Data Analytics Testing Automation Framework

By January 20, 2021 May 25th, 2021 Blogs
Data Analytics Testing Automation Framework

Testing is a crucial part of SDLC as it ensures the quality deliverables to the end-users. We all know that every organization prefers automation testing over manual testing to identify defects early and perform all the permutation and combination in a shorter timeframe. 

There are various types of automation frameworks available to provide expected benefits. However, the data analytics process involves multiple systems, applications, data warehouses, and tools. For example, an automation framework created for data warehouses might not work for different databases(such as RDBMS, Hive) and web-based UI applications. Similarly, a web-based application testing framework might not work for Analytic dashboard such as Tableau dashboard.

The team prefers to create different automation frameworks for each application, data warehouse, and analytic dashboard. A framework designed for one data warehouse project might be reused partially for other data warehouse projects in some cases. However, still, the team needs to build an automation framework for every project separately.

This approach increases time effort and drains the resources to maintain the different tools and frameworks.

Hence, we need to design an automation framework that is robust, easy to integrate, flexible, adaptable, maintainable, and reusable. Also, it should be a one-stop solution for end-to-end data analytics process testing.

In this blog, I am providing brief details about DA Testing Automation (DATA) framework and a link to the white paper in which I shared a detailed approach.

DA Testing Automation (DATA) framework is a platform that works for different types of data warehouses and BI Analytic dashboards without doing any code change in the core framework. It is simpler to use, easy to adapt, flexible to integrate with all different types of databases, and does end-to-end validation along with documentation and status reporting.

Kindly refer to the white paper for more details on 2-step process of implementing a flexible and straightforward data analytics automation framework, which covers all the platforms and work for all types of projects using the same framework and benefits the entire project team. Also, this approach does more than test execution.


Change is only constant in all domains. Creating a framework that is easy to adapt benefits the entire team. Please email us if you have any questions.

Stay tuned for the next article, which is AI on Data analytics automation framework.

Thilakshana Pandey

Thilakshana Pandey

QA Technical Lead with 12+ years of experience. Expert in providing technical solutions for automation framework and has worked across multiple domains including Commercial Pharmaceutical, Banking, Insurance, Finance etc. Extensive experience in Data QA methodology, Hadoop(HDFS, Map/reducer and Hive)/ETL/ Data warehouse backend testing, data quality testing and BI Intelligence reports testing.