8 Best Strategies for Big Data Automation Testing

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Big Data Testing refers to the big data application testing procedure that verifies that all the functional parts of the application are working at the expected level. The aim here is to ensure that systems running on big data are operating seamlessly, with minimal error involved, without giving up on security or performance.

What is Big Data?

Big Data refers to a compiled body of large datasets that are impossible to process using traditional computing methods. 

Consequently, testing this type of data requires a specific procedure involving the processing of various tools, techniques and frameworks. Big Data processing includes creating, storing, retrieving, and analyzing enormous data for its volume, diversity, and speed.

What is a good Big Data testing strategy?

Big Data testing focuses on verifying the data processing rather than individually testing the software features. The two vital aspects of Big data testing are performance testing and functional testing. 

The testing strategy includes QA professionals verifying that several terabytes are being effectively processed by utilizing commodity clusters and the other specific tools. The processing happens very fast and requires high level of testing skills to be handled. Processing occurs in three ways: Batch, Real-time and Interactive.

Data quality is another vital factor in this type of testing. Checking the quality of the data is compulsory before testing the application. While verifying the quality of data, the things that are looked at include conformity, precision, duplication, consistency, validity, completeness of data, etc.

What are the best strategies to include in your big data automation testing?

Conducting automation testing for big data applications is not a simple process and therefore, must include a holistic approach. The best strategies for this include the different areas where you need to conduct testing, to make the application foolproof.

  1. Functional Testing: Testing an application from front to end benefits the processes of data validation. It enables you to compare the real-time results delivered by the front-end application with the results expected from it. It also allows you to achieve deep insight into the application framework and its components. 
  • Performance Testing: Big data automation enables you to validate the performance level under varied circumstances, like testing an application with a dataset that varies in type and volume. In Big data testing, performance testing has irreplaceable importance since it makes sure the fundamental elements involved in the application are providing enough capabilities to store, process and retrieve large datasets.
  • Data Ingestion Testing: This type of data testing method ensures that all the necessary data components are correctly extracted and stored within the big data system.
  • Data Processing Testing: Big Data testing methodology should also include testing processes where the automation tools prioritize processing the ingested data and ensuring that the business requirements are being met at a satisfactory level through comparing output with input.
  • Data Storage Testing: QA professionals also need to pay attention to verify that the output data is positively loaded into the warehouse. This task is made possible by using big data automation tools by comparing output records with warehouse records.
  • Data Migration Testing: This is the type of testing process that ensures the best data testing practices are being complied with when an application is moved to a different server or any alteration in the technology being used. Data migration testing ensures that the downtime is minimal and there is no essential data loss during the migration process.
  • Sub-system performance: This is a testing strategy where individual components of the whole application are tested to identify bottlenecks and eliminate them.
  • Integration and collaboration of teams: This strategy is less technical and more managerial. To successfully carry out the big data testing process, it is necessary to create a collaborative approach among the QA team, the management, and the development teams. 

This will allow the QA professionals to understand better data extraction from various resources and algorithms before and after processing. A trained and expert QA team would run all the test cases through the automation tools, ensuring that the heterogeneous data is handled efficiently and adequately. 

Big Data automation is an essential part of the whole testing process. Big data automation tools offer test automation as a service. QA professionals need to be well-versed in creating and carrying out test automation to work with big data applications. Following these strategies would ensure that you achieve robust results within a given constraint of time and budget.