Summary: These days, reviewing historical trends and monitoring the near real-time operational data has become a key competitive advantage. This is why businesses are primarily focusing on the collection and organization of several kinds of data for strategic decision-making. Here in this article, you will find some practical recommendations and suggestions for testing extract, transform and load ETL applications based on years of experience in data warehousing testing. Every possible effort has been made to help the organizations looking to improve their existing data warehouse.

There are some common goals for testing an ETL application:

● Data completeness-It ensures that all expected data is loaded.

● Data Quality- It promises that the ETL application correctly rejects, substitutes default values, corrects and reports invalid data.

● Data transformation- This is meant for ensuring that all data is correctly transformed according to business rules and design specifications.

● Performance and scalability- This is to ensure that the data loads and queries perform within expected time frames and the technical architecture is scalable.

● Integration testing- It is to ensure that ETL process functions well with other upstream and downstream applications.

● User-acceptance testing- It ensures the solution fulfills the users’ current expectations and also anticipates their future expectations.

● Regression testing- This is to keep the existing functionality intact each time a new release of code is completed.

Data warehouse testing is basically divided into two categories ‘Back-end testing’ and ‘Front-end testing’. The former applies where the source systems data is compared to the end-result data in Loaded area, while the latter refers to where the user checks the data by comparing their MIS with the data that is displayed by the end-user tools.

Data Validation

Data completeness is one of the basic ways for data validation. This is needed to verify that all expected data loads into the data warehouse. This includes the validation of all the records, fields and ensures that the full contents of each field are loaded.

Data Transformation

Validating that the data is transformed correctly based on business rules, can be one of the most complex parts of testing an ETL application with significant transformation logic. Another way of testing is to pick up some sample records and compare them for validating data transformation manually, but this method requires manual testing steps and testers who have a good amount of experience and understand of the ETL logic.

Testing data warehouse is not any easy task; rather it is a very complex process that needs a high degree of experience from the testers. There are some basic and common steps for system analysis and process testing.

First Step- One needs to analyze documents of some sort. These documents can be useful for testing the development process, but the downside is that they lack the necessary details to support test development and execution. Many a times, people require other documents, which are, know as ‘ source-to-target mappings’. Such documents are full of the detailed technical specifications and help people to know where the data is coming from, what should be done to that data and where it should be loaded.

Second Step- After analyzing the documentation source, the second step is to develop strategy and test plans. It is recommended that the incremental testing approach is good when testing a data warehouse. The added advantage of this approach is that it avoids an overwhelming delivery of data and enables early defect detection and simplified debugging. Further, this approach serves you to set up the detailed processes that involve the development and testing process.

This article has been written by a data warehouse expert.