Big Data Testing – How to Improve Productivity and Increase Test Coverage

Big Data Testing – How to Improve Productivity and Increase Test Coverage

Big data refers to any collection of large data sets, both structured and unstructured, that is difficult to process using traditional data processing applications or software tools. With big data becoming integral to ensuring the quality of enterprise applications, businesses need to ensure it is collected, curated, stored, analyzed, retrieved, and managed properly. In general, big data is characterized by properties such as volume, velocity, variety, veracity, variability, and value. These properties are explained as under:

  • Volume: The sheer size of data
  • Velocity: The ability of a system to create and transmit the data
  • Variety: The various types of data
  • Veracity: The quality and integrity of data
  • Variability: The frequency of changes in data flow
  • Value: The benefit of data to the business

The importance of big data can be gauged from the fact that it offers enterprises critical insights about business trends, end-users, markets, and competitors. By enabling analytics, big data can help business enterprises make informed decisions. For instance, eCommerce companies can look into the search and sales patterns to understand the type of products customers look for and buy. Armed with this knowledge, they can work towards streamlining the placement and display of the specific product items in the catalog or shelves of the store.

Why test big data?

Big data testing is a process where a big data application is tested to determine whether its functionalities are performing as expected. Also, a big data environment may comprise a large volume and variety of data, complex algorithms, unstructured data layers, and complicated logic. And to ensure the accuracy, quality, and integrity of data across channels and sources, testing big data applications has become critical. The other benefits of big data testing are as follows:

  • Enables data-driven decision making in real-time.
  • Assures the accuracy of data based on which proper analysis can be done to identify and mitigate issues plaguing the business.
  • Optimizes business data to allow the delivery of best results.
  • Minimizes losses and increases revenues.
  • Enables businesses to achieve seamless integration, reduced quality costs, and time to market.

How to Improve Productivity and Increase Test Coverage.

It is only by following the best practices for big data automation testing that attributes such as productivity and test coverage can be improved. The components of big data testing are as follows:

Data validation testing: This type of testing ensures the accuracy and completeness of data, and verifies whether the collected data is corrupted or not. This is done in the Hadoop Distributed File System, or HDFS, where the data is partitioned and checked thoroughly. It is generally carried out on one or more data fields wherein it verifies whether the individual characters provided by the user as input are similar to the expected characters of data types as defined in a data storage mechanism. Data validation testing consists of four steps:

  • Preparing a detailed plan about the expectations, the way to resolve issues, defining the iterations, and obtaining the required data for testing
  • Validating the database by ensuring the applicable data is available and following the steps to determine the size of the data, number of records, and compare the source and target data.
  • Validating data format to ensure that end-users and the target system understand the data.
  • The sampling of data by testing a small amount to check if it meets the business requirements. It is only when the small sample is found to be correct that a large set of data is proceeded with for testing. This helps to increase the quality and accuracy of data and decrease the error rate

Range and constraint validation: This testing process examine the user input for consistency by conducting a test to evaluate the sequence of characters or the maximum or minimum range.

Code validation: This includes tests for validating the data type and verifying whether the user input is consistent with the external rules, validity constraints, or requirements of the business organization. The other validity constraints may involve cross-referencing the data with a directory information service or a look-up table.

Structured validation: This type of testing allows you to combine any number of different data validation steps and complex processing. The complex processing here may include testing the conditional constraints for a set of processes within a system or for an entire data object.

Source: datafloq.com

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