Big data testing: A medium of web based application testing

Big Data testing

Big data is a widely used medium in software testing. Since its dawn it has been challenged by many global organizations to provide a competitive advantage. The IT companies now are moving very quickly in the application testing and hence to catch the rate the proper testing and preparing user friendly applications is required.

Today Bigdata is described as velocity, variety and volume namely the three Vs. It describes as that the large amount of data in various structure should be processed in a high speed.

The different methodologies can be classified into 3 main parts:

1.Uploading the data:

In this primitive stage, the data is received from different sources namely social media, blogs, case studies, networks etc., and are uploaded to the HDFS (Hadoop distributed file systems) in such a way that they are split into different files. Now the process is executed in the following way:

  • Verifying the prime data if it is extracted from the original content and checking if there is any data corruption.
  • Check if the data files were uploaded correctly to the Hadoop distributed file systems.
  • Validate the partition of files and replicate them to different data segments
  • Identify the best complete set of data that needs to be verified for a step wise validation. We can use tools such as Talend, Informatica and Datameer.
  • Engineering of Map reduction operations

Here we need to process the initial data using a map reduce operation to obtain the required result. This phenomenon is a data processing method for considering large volumes of data into important results. The perfect language used for the examination of data is Hive.

  • Validate the required business concept on standalone unit and then on the set of units.
  • Examine the map reduce process to make sure that the ‘key value’ pair is the correct output.
  • Validate the aggregation and consolidation of data after performing the ‘reduce’ operation.
  • Compare the output with initial files to ensure that the output file was generated correctly and the obtained format meets the desired requirements.

The final step consists of unloading the data that was generated in the second step and loading into the downstream process, which is useful sometimes as a resource for data to create reports or transactional analysis system for future processing.

3.Rolling out the output results from HDFS:

In this step we need to conduct the inspection of data completely to make sure that the data has been loaded into the respective system and thus was not distracted and then validation of the reports which include all the required data, and all indicators are referred to firm measures and displayed correctly.

To know the performance metrics and to detect bugs you can use Hadoop performance monitoring tool.

Summary

If an organization need to give an enhanced competition in the market, then the companies should invest in Bigdata needs and developing the automation solutions for validation off Bigdata.

Big data holds a better promising performance for today’s business, if we are investing in the correct test strategies and follow the enhanced practices, it will improve the testing and delivery of products making a customer obsessed company.

To see how we can help you with your Bigdata needs talk to us today: sales@motivitylabs.com

About us:

Motivity Labs is a U.S(Texas) based mobile, cloud and Bigdata insights solution provider with a global presence. We look forward to create applications using next generation technology.

Motivity Labs was incorporated in 2010 and has quickly risen to 138 position on the Inc. 5000 by successfully executing projects including development and testing efforts for some of the largest software companies in the world along with many start-up companies.