• Big Data possess various properties such as Velocity, Volume, Variety and variability which puts forth many challenges. Testing such large and different variety of data sources which differ in structure needs strategy.

    If Big Data systems not
    efficiently tested then it will affect business, waste efforts and it would be tough to understand the error, cause of the failure.
    Testers must be capable of handling data patterns, layouts, actions and data loads. So it is important to have a clear test strategy for Data Ingestion, Data Processing, Data Storage and Data Migration Testing that enables an easy execution of big data testing and will prevent the wastage of resources in the future.

    Data accuracy, improved Business Decisions, increases revenues, Quality Cost and Improved market targeting can be some of the benefits of following efficient strategies.

    Big data testing is carried by quality assurance services for the successful processing of terabytes of data using commodity clusters and other supportive components. Testing an Application that handles terabytes of data would concentrate on running the application against faulty inputs and varying the volume of the data so find the errors in the early stage.

    Data validation is the most important component to ensure the data is not corrupted or is accurate. The information procured from the source is validated against actual business requirements and will be fed into Hadoop Distributed File System (HDFS).

    Once the data and the source are identical, they will be forwarded to the right location.
    MapReduce validation is counted as the second step of Business Rule validation or Process verification in which a tester checks the business logic, node by node, followed by the validation of the same after running across different and numerous nodes. It helps in ensuring that Data aggregation or segregation rules are correctly executed on the data, Key value pairs are generated appropriately and data is validated after Map Reduce process.

    Output validation is the next important component where validating the data integrity and successful loading of generated data into the downstream system. Data is further audited to make sure it is not distorted, by comparing HDFS file system against target data.
    Even poor architecture strategy would make the whole effort go wasted so architectural testing becomes mandatory. So it becomes an important phase of Big data testing, as poorly designed systems may lead to unprecedented errors and performance degradation.

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