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Home / Insights / Blog / A Complete Guide to Big Data Testing
December 16, 2020 - by Synoptek
As the amount of data that’s being generated grows with each passing day, the use of big data applications to manage this growing volume of data is also swelling. Analysts expect the global big data market to grow from $138.9 billion in 2020 to $229.4 billion by 2025. Given that traditional computing techniques do not have the ability to process these large data sets, big data applications pave the way for efficient and timely management of data pertaining to customers, processes, and competition.
When done right, data testing can help businesses in real-time fraud detection, competitive analysis, sentiment analysis, and traffic management. But the never-ending need for the creation, storage, retrieval, and analysis of colossal volumes of data means organizations need to rely on precision tools, remarkable frameworks, and brilliant strategies, which brings big data testing into the spotlight.
Big Data testing entails testing of big data applications to ensure they perform as expected. It involves examining and validating the functionality of the Big Data applications that traditional storage systems cannot handle. Since these applications deal with huge amounts of data, testing requires the use of special tools and techniques – that take testing to a whole new level.
Big data applications deal with critical data that gets generated from sensors, mobile devices, cameras, wireless networks, and other IoT devices at an extremely fast pace. Testing these applications means verifying their data processing capabilities – across performance and functions. Ensuring big data applications perform as intended is important because:
Now that you understand why big data testing is important, let’s dig a little deeper into why it is beneficial. There are plenty of benefits that big data testing can bring to an organization, and you might not be aware of them all. Let’s take a look at few benefits:
Unlike testing of normal applications where a significant time is spent on testing the performance and functionality of different software components, a large portion of effort in big data testing goes in validating data:
Big data applications need to process large amounts of data in a short period of time; therefore, they need to be able to function properly – quickly, smoothly, and without error – while keeping up with the required levels of performance and security. Testing big data applications brings with it its own set of challenges:
Big Data testing requires QA engineers to test and verify terabytes of data – right from when the data is fed, processed, and stored. Given the variety, velocity, and volume of data that needs to be processed, Big Data testing demands a sophisticated level of testing skills as data needs to be processed quickly and accurately.
In order to get the best results from Big Data testing initiatives, it is important to use tools like Hadoop to test and verify the ETL process as well as automation techniques to accelerate the speed and accuracy of testing. That being said, here are some tips and best practices:
Advancements in technology have caused massive amounts of data to be generated every second – which needs to be constantly processed for accurate, data-driven business decision-making. Big data applications have a major role to play in processing these large volumes of data, but ensuring these applications carry out accurate processing means they must constantly be tested. Big data testing allows for testers to test the performance and functionality of these applications and ensure data that is fed, processed, generated, and stored meets quality, accuracy, consistency, and compliance requirements.
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