Bahaa Al Zubaidi refers big data to a massive collection of data. It is enormous in variety, volume and velocity. Any standalone traditional computing technique is capable of managing Big Data.

When it comes to Big Data testing, the process incorporates reviewing and validating the functionality of Big Data. To test a dataset, it is important to deploy outstanding frameworks, reliable technique, varied tools and an innovative strategy.

The aim of Big Data testing is to ensure that the systems perform efficiently and securely, with minimal room for errors.

Big Data Testing Benefits

Through Big Data Testing, your company systems can reap benefits such as –

  • Cost-effective storage
  • Deficit reduction
  • Revenue increment
  • Presentation of precise data (at the correct time)
  • Business strategy aid
  • Efficient decision-making boost

Forms of Big Data Testing

Bahaa Al Zubaidi lists out the 4 ways to approach Big Data Testing –

  • Architecture Testing

This tests the efficiency of data processing to meet the requirement of the firm. Improper architecture could result in the loss of valuable data.

  • Database Testing

This tests whether the data received from different databases is valid. Database testing ensures your firm receives the correct data.

  • Performance Testing

As the name suggest, this type of testing is crucial to ensure your data applications process and load at good speeds. A lag in performance could have a ripple effect on the rest of your operations.

  • Functional Testing

This involves checking the analytical and operational components at the Application Programming Interface (API) standard.

Components of Big Data Testing

An efficient Big Data testing system has the following components –

Test Data

The aim of testing data is to gather a predictable outcome from a logically implemented input. It ensures –

  • Application processing precision.
  • Functional application of logic with the use of the latest data generating tools.
  • Storing data and hosting applications, keeping the production environment in mind.

Test Environment

This delivers precise feedback on the behavior and quality of the application being tested. The test environment must contain –

  • Large storage space for processing massive data.
  • Data and distributed notes clusters.
  • Efficient memory and CPU utilization to maintain high performance.

Performance Testing

Performance refers to processing maximum data in the shortest amount of time. A good Big data application must be able to compute and analyze different volumes and types of data.

Tools for Big Data Testing

Some of the best tools available for Big Data testing are –

  • HDFS (Hadoop Distribution File System)
  • High-Performance Computing Clusters (HPCC)
  • Cloudera Distribution for Hadoop
  • Cassandra (a NoSQL distributed open-source database)

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