or hybrid. The deployment option choice is dictated by numerous factors, such as data volume, data nature, costs, security requirements, number of users and their location as well as system availability among others.

3. Data w arehouse environment design

Before and during designing your data warehouse, you need to define your data sources and analyze information stored in there – what data types and structures are available, volume of information generated daily, monthly, etc., in addition to its quality, sensitivity, refresh frequency.

The next step would be logical data modeling, or arranging company’s data into a series of logical relationships called entities (real-world objects) and attributes (characteristics that define these objects). Entity-relationship modeling is used in various modeling techniques, including a normalized schema (a design approach for relational databases) and a star schema (used for dimensional modeling).

Next, these logical data models are converted into database structures, for example, entities are converted into tables, attributes are translated into columns, relationships are converted into foreign key constraints, and so on. 

After data modeling is finished, the first step is to design the data staging area to provide the data warehouse with high-quality aggregated data in the first place and also to define and control the source-to-target data flow during all subsequent data loads.

The design step also encompasses the creation of data access and usage policies, the establishment of the metadata catalog, business glossaries, etc.

4. D ata warehouse development and launch

The step starts with customizing and configuring the selected technologies (DWH platform, data transformation technologies, data security software, etc.). The company then develops ETL pipelines and introduces data security.

After all major components are introduced, they have to be integrated with the existing data infrastructure (data sources, BI and analytics software, a data lake, etc.) as well as each other so the data can be migrated afterwards.

Before the final roll-up, you have to ensure that your end users can handle the new technology environment, meaning that all of them understand what information is available, what it means, how to access it and what tools to use. Customized training for both standard and power users as well as support documentation will help with that. Besides that, you need to:

• Test the data warehouse performance, ETL, etc.
• Verify data quality (data legibility, completeness, security, etc.)
• Ensure users have access to a data warehouse, etc.

5. After-launch support and maintenance

After the initial deployment, you need to focus on your business users and provide ongoing support and education. Over time, data warehouse performance metrics and user satisfaction scores will have to be measured, as it’ll help you ensure the long-term health and growth of your data warehouse.

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Key roles for a data warehouse project.

Project manager

• Defines the scope of the data warehouse project and its deliverables.
• Outlines the project’s plan, including budget estimations, project resourcing and timeframes. 
• Manages day-to-day data warehouse project tasks and activities (resource coordination, project status tracking, project progress and communication bottlenecks , etc.)

Business analyst  

• Identifies business users’ requirements and ensures they are clearly communicated to the tech team.
• Conducts interviews and documents them.
• Assists data modeler and DBAs in data modeling, data mapping activities, etc.

Data modeler 

• Performs detailed data analysis.
• Designs the overall technical architecture of the data warehouse and each component in particular (data staging, data storage, data models, etc.).
• Supervises architecture development and implementation.
• Advises on a technology stack.
• Documents the scope of the overall solution and its constituents. 

Data warehouse database administrator (DBA)  

• Translates logical models into physical table structures.
• Ensures operational support of the database, tunes database performance to ensure data availability and integrity. 
• Plans a data backup/recovery plan, etc.

ETL developer

• Plans, develops and sets up the extraction, transformation, and loading pipelines.

Quality assurance engineer

• Develops a test strategy to ensure a data warehouse’s proper functioning and data accuracy.
• Identifies potential errors and ensures their resolution.
• Run tests on the developed DWH solution.

Besides these key roles, other professionals may participate in the project as well, such as a solution architect, a tech support specialist, a DevOps engineer, a data steward, a data warehouse trainer, etc. It is worth noting that sometimes individual staff members may perform several roles.

3 leading data warehouse technologies to consider

Using inappropriate technology is one of the reasons why data warehouse projects fail. Besides the fact that you need to correctly identify your use case, you also need to choose the optimal software from numerous seemingly similar options available on the market. Here, we review data warehouse services and platforms that have great customer satisfaction scores, are rated highly in various market research reports, and embrace the principles of data warehouse modernization . The described functionality is not exhaustive though: while drawing up their descriptions, we mainly concentrated on their data integration capabilities, built-in connectivity with analytics and business intelligence services, reliability, and data security.

Amazon Redshift

• Offers the federated query capability and built-in cloud data integration with Amazon S3 to query and analyze data of any type, format and size across operational databases and a data lake.
• Allows ingesting and transforming data in streams and batches, within and outside the AWS services with AWS Data Pipeline, AWS Data Migration Services, AWS Glue, and AWS Kinesis Firehose.
• Offers native integration with the AWS analytics services (AWS Lake Formation, Amazon EMR, Amazon QuickSight, Amazon SageMaker, etc.).
• Provides built-in fault tolerance and disaster recovery capabilities (automated cluster snapshots, snapshots replications, continuous cluster monitoring and replacement, etc.).
• Safeguards data with granular permissions on tables, multi-factor user authentication, data encryption, etc.
• Meets compliance requirements for SOC1, SOC2, SOC3, PCI DSS Level 1, HIPAA, ISO 27001, etc.
• Allows to decouple storage and compute resources.

Google BigQuery

• Offers native data integration with 150+ data sources via Cloud Fusion
• Provides multi-cloud analytics support (provided by Google BigQuery (Omni)) to query data across AWS and Azure (coming soon) without copying data.
• Native integration with Looker and the whole Google Cloud Analytics ecosystem.
• Charges for cold and hot data as well as for storage and compute resources separately.
• Provides replicated storage in multiple locations charge-free by default.
• Offers granular permissions on datasets, tables, views, multi-factor user authentication, data encryption (by default), etc.
• Meets compliance requirements for HIPAA, ISO 27001, PCI DSS, SOC1, SOC2, etc.

Azure Synapse Analytics

• Has 95+ native connectors for on-premises and cloud data sources via Azure Data Factory.
• Offers support for native HTAP via Azure Synapse Link.
• Supports big data and streaming data ingestion and processing with the built-in Apache Spark and Azure Stream Analytics event-processing engine.
• Native integrations with Power BI, Azure Machine Learning, Azure Cognitive Services, Azure Data Lake Storage, etc.
• Allows scaling storage and computation separately.
• Offers built-in fault tolerance and disaster recovery capabilities (automatic snapshots, geo-backup, etc.).
• Default data security features (granular permissions on schemas, tables, views, individual columns, procedures, etc., multi-factor user authentication, data encryption, etc.).

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Tips for ensuring your DWH project success

Go for agile dwh development.

Data warehouse development projects are time and resource consuming, so choosing an agile approach, which implies breaking the project into iterations with incremental investments, will help you start getting ROI early as well as minimize risks and avoid heavy upfront investments.

Ensure close cooperation between IT and business

Data warehouse success is a joint effort of IT and business specialists, who share the responsibility for the initiative from collecting business needs to data warehouse rollout and after-launch support.

Focus on end users

Guarantee high data warehouse adoption levels with solid support documentation, training and self-service data access tools for end users.

Consider expert recommendations

Building a data warehouse typically requires migrating workloads to the cloud, which is not easy since it requires specific skills and expertise. Therefore, do not disregard seeking advice from cloud migration experts when you start a development project. Also, if you decide to develop a data warehouse based on a platform such as Amazon Web Services (AWS), rely on AWS migration best practices  and check other relevant guidelines in case you prefer another cloud vendor.

A modern skillfully built data warehouse can help accomplish many of your current data management and analytics objectives, including broken down data silos, real-time analytics, interactive reporting, and safeguarded corporate data. And, even though to make your data warehouse a long-term success, you need considerable investments, don’t let it intimidate you. With reliance on a trustworthy BI vendor with solid domain expertise, tangible data warehouse benefits will not take long to appear.

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Building a Data Warehouse: A Comprehensive Guide and the Buy vs. Build Dilemma

Introduction: 

In today's data-driven world, businesses rely on data warehouses to efficiently store, manage, and analyze vast amounts of information. Building a data warehouse is a critical decision that requires careful consideration of various factors, including cost, scalability, maintenance, and time to market. In this article, we will explore the process of building a data warehouse, discuss the benefits of popular data warehouse solutions like Redshift and Snowflake, and delve into the buy vs. build dilemma.

I. Understanding Data Warehousing:

A. Definition and Purpose of a Data Warehouse:

A data warehouse is a centralized repository that consolidates data from multiple sources into a unified and structured format for efficient querying and analysis. Its primary purpose is to support business intelligence (BI) and decision-making processes by providing a reliable and consistent view of data across an organization.

B. Key Components of a Data Warehouse:

image source

A data warehouse comprises several essential components :

• Data Sources: These can include transactional databases, external data feeds, legacy systems, or even data from cloud-based applications.
• ETL Processes: Extract, Transform, Load (ETL) processes are employed to extract data from various sources, transform it into a standardized format, and load it into the data warehouse.
• Data Storage: The data is stored in a structured format optimized for querying and analysis, typically using a relational database management system (RDBMS).
• Metadata: Metadata provides information about the data stored in the warehouse, including its source, structure, and relationships.
• Query and Reporting Tools: These tools allow users to access and analyze data in the warehouse, generate reports, and gain insights.

C. Architecture and Data Modeling:

Data warehouse architecture can be categorized into three main types:

• Kimball Architecture: This architecture follows a dimensional modeling approach, organizing data into fact tables (containing measures) and dimension tables (containing descriptive attributes).
• Inmon Architecture: In this approach, data is normalized and stored in a third-normal form, resulting in a more flexible and scalable structure.
• Hybrid Architecture: It combines elements of both Kimball and Inmon architectures, leveraging the strengths of each.

D. Extract, Transform, Load (ETL) Process:

The ETL process is crucial for data warehouse development. It involves extracting data from various sources, transforming it into a consistent format, and loading it into the data warehouse. ETL tools are commonly used to automate this process, ensuring data quality, integrity, and compatibility.

E. Data Warehouse vs. Operational Database:

While operational databases are designed for transactional processing, data warehouses focus on analytics and reporting. Operational databases prioritize quick and efficient data modifications, while data warehouses prioritize data consolidation, historical analysis, and decision-making.

Steps in Building a Data Warehouse:

‍ a. planning and requirements gathering:.

Before starting the development process, thorough planning and requirements gathering are essential. This involves understanding the organization's data needs, defining the scope and objectives of the data warehouse, and identifying key stakeholders.

B. Infrastructure Considerations:

Selecting the right infrastructure for your data warehouse is crucial. Factors to consider include storage capacity, processing power, network connectivity, and scalability. On-premises, cloud-based, or hybrid solutions can be evaluated based on your specific requirements and budget.

C. Data Modeling and Schema Design:

Data modeling plays a crucial role in defining the structure and relationships within the data warehouse. Whether you choose a dimensional or normalized approach, careful consideration must be given to ensure optimal query performance and data integrity.

D. ETL Development and Data Integration:

The ETL process is responsible for extracting data from various sources, transforming it into a standardized format, and loading it into the data warehouse. ETL development involves designing data workflows, implementing data cleansing and validation rules, and integrating disparate data sources.

E. Performance Optimization Techniques:

To ensure efficient query performance, several techniques can be employed, such as indexing, partitioning, materialized views, and query optimization. These techniques help accelerate data retrieval and enable faster analysis.

F. Security and Access Control:

Securing the data warehouse is crucial to protect sensitive information. Implementing robust security measures, such as role-based access control, encryption, and auditing, helps ensure data privacy and compliance with regulations.

G. Testing and Deployment:

Thorough testing is essential to validate the data warehouse's accuracy, reliability, and performance. This includes unit testing, integration testing, and user acceptance testing. Once the testing phase is complete, the data warehouse can be deployed to the production environment.

III. Buy vs. Build: Evaluating the Options: ‍

A. introduction to buying a data warehouse.

In recent years, cloud-based data warehouse solutions have gained popularity due to their scalability, flexibility, and managed service offerings. Buying a data warehouse solution eliminates the need for building and maintaining the infrastructure and offers additional benefits.

B. Benefits of Buying a Data Warehouse:

• Rapid Deployment and Time to Market: Buying a data warehouse solution allows organizations to get up and running quickly. Cloud-based solutions, such as Redshift, Snowflake, BigQuery, and Azure Synapse Analytics, offer pre-configured environments that significantly reduce the time and effort required for setup.
• Scalability and Elasticity: Cloud-based data warehouses provide the advantage of seamless scalability. Organizations can easily scale up or down based on their storage and computing requirements, paying only for the resources they consume.
• Managed Services and Maintenance: By opting for a data warehouse solution, organizations can offload the burden of infrastructure management, software updates, and routine maintenance tasks to the vendor. This enables internal teams to focus on core business activities rather than IT operations.
• Advanced Analytics Capabilities: Many data warehouse solutions offer advanced analytics features, such as machine learning integrations, natural language processing, and predictive modeling. These capabilities empower organizations to extract valuable insights and drive data-driven decision-making.
• Integration with Third-Party Tools and Services: Data warehouse solutions often provide seamless integration with a wide range of third-party tools and services, including BI and visualization tools, data integration platforms, and data lakes. This integration facilitates a cohesive data ecosystem and streamlines the analytics process.

C. Prominent Data Warehouse Solutions:

• Amazon Redshift: Amazon Redshift , a fully managed data warehousing service, offers high performance, scalability, and cost-effectiveness. It integrates seamlessly with other Amazon Web Services (AWS) products and provides compatibility with existing SQL-based tools and applications.
• Snowflake: Snowflake is a cloud-native, fully managed data warehouse platform known for its scalability, elasticity, and ease of use. It separates compute and storage, enabling organizations to scale each independently, resulting in efficient resource utilization and cost optimization.
• Google BigQuery: BigQuery is a serverless, highly scalable data warehouse offered by Google Cloud. It excels in handling large volumes of data and provides tight integration with other Google Cloud services. BigQuery's pay-as-you-go pricing model makes it a flexible and cost-effective solution.
• Microsoft Azure Synapse Analytics: Azure Synapse Analytics is a unified analytics service that combines data warehousing, big data, and data integration capabilities. It integrates seamlessly with other Azure services and offers built-in security, scalability, and advanced analytics capabilities.

D. Factors to Consider When Choosing a Data Warehouse Solution:

• Cost and Pricing Models: Evaluate the pricing models of different solutions, considering factors such as storage costs, compute costs, and data transfer fees. Consider your organization's anticipated data volume and usage patterns to estimate the overall cost and determine which solution aligns with your budget.
• Performance and Scalability: Consider the performance requirements of your data warehouse. Evaluate the scalability options offered by each solution, such as the ability to scale up or down based on demand, and assess their performance capabilities to ensure they meet your data processing and querying needs.
• Security and Compliance: Data security is of utmost importance when selecting a data warehouse solution. Assess the security features provided by each solution, such as encryption, access controls, and compliance certifications (e.g., GDPR, HIPAA). Ensure the solution aligns with your organization's security and compliance requirements.
• Integration and Ecosystem: Consider the compatibility and integration capabilities of the data warehouse solution with your existing technology stack. Assess how well it integrates with your preferred business intelligence tools, data integration platforms, and other data-related services. A strong ecosystem of integrations can streamline data workflows and enhance productivity.
• Vendor Support and Reliability: Evaluate the vendor's reputation, reliability, and customer support. Look for customer reviews, case studies, and industry recognition to assess the vendor's track record in delivering quality service and support. Prompt and knowledgeable support can be crucial in resolving any issues that may arise.

IV. Conclusion: 

In conclusion, building a data warehouse is a complex undertaking that requires careful planning, technical expertise, and a significant investment of time and resources. However, the benefits of having a well-designed data warehouse are numerous, providing organizations with valuable insights to make informed business decisions.

When considering whether to buy or build a data warehouse, it is essential to weigh the advantages of popular solutions like Redshift, Snowflake, BigQuery, and Azure Synapse Analytics against the specific needs and constraints of your organization. Each solution offers unique features, scalability, and managed services that can significantly reduce the development and maintenance effort. However, it is crucial to assess factors such as cost, performance, security, integration, and vendor support to make an informed decision.

Ultimately, the decision should align with your business objectives, budget, and long-term data strategy. Whether you choose to build a data warehouse from scratch or opt for a ready-to-use solution, the key is to leverage the power of data to drive insights, innovation, and competitive advantage in today's data-driven world.

Frequently Asked Questions FAQs- Building a Data Warehouse

What is the concept of building a data warehouse? 

The concept of building a data warehouse involves the process of creating a centralized repository of integrated data from various sources, which is designed to support decision-making and business intelligence.

What are the 5 key components of a data warehouse? 

The five key components of a data warehouse are: 

• Source systems:
• ETL (Extract, Transform, Load) process:
• Data warehouse database
• Business intelligence (BI) tools:

What are the three approaches to building a data warehouse? 

The three main approaches to building a data warehouse are: 

• Top-down approach: In this approach, the data warehouse is designed and built from the top down, starting with the high-level business requirements and then working down to the detailed data structures and ETL processes. 
• Bottom-up approach: In this approach, the data warehouse is built from the bottom up, starting with the source systems and gradually building up the data structures and ETL processes to meet the business requirements. 
• Hybrid approach: This approach combines elements of both the top-down and bottom-up approaches, allowing for a more flexible and iterative development process. 

What are the 5 data warehouse architectures? 

The five common data warehouse architectures are: 

• Centralized data warehouse:
• Federated data warehouse:
• Hub-and-spoke architecture:
• Virtual data warehouse:

What is ETL in a data warehouse? 

ETL (Extract, Transform, Load) is a critical process in data warehousing that involves retrieving data from various source systems, such as operational databases, legacy systems, and external data sources or data marts. Cleaning, standardizing, and converting the extracted data into a format that is suitable for the data warehouse and then Transferring the transformed data into the data warehouse, typically into a staging area or directly into the data warehouse tables. 

What are the 4 key points of the data warehouse environment? 

The four key points of the data warehouse environment are: 

• Data sources: The various systems and applications that provide the data that is loaded into the data warehouse. 
• Data extraction and transformation: The processes and tools used to extract, transform, and load data into the data warehouse. 
• Data warehouse: The central repository where the integrated and transformed data is stored. 
• Business intelligence and reporting: The tools and applications used to analyze the data in the data warehouse and generate reports and insights. 

What are the functions of data warehouse tools and utilities? 

The main functions of data warehouse tools and utilities are: 

• Data extraction, transformation, and loading (ETL): Tools that automate the process of extracting data from source systems, transforming it into a consistent format, and loading it into the data warehouse. 
• Data modeling and design: Tools that support the design and development of the data warehouse schema, including the creation of fact tables and dimension tables. 
• Data quality management: Tools that help to ensure the accuracy, completeness, and consistency of the data in the data warehouse. 
• Data analysis and reporting: Tools that enable users to analyze the data in the data warehouse and generate reports and visualizations. 

What are the three C's of data warehousing? 

The three C's of data warehousing are: 

• Consolidation: The process of integrating and combining data from multiple source systems into a single, centralized repository. 
• Consistency: The ability to maintain data integrity and ensure that the data in the data warehouse is accurate, consistent, and up-to-date. 
• Clarity: The ability to present the data in a clear and easily understandable format, enabling users to make informed business decisions. 

What are the main functions of a data warehouse? 

The main functions of a data warehouse are: 

• Data integration: Combining data from multiple, heterogeneous sources into a unified, consistent format. 
• Data storage: Providing a centralized repository for the storage and management of historical data. 
• Data analysis: Enabling users to analyze the data in the data warehouse to uncover insights and trends.  

What are the four steps in designing a data warehouse? 

The four steps in designing a data warehouse are: 

• Requirements gathering: Identifying the business requirements and objectives that the data warehouse needs to support. 
• Data modeling: Designing the logical and physical data models for the data warehouse, including the fact tables and dimension tables. 
• ETL design: Developing the processes and workflows for extracting, transforming, and loading data into the data warehouse. 
• Implementation and testing: Building the physical infrastructure for the data warehouse, including the database, servers, and software, and testing the system to ensure it meets the business requirements. 

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Data Warehouse building: A Comprehensive Overview

• BI4Dynamics Blog
• Data Warehouse building: A Comprehensive…

In today’s digital age, data is becoming the lifeblood of businesses across industries. The ability to collect, analyze, and utilize vast amounts of data has become increasingly crucial for companies to gain a competitive edge. This is where the concept of data warehousing comes into play. In this comprehensive guide, we will delve into the world of data warehousing and explore everything you need to know about building a data warehouse from scratch.

Understanding Data Warehousing

Before we dive into the specifics of building a data warehouse, it is crucial to have a solid understanding of what data warehousing entails. At its core, data warehousing is the process of consolidating and organizing data from various sources into a single, centralized repository. This repository, known as a data warehouse, serves as a powerful tool for storing and analyzing large volumes of structured and unstructured data.

Data warehousing has become increasingly important in today’s data-driven world. With the exponential growth of data, organizations need a way to efficiently store, manage, and analyze vast amounts of information. A data warehouse provides a solution to this challenge by offering a structured and optimized environment for data storage and retrieval.

By centralizing data from different sources, a data warehouse eliminates data silos and enables organizations to gain a holistic view of their operations. This comprehensive perspective allows businesses to identify patterns, trends, and correlations that may not be apparent when looking at individual data sources in isolation.

Definition and Importance of Data Warehousing

Simply put, a data warehouse is a relational database that is specifically designed for query and analysis rather than transaction processing. It provides a means of storing and managing data in a way that facilitates efficient reporting and decision-making. The importance of data warehousing lies in its ability to provide businesses with timely, comprehensive, and accurate insights that drive strategic decision-making processes.

One of the key advantages of data warehousing is its ability to handle large volumes of data. Traditional transactional databases are optimized for handling small, frequent transactions, but they may struggle when it comes to processing complex queries on massive datasets. Data warehouses, on the other hand, are designed to handle analytical queries efficiently, making them ideal for business intelligence and reporting purposes.

Another important aspect of data warehousing is data integration. In today’s organizations, data is often scattered across multiple systems and applications. Data warehousing allows organizations to bring together data from various sources, such as operational databases, spreadsheets, and external data feeds, into a unified and consistent format. This integration process involves data extraction, transformation, and loading (ETL), which ensures that data is cleansed, standardized, and ready for analysis.

Key Components of a Data Warehouse

A successful data warehouse consists of several key components that work together to create a robust and reliable infrastructure for storing and retrieving data. These components include:

• Data Sources: The various systems and sources from which data is collected and integrated into the warehouse
• Data Extraction, Transformation, and Loading (ETL): The process of extracting data from source systems, transforming it to fit the data warehouse schema, and loading it into the warehouse
• Data Warehouse Database: The central repository where data is stored, organized, and optimized for query and analysis
• Data Access Tools: The software applications and interfaces that allow users to interact with and retrieve data from the warehouse
• Metadata Repository: The database that stores information about the data in the warehouse, including the structure, meaning, and relationships between different elements

Each component plays a crucial role in the overall functioning of a data warehouse. Data sources provide the raw data that is collected and integrated into the warehouse. The ETL process ensures that the data is transformed and loaded into the warehouse in a format that is suitable for analysis. The data warehouse database serves as the central repository where data is stored, organized, and optimized for efficient querying. Data access tools enable users to interact with the warehouse and retrieve the information they need. Finally, the metadata repository stores important information about the data in the warehouse, making it easier for users to understand and interpret the data.

The Role of Data Warehousing in Business Intelligence

Data warehousing plays a vital role in enabling effective business intelligence (BI) practices. By consolidating data from disparate sources into a single location, organizations can gain a holistic view of their operations, customer behavior, market trends, and more. This, in turn, allows businesses to derive valuable insights that can drive growth, improve decision-making, and optimize operations.

Business intelligence relies on accurate and timely information to support strategic planning and decision-making. Data warehousing provides a reliable and consistent source of data for BI initiatives. By storing data in a structured and optimized format, data warehouses enable organizations to perform complex queries and analysis, uncovering hidden patterns and trends that can inform business strategies.

In addition to providing a centralized repository for data, data warehousing also supports data governance and data quality initiatives. By establishing standardized processes for data extraction, transformation, and loading, organizations can ensure that the data in their warehouse is accurate, consistent, and reliable. This, in turn, enhances the trustworthiness and credibility of the insights derived from the data warehouse.

Overall, data warehousing is a critical component of modern business intelligence practices. It empowers organizations to harness the power of their data, gain actionable insights, and make informed decisions that drive success and competitive advantage.

Planning Your Data Warehouse

Before embarking on the journey of building a data warehouse, it is essential to have a clear plan in place. Planning involves understanding your data needs, setting goals for your data warehouse, and choosing the right architecture to support your objectives.

Identifying Your Data Needs

The first step in planning your data warehouse is to identify and understand your data needs. This requires a comprehensive assessment of your organization’s goals, objectives, and data requirements. Consider the types of data you need to store, the volume and velocity of data, and the desired level of data granularity. By understanding your data needs, you can ensure that your data warehouse is designed to meet your specific requirements.

Setting Your Data Warehouse Goals

Once you have identified your data needs, the next step is to set clear goals for your data warehouse. What do you want to achieve with your data? Do you want to improve reporting capabilities, gain actionable insights, enhance customer segmentation, or optimize operational processes? Defining your goals will guide the design and implementation of your data warehouse and ensure that it aligns with your broader business objectives.

Choosing the Right Data Warehouse Architecture

Choosing the right data warehouse architecture is a critical decision that will shape the scalability, flexibility, and performance of your data warehouse. There are three main types of data warehouse architectures: the traditional enterprise data warehouse (EDW), the hub-and-spoke architecture, and the data lake architecture. Each architecture has its own strengths and considerations, so it is essential to carefully evaluate your options and choose the architecture that best suits your organization’s needs.

Building Data Warehouse

With a solid plan in place, it’s time to embark on the exciting journey of building your data warehouse. This phase involves data collection and integration, data cleaning and transformation, and data loading and refreshing.

Data Collection and Integration

The first step in building your data warehouse is collecting data from various sources and integrating it into a single, unified format. This can involve extracting data from transactional databases, legacy systems, external sources, and other relevant sources. The collected data needs to be transformed and standardized before being loaded into the data warehouse for further analysis.

Data Cleaning and Transformation

Data coming from different sources often requires cleaning and transformation to ensure its quality and consistency. This process includes removing duplicates, resolving conflicts, standardizing formats, and handling missing or erroneous data. By cleaning and transforming your data, you can ensure its accuracy and reliability for effective analysis and reporting.

Data Loading and Refreshing

Once the data has been cleaned and transformed, it can be loaded into the data warehouse. This involves populating the database with the processed data, organizing it according to the defined schema, and optimizing it for efficient query and analysis. It’s crucial to establish regular data refreshing processes to ensure that your data warehouse remains up-to-date and relevant.

Managing Your Data Warehouse

Building a data warehouse is just the beginning. To ensure its long-term success, you need to effectively manage and maintain your data warehouse. This phase involves addressing data security and privacy concerns, performing regular maintenance tasks, and optimizing the performance of your data warehouse.

Data Security and Privacy

Data security and privacy are paramount when it comes to managing a data warehouse. You need to implement robust security measures to protect your data from unauthorized access, breaches, and misuse. This includes implementing access controls, encryption, and data anonymization techniques while adhering to relevant data protection regulations.

Data Warehouse Maintenance

Regular maintenance is essential for the smooth operation of your data warehouse. This includes monitoring system performance, troubleshooting issues, applying software updates, and ensuring data integrity. By regularly maintaining your data warehouse, you can prevent data corruption, optimize system performance, and address any potential issues proactively.

Performance Tuning in Data Warehousing

Performance tuning is an ongoing process in data warehousing. It involves monitoring and optimizing system performance to ensure that queries are processed quickly and efficiently. This can include index optimization, query rewriting, partitioning, and caching strategies. By fine-tuning the performance of your data warehouse, you can enhance user experience, reduce query response times, and maximize the value of your data.

Understanding the basics of data warehousing and following a step-by-step approach helps you build a strong tool. This tool gives your organization the advantage of making decisions based on data insights.

However, for those seeking an immediate, comprehensive solution, BI4Dynamics offers a pre-built, all-in-one data warehousing solution, refined and perfected through our experience with over 1,000 customers. Our ready-to-use platform eliminates the complexity of building a data warehouse from scratch, providing you with a robust tool designed to empower your organization with critical insights from day one. Whether initiating or advancing your data management capabilities, BI4Dynamics stands as your premier partner, offering an expertly crafted data warehouse that caters to all your analytical needs.

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Case Study: Building a Data Warehouse Powered by Snowflake

The client is a company in the healthcare sector that is in the midst of a rapid period of growth over a short time due to their continued success. The original data infrastructure they had in place was not going to be able to scale with the ongoing development, so they were rapidly approaching a point where they would need a solution that could serve their needs better than the current system. Although the client had adopted a cloud data warehouse technology in Snowflake before our team got involved, the original processes and usage would soon prove unsustainable with the incoming changes required to scale the solution for their rapid growth. They needed a better way to support the changes the company was undergoing and clean up older data in order to succeed.

The INSPYR Solutions Professional Services division was originally engaged for Tableau consulting services. We noticed systemic issues during the course of our work and spent some time to understand the state of the data solution environment at the company. We saw an opportunity to create a solution that would serve the client’s needs much better than their current infrastructure and streamline their Tableau dashboard development efforts, so we presented these ideas. The client had not previously considered implementing an enterprise data warehouse built in the Snowflake cloud, but it would prove to be the exact solution they needed to sustain the company’s rapid growth and data metrics needs.

INSPYR Solutions quickly deployed a team of consultants to work on different aspects of the project including four Data Engineers specializing in ELT and a Solutions Architect. The original project timeframe was approximately three months, but was later extended as the scope of the project changed to address additional needs that were discovered throughout the process.

The solution was built with Matillion ELT tools and incorporated a data vault data warehouse, chosen because it would be particularly beneficial for a client in the healthcare sector, which is known for constant change. This type of data warehouse would be well equipped to absorb changes with relative ease, creating a flexible solution that would work for the client in the long term. In addition, this solution would capture historical data changes, which was a first for the company.

Our team worked together with the client’s internal team to complete this project and the client’s staff took on roles as administrators for Snowflake and Matillion. The client was also responsible for landing the external data into Snowflake and the INSPYR Solutions team would work on it from that point forward in the solution. Working together with the client’s internal team required a great deal of synchronization between these processes, which was handled fluidly between the client’s internal team and the INSPYR Solutions team to create an efficient workflow.

The first phase of the project consisted of three data marts to be architected, developed, and rolled out over the course of three months. The first data mart was constructed for the sales department since it contained many of the data assets the other data marts would depend on. In the course of their work, the team uncovered some issues with data quality that needed to be addressed. This additional work, in combination with changes to the scope of the original project charter to go with a data vault architecture, required that the project be extended in order to accommodate the additional tasks.

The INSPYR Solutions team did a thorough job handling these needs and exceeded the client’s expectations by the end of the first phase by leading the client’s internal team through several important milestones. This encompassed the first full development path to production through the various development, testing, and production environments including the first to use a deployment model; the first project to have a very thorough and full documentation done in conjunction with the milestones; the first project fully transitioned and functional in JIRA; the first to contain a process for capturing and logging data quality issues for the client’s new data governance effort; and the first project to include building out a data dictionary. These many firsts for the client represented a huge step forward in its development processes and will provide the groundwork for future projects.

Beyond these innovative improvements, INSPYR Solutions helped with other processes that would assist the client in future endeavors. For example, our team recorded videos of peer review sessions that were then utilized as helpful training materials when new members joined the team. This documentation will be invaluable as the team expands and when our team eventually hands off the system to the client. Another aspect of our work with this client also included scaling up the project management structure from a task tracking system to utilize Atlassian JIRA and include documentation and training materials that were stored in Confluence.

Once fully launched, our solution will solve several problems the client was experiencing. First, our team created a more efficient means for Tableau reporting to help with the client’s data. Another solution INSPYR Solutions created for the client was making the data sources in Tableau live-read (pass-through) from Snowflake. Additionally, the data vault now allows for easier incremental field additions with minimal to no data pipeline or data warehouse schema changes. Lastly, the single source(s) of truths could be realized as single star schemas. These certified data sources that allow for ad-hoc querying by others in the company without fear of multiple versions of business rules, etc.

After the initial project phase, the client signed INSPYR Solutions on for two more data marts to further extend the enterprise data warehouse and has been looking to utilize our experts for additional projects. Our team had a huge impact on the client’s business by helping their internal team improve their efficiencies, as well as by creating a system that would allow for faster creation of dashboards, alleviating pressures on the internal team for specialized ad-hoc querying. We continue to work with this client to this day by supporting further development of the company’s enterprise data warehouse powered by Snowflake.

Client Profile

The client is a part of the healthcare sector focused on advancing the health of certain populations and raising awareness of their specific healthcare issues. The company’s goals include developing solutions to advance healthcare through innovation and to help both patients and practitioners move toward a better tomorrow.

Technologies Supported

Snowflake, Matillion, Tableau, ER Studio, Atlassian Suite (JIRA / Confluence).

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Kip Wright serves as Chairman of INSPYR Solutions. Wright is a staffing industry veteran instrumental in shifting the landscape of the human capital industry. Known as a passionate leader with an innate ability to drive both growth and organizational efficiencies, Wright is responsible for all facets of executive strategy and leadership for the INSPYR Solutions organization.

In his 26-year career, Wright has served in numerous leadership roles with public and private staffing and workforce solution companies. As Senior Vice President of Manpower, North America, he successfully led Manpower’s $2 billion contingent staffing line of business for the United States and Canada. Wright also served as Senior Vice President of ManpowerGroup Solutions in North America, which include service offerings of Recruitment Process Outsourcing (RPO), Managed Services Provider (MSP) and Talent Based Outsourcing (TBO). Wright joined ManpowerGroup through the acquisition of COMSYS / TAPFIN where he served in several executive roles including Senior Vice President of Managed Solutions, Chief Financial Officer and President of TAPFIN. Wright began his career as an auditor with Ernst & Young.

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• Building a Data Warehousing Architecture for an HR Department

Building a Data Warehousing Architecture for an HR Department ¶

Astera Data Warehouse Builder provides an open-ended platform that allows users create a data warehousing architecture based on their requirements. Users can either build a data warehouse from scratch to enable historical analysis for future transactions or take existing historical data and build an architecture around it.

In this article, we will be observing a use case where an HR department wishes to move its data from Excel sheets to an automated data warehousing environment.

In this use case, the HR department of a fictitious company has a vast dataset that comprises numerous Excel sheets. These sheets are maintained manually and include the following datasets, to name a few: Employees , Departments , Expenses , Currency , etc.

These Excel sheets are logically related to each other but there are no formal relationship links between them. Therefore, it is impossible to perform any analysis for reporting. Moreover, some of these sheets already contain historical data, which is updated and maintained manually. For further clarity, here is a look at a portion of the Excel sheet containing employee data:

You’ll notice that there are multiple records for a single employee, which shows that each employee’s history has been maintained within the sheet. Our goal is to move all of this data to a data warehousing environment, and then automate the process of keeping track of historical data.

In this unique situation, let’s take a look at the process we’ve followed to design, develop, and maintain a data warehouse for this HR department, using Astera Data Warehouse Builder.

Step 1: Design a Dimensional Model ¶

Since all of the source data is contained in Excel sheets and comprises of historical data, there is no need for a source model in this particular situation. Hence, our first step would be to design a dimensional model from scratch. Here is the model that we’ve designed:

As you can see, this model represents a snowflake schema that contains the following:

• Three Fact Entities
• Six Dimension Entities
• One Date Dimension Entity
• Four General Entities

We’ve created this model from scratch by:

• Adding and configuring new entities via the Entities object in the data model toolbox.
• Creating relationships between these entities via the Link Entities to Create Non-Identifying Relationships and Link Entities to Create Identifying Relationships options in the data model toolbar.

To learn about how you can create a data model from scratch, click here .

After configuring the entities in this model and establishing the relations between them, we’d successfully created the structure for our dimensional model. The next step was to assign dimensions and fact entity types and pick appropriate dimension and fact roles for the fields in each of these entities. To learn about how you can convert a data model into a dimensional model, click here .

Dimension Entities ¶

Let’s take a look at the layout of two dimension entities in this model: Employees and Employees_Details . As you can see, these two entities are related to each other in the model.

Employees ¶

Here is the layout of the Employees entity:

Here, you’ll notice that all the only SCD type that has been used for the fields in this entity is SCD1. This is because there is no need to keep track of historical data for these fields. From a logical standpoint, there is no need to keep track of any changes in the Gender , Age , Religion , and CGPA fields. The same reasoning applies to all of the other fields that have been assigned the SCD1 role.

Employees_Details ¶

Here is the layout of the Employees_Details entity:

In this entity, most of the fields have been assigned the SCD2 role because there is a need to record historical data. Let’s take the Title field as an example. An employee’s title can change unpredictably and should therefore be kept track of.

Fact Entities and Date Dimension Entity ¶

Let’s take a look at the layout of the Payroll_Expenses fact entity.

Here, the Payroll_Expenses_Date_Key field has been assigned the Transaction Date Key role. This field acts as a foreign key in the relation between this fact entity and the date dimension entity in the model.

General Entities ¶

The general entities in this model represent bridge tables that have been used to link two dimension entities to each other.

Bridge tables are needed here to establish a many-to-many relationship between the two dimension entities in question. As an example, let’s take a look at the relationship between the Departments and Employees_Details entities. One employee can be part of multiple departments and one department can have multiple employees in it. Hence, there is a many-to-many relationship between them.

Once the dimensional model is ready, we’ll verify it to check for any errors and warnings. After that, we’ll deploy it to the server for usage.

Step 2: Populate and Maintain the Data Warehouse ¶

Since the source data, in this case, already contains historical records, there are two separate sets of flows that we’ve created to populate and maintain/update the data warehouse, respectively. But before we move towards the actual dataflows, here is a look at the project that contains all of the items needed to execute this data warehousing process:

Here, there are two folders that contain dataflows: FirstRun_Dataflows and Final_Dataflows .

The dataflows present in the FirstRun_Dataflows folder are used to dump all of the source data into the data warehouse right after it is created.

On the other hand, the dataflows present in the Final_Dataflows folder are used to update the data warehouse at regular intervals.

First Run Dataflows ¶

Let’s take a look at the DimEmployee_Details dataflow in the FirstRun_Dataflows folder. As the name suggests, this dataflow is designed to populate the Employee_Details table in the data warehouse.

Here, we’ve extracted data from two source Excel sheets via Excel Workbook Source objects and joined them together using the Join transformation object. After that, we’ve used a couple of Expression objects to add some new fields to the table. Between the two Expression objects, we’ve used a Database Lookup object to find each employee’s key from the original Employee table and add it to the Employee_Details table.

Finally, we’ve used a Database Table Destination object to load data into the dimension table. Notice that even though the destination is a dimension table, we have not used the Dimension Loader object in this dataflow. The purpose of this dataflow is only to dump data into the destination once. Since there is no need to keep track of historical data, dimension roles do not need to be taken into account. Therefore, a simple Database Table Destination object is enough to complete the job successfully.

Upon running this dataflow, the source data from employee dataset will be written to the dimension table. Since this is a one-time job, we will not be needing this dataflow for any further processing.

Final Dataflows ¶

Now, let’s have a look at the DimEmployees dataflow in the Final_Dataflows folder of the project. The purpose of this dataflow is to maintain and update data in the Employees dimension table.

Here, we’ve simply extracted data from the source Excel sheet and then used an Expression object to add some new fields to the dataset. Moreover, we’ve used three Database Lookup objects to search for and add values to these new fields. Our main focus, however, is on the Dimension Loader object that has been used as a destination in this dataflow.

The Dimension Loader object is connected to the deployed dimensional model and takes all of the assigned dimension roles into account when updating and maintaining data in the destination table. Here is a look at the Layout Builder screen in the properties of the Dimension Loader object:

You can see that the Dimension Role for each field, as assigned in the dimensional model, has been identified by the Dimension Loader object. Since this dataflow will be used for all further processing of data in the Employees table, the proper identification and implementation of these roles is pivotal in keeping track of historical data.

Orchestration ¶

We’ve designed a workflow to orchestrate the entire process of updating and maintaining data in the final data warehouse. Here’s what it looks like:

Not only does this workflow save a user from the hassle of executing each dataflow individually, but it also dictates the sequence in which the dataflows will be executed. For instance, dimension tables always need to be populated and updated before fact tables. Here, the dataflows that we created for dimension tables have been placed first in the sequence, followed by the bridge table dataflows, and then finally, the fact table dataflows.

To learn more about how you can use a workflow to orchestrate a data warehousing process, click here .

Automation ¶

Finally, we’ve created a new schedule via the job scheduler , to automate the process of updating data in the data warehouse.

Here, you can see that we’ve scheduled our workflow to be executed on a weekly basis, every Friday at 6 pm. Now that this job has been scheduled on the server, it will be executed automatically at the assigned frequency. Therefore, the data warehouse will be updated automatically.

To learn more about how you can use the job scheduler to automate a data warehousing process, click here .

Step 3: Visualize and Analyze ¶

Now that the final data warehouse is ready, the HR department can integrate their data with industry-leading visualization and analytics tools like Power BI, Domo, etc. through Astera Data Warehouse Builder’s OData module. Here is a look at a sample dashboard that we’ve prepared for this use case:

This concludes our discussion on building a data warehouse architecture for an HR department using Astera Data Warehouse Builder.

Data Topics

• Data Architecture
• Data Literacy
• Data Science
• Data Strategy
• Data Modeling
• Governance & Quality
• Education Resources For Use & Management of Data

Case Study: Cornell University Automates Data Warehouse Infrastructure

Cornell University is a privately endowed research university founded in 1865. Ranked in the top one percent of universities in the world, Cornell is made up of 14 colleges and schools serving roughly 22,000 students. Jeff Christen, data warehousing manager at Cornell University and adjunct faculty in Information Science, and Chris Stewart, VP and general […]

Cornell University is a privately endowed research university founded in 1865. Ranked in the top one percent of universities in the world, Cornell is made up of 14 colleges and schools serving roughly 22,000 students.

The Primary Issue

Cornell was using Cognos Data Manager to transform and merge data into an Oracle Data Warehouse. IBM purchased Data Manager and decided to end support for the product. “Unfortunately, we had millions of lines of code written in Data Manager, so we had to shop around for a replacement,” said Christen. He looked at it as an opportunity to add new functionality so that their data warehouse ran more efficiently.

The Assessment

Christen’s IT team had to confine processing to hours when the university was closed, so batch processing from financial, PeopleSoft, or student records couldn’t start warehouse processing until the end of normal operations and had to be completely finished by 8:00 a.m. when staff arrived as they needed access to the warehouse.

“It was getting really close. We were frequently bumping into that time,” said Christen. Because their processing window was so short, errors and issues could be very disruptive.

“Our old tool would just log it if there was an issue, but then we couldn’t load the warehouse, because some network glitch that probably took seconds was enough to take out our nightly ETL processing,” elaborated Christen.

Outdated documentation was also a problem. Stewart said that they joke with their customers about documenting a data warehouse. “There are two types of documentation: nonexistent and wrong. People laugh, but nobody ever argues that point because it’s the thing that people don’t like to do, so it rarely gets done,” said Stewart.

Because it is an academic institution, licensing and staffing costs were important factors for Cornell. Stewart often sees this in government and in higher education organizations where the administration has increasing data needs, yet the pool of available people is small, like Christen’s staff of four.

Stewart said that automation can lift much of that workload so staff can get more accomplished in a shorter amount of time. “You can’t just go out and add two more people. If you have more work, you need to get more out of your existing staff,” said Stewart.

Finding a Solution

Christen started to shop around for ETL tools , with an eye to adding some improvements. There were several key areas he focused on when evaluating vendors: documentation, licensing costs, improving performance and being able to work within existing staffing levels. In 2014, Christen attended the Higher Education Data Warehousing conference to research options.

WhereScape was one of the exhibitors at the conference and one of the features that caught his attention was its approach to documentation. “Our customers were used to having outdated and incomplete documentation, and that was something WhereScape definitely had a handle on,” he said.

Most of the products Cornell considered required licensing by CPU, which could prove cost-prohibitive as Cornell’s extensive data warehouse environment was scaled for end-user query performance.

“We have a ton of CPUs,” Christen said. CPU-based licensing costs would be significant, and they found themselves trying to figure out how to re-architect the entire system to reduce the CPU footprint enough so that the licensing could work, a process that would create other limitations. WhereScape’s license model is a developer seat license, so with four full-time warehouse developers, they only needed to purchase four named user licenses.

“There’s no separate license for the CPU run-time environment with WhereScape, so if we’re successful, we’ll get everything converted, but there’s no penalty for how we configure the warehouse for end-user performance or query performance,” Christen said.

Being able to integrate and use the product without increasing the number of developers was a clear advantage. “That’s has been a key driver for organizations evaluating automation for their teams,” Stewart added.

Cornell didn’t just rely on marketing material to make their decision. They did an on-site proof of concept where one of their developers worked with the product on a portion of their primary general ledger model. They discovered that WhereScape was intuitive enough that one of their ETL developers was able to code a parallel environment in the proof of concept with minimal assistance from WhereScape. The developer hadn’t gone through any formal training, which proved that the learning curve would be manageable. \

The proof of concept allowed them to get a nearly apples-to-apples comparison, which showed “huge improvements” in load time performance compared to Data Manager. “So, it was a robust enough tool, but also intuitive enough that it could be mastered in a few weeks,” said Christen.

About WhereScape

WhereScape helps IT organizations of all sizes leverage automation to design, develop, deploy and operate data infrastructure faster.

“We realized long ago that there were patterns in data warehousing that really transcend any industry vertical or any size of company,” said Stewart.

Because the process of building a data warehouse out is primarily mechanical, and much of that is common among data warehousing organizations, WhereScape automates both the design and modeling of the data warehouse, all the way through to the physical build.

“Even deployments, as you’re moving a project from development to quality assurance environment (QA), and then on to production, we’re scripting all that out as well,” said Stewart. These are all processes companies usually use multiple tools to address – a resource-heavy process that can create a silo for each tool.

“We have one tool suite that covers data warehousing end-to-end and it’s just one set of tools to learn,” said Stewart. Instead of licensing separate tools to for each part of building a data warehouse, then finding a place to install all those tools, and spending weeks for staff training and management – teams have just one tool to learn and use. Handing off the build to WhereScape’s automated process frees up time and energy so that the business can take advantage of that data and produce useful analytics.

The initial wins of the conversion from their traditional ETL tool to WhereScape allowed Cornell to cut their nightly refresh times in half, or better, in some cases. Although they didn’t start that way, they are now a 100 percent WhereScape solution, with 100 percent Amazon-hosting as well.

“We did a major conversion which took a few years to get to WhereScape from our old tool, but that’s behind us. We’re running WhereScape on Amazon Web Services in their Oracle RDS service,” said Christen.

Although they just finished this conversion in the last year, since 2014 when they purchased WhereScape, all new developments and enhancements have been done in WhereScape.

“There’s actually an option to fix the problem, restart it, and still complete before business hours, which is a big win for our customers,” said Christen. “Essentially, we’ve cut our refresh times in half, so not only can the team complete all the processing they need with their batch windows, we’re not brushing up against business hours anymore.”

By automatically generating documentation, WhereScape solved the problem of outdated and incomplete documentation.

What’s Next?

To take full advantage of the automated documentation process, Cornell decided to build in some new subject areas, but the speed of the tool outstripped their internal modified waterfall approval process. Christen believes they can speed up their process now that they can quickly put out a prototype. They can start receiving feedback immediately from customers within days rather than weeks, and from there, refine the model until they’re ready for production.

“So, it’s changing our practices now that we have some new abilities with WhereScape,” said Christen. One of the next steps is to more fully leverage and market the documentation so they can start providing their customers with more information about the attributes that are available in the warehouse.

An unexpected benefit is that Christen’s Business Intelligence Systems students get to use WhereScape to learn Dimensional Data Modeling, ETL concepts, and Data Visualization hands-on with real datasets.

“We’re teaching the concepts of automation so they learn the hard way, with SQL statements, and then we use WhereScape and they can see how quickly they can create these structures to build out real dimensional model data warehouses,” explained Christen.

Stewart noted that they’ve had inquiries from other universities that have heard about Christen’s use of WhereScape in the classroom and are interested in incorporating WhereScape into their curriculum, so the students can get more work done in a semester.

“It’s a similar benefit to what our customers are receiving in their ‘real-world’ application of automation, and it is giving students the chance to understand the full data warehousing lifecycle,” said Stewart.

Image used under license from Shutterstock.com

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A Data Warehouse for E-Commerce

Case study: how a data warehouse helped one company become the largest ever e-commerce acquisition.

How cloverdx is a key technology asset for data-driven analytics and optimization.

A high quality data warehouse is critical for this fast-growing company. Being able to bring procurement, sales and marketing, inventory, logistics, product development and other data into a meaningful data warehouse gives them deeper insights, facilitates better quality decision-making and satisfies their hunger for data and extracting value from it.

Consolidating data at scale

The company had always had a data-driven approach, but were struggling to scale their manual data integration operation to keep up with their growth. As data volumes and the number of systems were increasing,  they were looking for a better way to get insights from every area of the business quickly.

A key requirement was to be as agile as possible, in order to handle changes in the business and get fast answers to business questions. Using CloverDX to bring data from different sources – from the cloud, databases, applications and elsewhere –  into the data warehouse means it can now be accessed without the need for extra development work. 

Seamless data pipeline management

The company can now manage their data integration pipelines more easily and more reliably, with data being pushed into Tableau for analysis. CloverDX ensures that the data warehouse has a constant stream of live daily updates , enabling each department to take core data and build their own business-critical reporting. 

Business unit leaders, as well as the business intelligence team, now have a far more detailed analysis of costs, margins and pricing promotions. They can continually optimize everything from sales analytics, logistics and delivery to shipping, cost control and vendor comparisons.

With the focus on data-driven optimization to give them a competitive edge, this e-commerce company was the subject of one of the largest ever acquisitions in online retail.

• Data warehouse
• Integrates procurement, sales and marketing, inventory, logistics, product development and other data into one data source
• Powering over 100 Tableau users, satisfying the hunger for extracting meaning from corporate data
• Replaced custom scripted solution that had been built in-house
• Is an integral part of the company’s technology stack
• Played a role in attracting the highest ever acquisition of an e-commerce business (2017)

Case Study: Leading E-Commerce Site Puts Data at the Center of Success

Data Warehouse

• Needed to be able to:
• Get insights from every area of the business
• Scale to keep up with rapid growth
• Be agile enough to handle changes in the business
• Constant stream of live updates from across the business
• Can perform more detailed analysis and optimization
• Data from different sources accessible without extra development work

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Integration of building age into flood hazard mapping: a case study of al ain city, united arab emirates.

1. Introduction

2. materials and methods, 2.1. study area, 2.2. flood-hazard factors and mapping, 2.3. score and weight assignment, building age extraction, 4. discussion, 5. conclusions, 6. recommendations.

• There is a need to document flood events spatially and temporally, both from rainfall and from infrastructure failure. Moreover, predictive analysis needs to be improved to assess areas that could be prone to flooding by adding more accurate data coupled with hydrological models.
• Soft solutions: Implementing effective planning rules and enhancing public awareness can play a crucial role in addressing the issue of flooding potential. By establishing comprehensive planning regulations, including zoning and land-management guidelines, Al Ain can ensure sustainable development and minimize the impact of flooding.
• Hard solutions: Al Ain has already constructed several dams to harness rainfall, and further dams can be considered as land use alters the topography. Expansion of green areas and maintenance of efficient drainage systems are also important.
• In assessing building resilience and establishing construction guidelines, it is essential to allow zoning planning and to establish more stringent requirements for issuing building permits. This will ensure that the structures can withstand potential disasters. Educating people about the hazard levels of their structures will provide valuable insights into their structural vulnerabilities and encourage them to implement the necessary improvements to make their buildings more resilient and better prepared for potential hazards [ 18 ].
• Assessing the effectiveness of property-level resistance and resilience measures can reduce loss and repair time due to flooding. For example, property owners can retrofit or demolish old buildings and adjust building heights in flood-vulnerable areas [ 65 ]. Develop a comprehensive database about buildings, including building age, and use the database in flood hazard and risk mapping.
• Cooperation between various stakeholders, such as central and local governments and the private sector, is needed. Future research can use machine learning models and satellite-derived rainfall data to predict flash floods in Al Ain [ 66 ].

Author Contributions

Data availability statement, acknowledgments, conflicts of interest.

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• Ogania, J.L.; Puno, G.R.; Alivio, M.B.T.; Taylaran, J.M.G. Effect of Digital Elevation Model’s Resolution in Producing Flood Hazard Maps. Glob. J. Environ. Sci. Manag. 2019 , 5 , 95–106. [ Google Scholar ] [ CrossRef ]
• Hamlat, A.; Meharzi, S.; Guidoum, A.; Sekkoum, M.; Mokhtari, Y.; Kadri, C.B. GIS-Based Multi-Criteria Analysis for Flood Hazard Areas Mapping of M’zab Wadi Basin (Ghardaia, North-Central Algeria). Arid. Land Res. Manag. 2023 , 38 , 1–25. [ Google Scholar ] [ CrossRef ]
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Alsumaiti, T.; Yagoub, M.M.; Tesfaldet, Y.T.; Alhosani, N.; Pakam, S. Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates. Water 2024 , 16 , 2408. https://doi.org/10.3390/w16172408

Alsumaiti T, Yagoub MM, Tesfaldet YT, Alhosani N, Pakam S. Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates. Water . 2024; 16(17):2408. https://doi.org/10.3390/w16172408

Alsumaiti, Tareefa, M. M. Yagoub, Yacob T. Tesfaldet, Naeema Alhosani, and Subraelu Pakam. 2024. "Integration of Building Age into Flood Hazard Mapping: A Case Study of Al Ain City, United Arab Emirates" Water 16, no. 17: 2408. https://doi.org/10.3390/w16172408

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