Big data testing: A medium of web based application testing Aleksandar August 18, 2016

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. IT companies now are moving very quickly in application testing and hence to catch the rate proper testing and preparing user-friendly applications is required.

Today Bigdata is described as velocity, variety, and volume namely the three Vs. It describes that a large amount of data in various structures should be processed at 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 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 a 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 it into the downstream process, which is useful sometimes as a resource for data to create reports or a 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 the Hadoop performance monitoring tool.

Summary
If an organization needs to give enhanced competition in the market, then the companies should invest in Bigdata needs and develop automation solutions for the validation of Bigdata.

Big data holds a better promising performance for today’s business, if we are investing in the correct test strategies and following 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:marketing@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 creating applications using next-generation technology.

Motivity Labs was incorporated in 2010 and has quickly risen to 138 positions 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.

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