Conquering the challenges of Data Warehouse ETL Testing

1. Conquering the challenges of Data Warehouse ETL Testing

2. Introduction: ETL stands for Extract-Transform-Load and is a typical process of loading data from a source system to the actual data warehouse and other data integration projects. It is important to know that independent verification and validation of data is gaining huge market potential. Many organizations and companies are now thinking of implementing ETL and Data warehouse processes as they realize that valid data in production is critical for making informed business decisions.  Importance of Data Warehouse for organizations Organizations with already well–defined IT practices are at an innovative stage, leading the next level of technology transformation by constructing their own data warehouse to store and monitor real-time data. However, such organizations realize that testing the data is business-critical as it ensures the data collected is complete, accurate, and valid. They also understand the fact that comprehensive testing of data at every point throughout the ETL process is important and inevitable, as more of this data is being collected and used for strategic decision-making that impact their business forecasting capabilities. But certain strategies that are being followed currently are time-consuming, resource-intensive, and inefficient. A well-planned and effective ETL testing scope guarantees smooth conversion of the project to the final production phase. Now, let us see some of the issues that are common with ETL and Data Warehouse testing. 

3. Some of the important ETL testing challenges are:  • Unavailability of inclusive test bed at times  • Lack of proper flow of business information  • Loss of data might happen during the ETL process  • Existence of several ambiguous software requirements  • Existence of apparent trouble in acquiring and building test data  • Production sample data is not a true representation of all possible business processes  • Some of the important issues with Data Warehouse testing are:  • Data Warehouse/ETL testing requires SQL programming. As most of the testers usually have limited SQL coding skills, it makes data testing very difficult  • Performing Data  completeness checks for the transformed columns is tricky  • Certain testing strategies used are time consuming 

4. Types of ETL Testing  Data is important for all businesses to make critical decisions. ETL testing plays a significant role in verifying, validating, and ensuring that the business information is exact, consistent, and reliable. ETL Testing is data–centric testing, which involves comparing large volumes of data across heterogeneous data sources. This data–centric testing helps in achieving high–quality data by getting the erroneous processes fixed quickly and effectively.  Data-centric Testing: Data-centric testing revolves around testing the quality of data. The objective of data-centric testing is to ensure that valid and correct data is in the system. It ensures that proper ETL processes are applied on source database, during transformation, and on load data in the target database. It further ensures that proper system migration and upgrades are performed.  Data accuracy testing: This type of testing ensures that the data is accurately transformed and loaded as expected. Through this testing, we can identify errors obtained due to truncation of characters, improper mapping of columns, implementation errors in logic, etc.  Data completeness testing: These tests help to verify that all the expected data is loaded in target from the source. It helps to verify the count of rows in driving table matches with the counts in the target table.  Read Full Blog at: https://www.cigniti.com/blog/conquering-the-challenges-of-data-warehouse-etl-testing/