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  • Each type of data requires different testing method, adapted adequately to the most significant V for it:

    • Data ingestion testing — applicable to databases, files, and near real-time records. A high priority needs to be given to variety in case of file-based data, and velocity when dealing with a large influx of records.
    • Data migration testing — this testing type is absent in real-time processing. Therefore, the priority is given to volume.
    • Data integration testing — focuses on identifying inconsistencies. The highlight is on variability, as data coming from different sources needs to be fed into a single repository.
    • Data homogenization testing — here the vast majority of big data is unstructured or semi-structured, so variety imposes the need to create rules.
    • Data standardized testing — volume is the most important feature here, to ensure that all data follows requirements and is compliant with regulations.

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  • Data is extending at large volume at fast rate, such a way that the traditional data base systems (Oracle, MySQL, SQL Server) is running out of processing capacity to handle the massive data.


    Here, Big data analyses concept helps the quality assurance services organization to increase efficiency in operations, and in business decisions, by analyzing the large raw data and extracting out the required information with the help of various tools. There are number of tools in market trend for Big Data Testing. Below are the popular ones:
    1. Apache Hadoop
    2. Mongo DB
    3. Datawrapper

    Huge Challenges are faced in the existing IT system for Big Data Testing. Below are the major ones:


    1. Complex Projects: The technical part in the projects which are related to big data application is complex. Sometimes, each module is involved with different technology. Hardware and software involved in the project are quite high in cost too in terms of support and maintenance.

    2. Skilled Resources: In order to work at big data testing projects, highly technical and skilled resources (developers and testers) are required. It is also a challenge for organizations, to fetch and retain the skilled resources from market.


    3. Logistical Modifications: Huge volume of data require the modifications in data flow management. Therefore, organization prefer the constant data flow instead of flow in batches. Under the requirements, the existing system of technology needs to stay updated.

    4. Accuracy: Resources need to understand the concept of business before getting deepen into the process in order to exact the right information from bundles of data. The correct evaluation for more accurate results from raw information is required.

    5. High-Priced: The costly data computing machinery and highly paid resources (experts, testers and developers) are required to work at big data applications and handling huge expenses is becoming one of the huge challenges in big data testing process.

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