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Big Data Testing:

Big Data Testing is the process of testing applications which contains Big Data. Here, Big Data in the sense collection of large data sets that is too hard to handle by traditional data computing applications. Datasets involves a wide range of tools, techniques and frameworks to process the application testing. Performance Testing and Functional Testing are key elements of Big Data Testing.

In the process of this testing, testers need to verify the processing of terabytes of data using supportive components. It involves checking various characteristics such as accuracy, conformity, consistency, data completeness, duplication, validity etc.,

Big Data Testing is divided into three steps

Step 1: Data Staging Validation

  • In the first step, a large amount of data should be validated from a wide range of sources like RDBMS, Social Media, Weblogs etc., to ensure that data is correctly pulled into the system
  • It compares the data pushed into the Hadoop with the source data to ensure that they both are matching
  • It helps to verify the data which is extracted and pushed into correct HDFS location

Step 2: MapReduce Validation

In the second step, QA engineers or testers need to verify the business logic validation among every node and need to validate them after running over multiple nodes. Here MapReduce validation works based on Map procedure which performs filtering and sorting whereas Reduce procedure performs a summary operation

  • It ensures that application process works properly
  • Implementing the data based on data aggregation rules
  • Make sure validating the data after the process of MapReduce

Step 3: Output Validation Phase

The third step in big data testing is the output validation phase. In this final step the output files are created and moved to an Data Warehouse system or to any other system depending on requirements

  • It helps to check whether the transformation rules applied correctly or not
  • It validates the data integrity and data load into the system
  • Helps to ensure the data free from corruption by comparing the HDFS system data with target data

Difference between Traditional Database Testing and Big Data Testing:

Properties Traditional Database Testing Big Data Testing
Data Here tester able to work with structured data Here tester able to work with structured and unstructured data
Approach In this type, testing approach is well defined and time-tested Here testing approach requires focused R&D efforts
Infrastructure As the system size is limited there is no need of any special test environment It must requires a special test environment as it contains large datasets usually in terms of Tera Bytes
Validation Tools In this types, for system validation testers use macros or automation tools It uses different types of tools based on the big data cluster

Different Types of Big Data Testing Tools:

Big Data Cluster Big Data Testing Tools
MapReduce Cascading, Flume, Hadoop, Hive, Kafka, MapR, Oozie, Pig, S4
NoSQL Cassandra, CouchDB, HBase, MongoDB, Redis, ZooKeeper
Processing BigSheets, Datameer,  Mechanical Turk, R, Yahoo! Pipes
Servers EC2, Elastic, Google App Engine, Heroku
Storage Hadoop Distributed File System (HDFS), S3

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