Today, businesses are exponentially generating a variety of data in large volumes and need timely processing to derive relevant analytics from it. This involves multi-layered, complex, big data platform implementations to cater for various business processing needs such as batch processing, near real-time processing, big-query interactions, ML processing, etc.
Such highly distributed and integrated platform solutions incur additional testing challenges around added data dimensions (volume, variety, velocity, veracity, value and variability), complex technologies, and infrastructure provisions.
Defining pragmatic testing approach for such complexities requires a greater understanding of big data platforms, underneath configuration dependencies, data sampling and test data management approaches, statistical validation techniques, and purpose specific backend test tooling.
Our big data test service group has facilitated many enterprises to validate such implementations and continues to enhance testing of big data technologies by focused R&D. We facilitate clients with an array of test frameworks and accelerators to expedite and achieve required quality on optimized big data platform.