Conquering the challenges of Data Warehouse ETL Testing

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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 welldefined 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. 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. 

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 programmingAs 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 

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 datacentric testing, which involves comparing large volumes of data across heterogeneous data sources. This datacentric testing helps in achieving highquality 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. 

Data integrity testing: This type of testing helps to check for counts of ‘unspecified’ or ‘unmatched’ rows with respect to foreign keys and to compare the percentage of foreign key matches by running queries on data. 

Business testing: This testing ensures that the data fulfills the critical business requirements. The data is evaluated against the business rules stated. This test also checks whether data has been moved, copied, or loaded completely and accurately. 

Data transformation testing: It is done in many cases as it cannot be achieved by writing on source SQL query and comparing the output to the target. 

Production validation testing: This type of testing is done on data that is being moved to production. In order to achieve effective business decisions, data in the production systems should have valid and correct order. 

With the constantly evolving needs of businesses and similar changes in the source systems, ETL testing effectively drives continuous change in the data warehouse schema and the data being loaded. Hence, it is necessary that development and testing processes are clearly defined. ETL and Data Warehouse testing should be followed by impact-analysis and should focus on strong alignment between development, operations, and the business teams. Let us see below some of the ETL testing tools that can be used. 

ETL testing tools 

ETL testing can be performed either manually, or by using tools like InformaticaQuerySurge etc. However, much of the ETL testing is done by SQL scripting or eyeballing of data on spreadsheets. The usage of automated testing tools ensures that only trusted data will be delivered in your production system. The types of testing that can be achieved with ETL tools include unit, functional, regression, continuous integration, operational monitoring and more.  

Coming to the benefits, you can reduce testing time by about 50% to 90% while also reducing resource utilization. ETL testing lowers business risks and instils confidence in the data. The two documents that will be used by an ETL tester are ETL mapping sheets and the DB Schema of source and target. 

To sum up 

Extracting data from disparate sources, transforming the obtained data into a legible format, and uploading it into the data warehouse is as huge a task as it is critical for a business’ competitiveness. Having the right data and analysis at the disposal can make a world of difference to how businesses make important decisions that impact their growth. The process of extraction, transformation, and loading has several challenges, which make it a tricky activity. To ensure that ETL serves the intended purpose, it needs to happen seamlessly. This is where ETL testing or data warehouse testing comes into the picture. 

Cigniti’s ETL/Data warehoustesting teams have produced effective results for our clients, including reduced test cycle, zero production defects, and faster go-to-market. Our comprehensive big data testing services are complemented by 100+ expert & certified test professionals, custom test and process templates, in-house tools, framework, accelerators, and CoEConnect with us today.