How Automated Testing Enhances Data Warehouse Testing

Introduction

Every business deals with data from various sources that needs to be efficiently managed and stored in a data warehouse. With the increasing adoption of cloud-based data warehouses, new challenges arise that differ from their traditional on-premise counterparts.

To understand the importance of data warehouse testing, envision a data warehouse as a bank continuously receiving and distributing vast amounts of data for user feedback and acceptance. After each iteration, it becomes necessary to replenish every data table to prepare for the next phase. This exclusive process demands quality assurance methods and tools that surpass manual testing, known for its cumbersome and time-consuming nature. The ideal solution lies in leveraging automated tools for data warehouse testing, often referred to as ETL (Extract, Transform, and Load) testing.

What is Data warehouse Testing?

Data warehouse testing is a comprehensive process that verifies the data warehouse landscape to ensure data accuracy, completeness, integrity, and quality. It encompasses two key components: ETL testing, which evaluates the extraction, transformation, and loading processes, and BI report testing, which validates the accuracy and functionality of business intelligence reports generated from the data warehouse.

Data warehouse testing plays a crucial role in ensuring the success and effectiveness of a data warehouse implementation. By conducting thorough testing, organizations can uncover and resolve issues related to data accuracy, completeness, and integrity. This helps to minimize the risk of making decisions based on incorrect or incomplete information, leading to more accurate and reliable insights for decision-making. Additionally, data warehouse testing helps to identify and address performance bottlenecks, ensuring that the data warehouse can handle large volumes of data and support concurrent user access. With robust data warehouse testing, businesses can have confidence in the quality and reliability of their data, enabling them to make well-informed decisions.

How Automated Testing Tools Enhance Data Warehouse Testing?

Data warehouse test automation includes all the tools that control, execute, and compare outcomes by configuring the required test conditions and other parameters, including test reporting functions. As the name suggests, test automation is an overarching approach to automating a previously manual process. As businesses rely on data and data-driven insights, their growth depends on ensuring that the data they own is stored, refreshed, and accessed in the most optimized manner possible. With billions of data points and records generating huge volumes, the cloud’s data integrity is critical for digital transformation and its very functioning. All of this must occur at extremely high speeds while ensuring maximum business continuity, governance, and compliance security. This makes the entire process as sophisticated as possible!

Challenges in Data Warehouse Testing

To understand the intricacies of data warehouse or ETL testing, it is important to know beforehand that it differs significantly from regular testing in many regards. However, with data taking center stage in ETL, testing becomes crucial to ensuring reliability, consistency, and validating processes to generate positive business outcomes.

Beyond data, ETL poses many challenges, including:

• Huge data volumes, coupled with high complexity
• Inefficient or insufficient procedures and processes
• Architectural differences,which may pose more technical challenges along with time and cost overruns
• Risk of data loss during ETL testing
• Possibility of data duplication and incompatibility during ETL testing
• Inclusive test beds may be lacking, leading to improper test results
• Securing and building test data is a time-consuming process
• Results generated in the absence of complete business information may fall short of expectations, rendering the entire exercise pointless

Manual ETL testing can be exhaustive and time-consuming, despite its ability to uncover many data defects. However, certain types of defects may go undetected. Automation, on the other hand, can effectively address some of these challenges. ETL test automation involves developing programs to test the data, which can be executed quickly and repeatedly. This approach is a cost-effective way to conduct ETL testing.

However, data warehouse testing comes with certain caveats. For instance, test automation tools can be expensive, and manual testing may still be required. The true benefits of automated testing can be observed over the long term, especially when used for regression testing repeatedly. Furthermore, with proper planning, diligence, and continuous monitoring before, during, and after ETL, the likelihood of success can be higher and faster.

Benefits of Data Warehouse Testing

Data warehouse testing comes with several challenges, but its benefits make it a valuable investment for any enterprise. These benefits include:

• High-quality data: ETL testing ensures that data is of high quality, which can be accessed for data analysis, providing business leaders with a better understanding of the state of the enterprise.
• Early identification of defects: ETL helps identify defects early on, and these issues can be resolved promptly, saving valuable resources and avoiding costly errors in the future.
• Minimized financial loss: ETL testing eliminates unreliable data during the testing phase, preventing enterprisesfrom incurring financial losses resulting from bad data.
• Compliance made easier: The thorough ETL testing process helps enterprises meet regulatory mandates and compliance norms across different geographies, avoiding penalties and preserving their brand reputation.
• Prevention of bad data: Enterprises rely heavily on data-based decisions. Still, the use of inferior, outdated, or incorrect data can be damaging to their reputation and growth prospects. Conducting periodic and comprehensive data testing can prevent such damaging implications. Investing in the quality of data should be a priority before migrating to a data warehouse. By doing so, the costs of migration can be justified, and enterprises can maximize their ROI.

How Hexaware’s JUMBO Automated Data Testing Solution Can Help

Within the ambit of data-centric testing, Hexaware provides an industry-tested automated data testing solution with an experienced test data engineering team covering the entire gamut of varied expertise required. It is called JUMBO.

One of JUMBO’s biggest advantages is that it provides QA teams with actionable insights that can lead to tangible business benefits. How does it do this? JUMBO’s end-to-end automated data testing solution covers all stages of the Data Adoption Lifecycle – Data Pre-Extraction Testing, Data Extraction Testing, and Data Transformation Testing, ensuring no blind spots at any point. It can also compare large data volumes with 100% testing faster than other solutions on the market, greatly reducing manual effort through customization for specific queries. JUMBO is ahead of the competition when it comes to extreme automation and has zero associated license costs.

For more information on how Hexaware’s JUMBO can help you with your data warehouse testing requirements, please visit: https://hexaware.com/services/digital-assurance/data-centric-testing/