Big Data QA

Curriculum of Big Data QA

Why Big Data QA?

Are you working as a Manual QA or Automation QA and you are bored of your repetitive and monotonous work ? Do you feel stagnant in your current profile and feel you have not learnt much to actually tell about yourself in 15 minutes ?

Do you feel that your job can be done even by a fresher or college student or they can overthrow you any-day in your career with respect to the skills needed for QA work? Do you see people getting fired from QA profiles as automation has taken over and developers themselves do the work of QA as well these days ?

We can help you transition to Big Data QA profiles where your salary will increase exponentially and you will have a bright future from there. Some of our mentors themselves have transitioned from QA background to Big Data QA and they will be able to help you out as well to effectively transition to Big Data World.

If you don’t like programming or fear that you wont be able to cope up , relax. We will help you thoroughly with complete guidance and help you move out of a crowd of 10 lac QAs to 20k Big Data QAs.


  • SDLC – Software Development life cycle
  • STLC – Software Test Life Cycle
  • Test Planning, Test Case Designing & Execution
  • Importance of testing in SDLC & various kinds of testing
  • Black Box, White Box & Exploratory Testing
  • Look & Feel testing
  • Usability testing
  • Performance testing – load , stress, volume testing
  • Integration & System testing
  • Entry exit criteria
  • Defect Reporting – defect life cycle
  • Concept of Continuous Integration
  • Test Driven Development (TDD) approach
  • Black box testing strategy in Big Data
  • White box testing strategy in Big Data
  • Creating test suite /test harness
  • How to do performance testing on big data
  • Continuous Integration
  • Data mock up
  • Analyzing missing data
  • Identifying corner cases
  • Testing techniques
  • Boundary Value analysis
  • Equivalence class partitioning
  • How Automation Testing in Big Data works
  • How is Data – Then and Now, What is Big Data?, Big Data – 3Vs, Data Sources
  • New model of data storage
  • Technology stack of Big Data
  • Overview of Apache Software foundation
  • Data Science Validations Vs QA validations
  • Hadoop Distributed File System (HDFS)
  • Design of HDFS
  • HDFS data flow
  • Learning compression technique in hadoop
  • Testing flat files using HDFS commands
  • Performance Testing in big data applications
  • Hive query lifecycle on Hadoop, Basic operations in Hive, Create table and load data, Altering & dropping tables
  • QA validation through Hive scripts, Test case coverage through hive queries
  • Basic SQL to transform data, SQL statements on DDL, DML, group by, having clause, joins and SQL queries to perform testing
  • Definition, Real life examples, Building principles,Mapper-reducer functions, MapReduce Example,Demo
  • Demo to build a MR application – Word count and testing it and How to test a map reduce program
  • Introduction to various databases on which QA validations are done and Importance of Nosql Databases
  • Introduction, Architecture, Data type, ETL commands
  • QA validation through Pig scripts, Test case coverage through Pig queries
  • Key concepts of ETL testing methods and Log file analysis and testing
  • Write JUNit Test Case, Write MR UNit Test Case
  • Basic Unix Operations to work on files,Data transformation through Unix and Perform quick testing through unix on HDFS
  • Basic Python scripting, Unit testing in Python
  • Basic shell scripting, Testing in shell script, Automating python, hive , pig scripts through shell scripts
  • Basic R programming, Unit testing in R, Writing R functions for testing, Boundary value analysis through R and Hive Vs R comparison for testing with example
  • Import and export of structured data on Hadoop, Introduction to Apache Sqoop, Sqoop commands and Injecting unstructured data into Hadoop
  • Flume architecture and Flume component
  • Oozie co-ordinator, Oozie workflows and Oozie scheduler hands-on
  • Why Hadoop testing is important, Unit testing, Integration testing
  • Performance testing, Diagnostics, Nightly QA test, Benchmark and end to end tests, Functional testing, Release certification testing, Security testing, Scalability Testing and Reliability testing
  • Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Test completion.
  • ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation.
  • User Authorization and Authentication testing (Groups, Users, Privileges etc)
  • Report defects to the development team or manager and driving them to closure.
  • Consolidate all the defects and create defect reports and Validating new feature and issues in Core Hadoop.

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