Big Data Testing Training and Certification Course In Pune

Big Data Testing
Duration of course: 40 hrs

Best Blended Syllabus for Big Data Testing Testing Training in Pune by a 100% Placement-Oriented Training Institute

Big Data Testing involves testing the various data storage tools, frameworks, and processes for these big data apps.  Big data testing focuses on the performance and functional testing of the application framework’s data processing components. 

The Hadoop testing course focuses on test plans, estimations, test cases, test scripts, error tracking, reporting, and other Hadoop testing ideas. These lessons will help students improve their skills and become more effective QA testers on the Hadoop platform.

The training focuses on Big data and Hadoop concepts. The course underlines the need for understanding the processes involved in managing large volumes of data, processing applications that work with huge volumes of data, and more.

Instructor-led Big Data Testing Live Online Interactive Training

Date Upcoming Batch Schedule
(Weekdays/Weekends)
Batch Timings Mode of Learning
26th Jun
Saturday-Sunday
26th Jun
Online/Classroom
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Big Data Testing Course​

Best Blended Syllabus for Big Data Testing Training in Pune by a 100% Placement-Oriented Training Institute

  • Introduction to Hadoop and its constituent ecosystem
  • understanding MapReduce and HDFS
  • Big Data
  • factors constituting Big Data
  • Hadoop and Hadoop Ecosystem
  • MapReduce: concepts of MapReduce
  • ordering
  • concurrency
  • shuffle and reducing
  • Hadoop Distributed File System (HDFS) concepts and its importance
  • deep dive into MapReduce
  • execution framework
  • partitioner
  • combiner
  • data types
  • key pairs
  • HDFS deep dive: architecture
  • data replication
  • name node
  • data node
  • dataflow
  • parallel copying with DISTCP and Hadoop archives

Hands-on Exercises:

  • Installing Hadoop in pseudo-distributed mode
  • Understanding important configuration files
  • Their properties and Demon Threads
  • Accessing HDFS from Command Line
  • MapReduce: basic exercises
  • Understanding Hadoop ecosystem
  • Introduction to Sqoop
  • Use cases and installation
  • Introduction to Hive
  • Use cases and installation
  • Introduction to Pig
  • Use cases and installation
  • Introduction to Oozie
  • Use cases and installation
  • Introduction to Flume
  • Use cases and installation and introduction to YarnMini Project:

Importing MySQL data using Sqoop and querying it using Hive

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  • How to develop a MapReduce application
  • Writing unit test
  • The best practices for developing and writing and debugging MapReduce applications

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  • What is Pig
  • Pig’s features
  • Pig use cases
  • interacting with Pig
  • basic data analysis with Pig
  • Pig Latin Syntax
  • loading data
  • simple data types
  • field definitions
  • data output
  • viewing the schema
  • filtering and sorting data and commonly-used functions

Hands-on Exercise: Using Pig for ETL processing

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  • What is Hive
  • Hive schema and data storage
  • comparing Hive to traditional databases
  • Hive vs. Pig
  • Hive use cases
  • interacting with Hive
  • relational data analysis with Hive
  • Hive databases and tables
  • Basic HiveQL Syntax
  • data types
  • joining data sets and common built-in functions

Hands-on Exercise: Running Hive queries on the Shell Scripts and Hue

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  • 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
  • commissioning and decommissioning of data nodes testing
  • reliability testing and release testing

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  • Understanding the requirement
  • Preparation of the testing estimation
  • Test cases
  • Test data
  • Test bed creation
  • Test execution
  • Defect reporting
  • Defect retest
  • Daily status report delivery
  • Test completion
  • ETL testing at every stage (HDFS Hive and 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
  • 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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  • Report defects to the development team or manager and driving them to closure
  • Consolidate all the defects and create defect reports
  • Validating new feature and issues in core Hadoop and responsible for creating a testing framework called MRUnit for testing of MapReduce programs

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  • Automation testing using the Oozie and data validation using the query surge tool

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  • Test plan for HDFS upgrade and test automation and result

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Big Data Testing Training Completion Certificate

To Obtain The Big Data Testing Course Completion Certification,You Have To Fulfill The Following Criteria

  • Complete the Big Data Testing Course Online/Classroom Training
  • You can attend multiple batches of the same trainer & complete the Big Data Testing Course Training
  • Completion of all exercises, assignments & capstone project.
150+

Batches Completed

Industry Oriented Syllabus

Designed By Expert

2000+

Happy Students

Self Assessments

Quizzes, POC

8+

8+ Years Of Experience

Recorded Sessions

1 Year Of Access

Hadoop Testing Training Completion Certificate

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Course Benefits​

  • No Prerequisite
  • Cloud based cluster available for hands on practice

  • Course designed for non-IT as well as IT professionals

  • Flexible batch switch is available

  • Placement assistance in MNC

  • Classroom & Online Training – Can switch from online training to classroom training with nominal fee

  • Working professional as instructor

  • Proof of concept (POC) to demonstrate or self-evaluate the concept or theory taught by the instructor

  • 100% placement calls guaranteed till you get placed

  • Interview Preparation Support

  • Resume Building & Mock Interview

  • Evaluation after each Topic completion

  • Hands-on Experience with Real-Time Projects

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About Course

  • Module 01 - Introduction to Hadoop and Its Ecosystem, MapReduce and HDFS
  • Module 02 - MapReduce
  • Module 03 - Introduction to Pig and Its Features
  • Module 04 - Introduction to Hive
  • Module 05 - Hadoop Stack Integration Testing
  • Module 06 - Roles and Responsibilities of Hadoop Testing
  • Module 07 - Framework Called MRUnit for Testing of MapReduce Programs
  • Module 08 - Unit Testing
  • Module 09 - Test Execution of Hadoop: Customized
  • Module 10 - Test Plan Strategy Test Cases of Hadoop Testing

Big Data Testing involves testing the various data storage tools, frameworks, and processes for these big data apps.  Big data testing focuses on the performance and functional testing of the application framework's data processing components.

►Duration - 30 hrs + 20 hrs Assignments

► Mode - Classroom / Online Interactive

► Batches - Weekdays / Weekend

► Projects - 2 Major Projects

Training Projects​

  • You will test a big data application in the Big Data Testing course to ensure that all of the application’s capabilities work as planned.

  • Big data testing ensures that the big data system functions smoothly and without errors while maintaining performance and security.

FAQ - Big Data Testing

Yes, you can attend demo session before you enroll either we can provide you the recorded lecture so that you can watch it as per your schedule or you can attend live demo lecture either online or offline

 

Yes, we do provide the placement assistance in which how we work on real time projects will be taught,resume preparation and Job openings will also be provided.More than 80% of candidates have changed their profile by getting either promotion or getting new job offers on good package.

Checkout our Telegram Channel for Placement Assistance (Open in Mobile Browser): https://t.me/technogeekssolutions

 

If you miss classes, you can get recording sessions of the lectures.

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