Enterprises today are constantly looking for ways to leverage the power of Big Data in order to gain a competitive advantage and make informed decisions. Referred to as a huge set of raw data with valuable information, it needs careful design and testing to ensure desirable outcomes with applications. And, with the exponential growth in the number of big data applications in the world, their testing is crucial. Big data testing is referred to as the process of performing data QA. It could be related to database, infrastructure & performance & functional testing.
Through this article, we’ll help you understand the types, tools, and terminologies associated with big data testing.
It can be defined as the process that involves checking and validation of the big data application functionality. We all know that Big Data is a collection of a massive amount of data in terms of volume, variety, and velocity that any traditional computing technique can standalone handle. And, testing of the datasets would involve special testing techniques, remarkable frameworks, brilliant strategy, and a wide range of tools. The goal of this type of testing is to ensure that the system runs smoothly and error-free while retaining efficiency, performance, and security.
Data plays a primary role in testing to provide an expected result based on implemented logic. And, this logic needs to be verified before moving to production based on business requirements and data.
A test environment provides accurate feedback about the quality and behavior of the application under test. While it could be a replica of the production environment, it is one of the most crucial element to be confident about the testing results.
For Big data software testing, the test environment should have;
Big data applications are meant to process different varieties and volumes of data. It is expected that the maximum amount of data to be processed in a short period. Therefore, performance parameter plays a crucial role and help to define SLAs. It might not be the easiest task and hence one should have comprehensive insight into the essential considerations of performance testing and how to go about it.
We can test large scale / Big Data applications to ensure optimum performance for user’s adaptability.
Here are some of the common challenges that big data testers might face while working with the vast data pool.
Data Growth Issues: The quantity of information being stored in large data centers and databases of companies is increasing rapidly. QA professionals must audit this ample data periodically to verify its relevancy and accuracy for the business. But testing it manually is no longer an option.
Solution: Automated test scripts play a vital role in big data testing strategy to detect any flaws in the process. Make sure to assign proficient software test engineers skilled in creating and executing automated tests for big data applications.
Real-time Scalability: Significant rise in workload volume can drastically impact database accessibility, networking, and processing capability.
Solution: Your data testing methodology should include the following testing approaches:
Stretched Deadlines & Costs: If the testing procedure is not properly standardized for; 1) optimization 2) re-utilization of the test case sets, there is a possible delay with test cycle execution exceeding the intended time frame. This may lead to increased overall costs, maintenance issues, and delivery slippages.
Solution: Big Data testers should ensure that the test cycles are accelerated. This is possible by the adoption of proper infrastructure and using proper validation tools and data processing techniques.
QA team can avail the most of big data validation only when the strong tools are in place.
Hadoop Distribution File System (HDFS): Apache’s Hadoop testing tool – HDFS is a distributed file system that maanges large data sets.
HPCC: High-Performance Computing Cluster is a collection of various servers (computer) referred to as nodes. It is an open-source, data-intensive computing platform.
Cloudera: Popularly known as CDH (Cloudera Distribution for Hadoop), is built specifically to offer integrations for Hadoop with more than a dozen other critical open source projects.
Cassandra: A free, open-source NoSQL distributed database designed to handle large amounts of data across various commodity servers.
Big Data Testing Reduced Processing Time by 20% for the Multi-Node Data Architecture solution
Business Need: A Europe-based out-of-home & digital advertising customer wanted to improvise its advertising booking process by minimizing the booking time for its various inventory.
Solution: Our proficient big data team analyzed the legacy system architecture to develop the multi-node Big Data architecture solution. The solution offered RDD (Resilient Data Distribution) to seamlessly manage millions of ad booking requests per hour. Our dedicated big data QA engineers conducted thorough performance testing to improve the booking data processing time and handle millions of booking requests received.
Big Data Testing Boosts Accuracy of Global Sales Calculation & ease Talend data migration
Business Need: The primary objective of the customer was to get precise sales reporting & revenue generation as per their defined sales KPI’s. This generated information was also used to calculate sales commission & compensation for the financial year.
Solution: Rishabh Software’s big data team architected the Talend data integration solution by integrating Talend with the existing systems. The approach was divided into several blocks to simplify functionality and combine the various outputs of the blocks. A team of 15+ big data engineers & QA testers supported the customers to validate & check every aspect of sales commission & compensation to create accurate modules for generating sales reports & help revenue generation as per the sales KPI’s.
Performing comprehensive testing on big data requires extensive & proficient knowledge to achieve robust results and within the defined timeline & budget. With a dedicated team of QA experts, you can experience the best practices for testing big data applications.
As a matured software testing company, we provide an end-to-end methodology that addresses all the Big Data testing requirements. With the right skillset and unmatched techniques, we cater to the latest needs and offer services for new-age big data applications. With pre-defined built test data sets, test cases, and automation frameworks, our team ensures rapid QA process deployment which in turn reduces time to market.
We help improve & increase data coverage to ensure accurate results in every project deliverable.