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- 40+ Days Classroom Training
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Testing Big data applications involves, testing of the various data storage tools, framework and processes. Big data testing focuses on performance and functional testing validating the data processing functions of the application framework.
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Industry Relevant Curriculum
Course designed keeping in mind the present and future needs of the Industry. All our training programs are constantly updated and tuned to meet Industry requirements.
Real-Time Case Studies
Real-Time case studies and project are mandatory part of our training programs. All the assignments are designed to help students understand practical applications of the learning’s.
With options to join classroom and online batches, you have a wide array of options in terms of batches, timing and duration allowing to you plan your learning, and achieve your carrier goals.
Continuous feedback and interaction with our student community help us identify concern area and mitigate issue early on ensuring a great learning environment.
State-of-art Lab Infrastructure
Best in class Lab infrastructure to help students work on the latest assignments and project. Practical application of the learning ensures a more satisfied training.
UNIX commands and processes are discussed in order for the students to master the skills needed to manage operating system and application frameworks.
Hadoop testing concepts are mastered with the focus on Test plans, estimates, test cases, test scripts, defect tracking and reporting etc. These topics will help student up skill and be a effective QA testing professional on the Hadoop platform.
Working IT professional from programming, web development and DBA fields
Bigdata Testing Course Curriculum
Duration: 45 Hours
- What is Big Data?
- 3 V’s Concepts
- Diff Problems and Solutions of Big data
- What is Big Data?
- What is Hadoop and History of Hadoop?
- Hadoop Architecture
- Hadoop ecosystem components
- Hadoop Storage: HDFS
- Hadoop Processing: MapReduce Framework
- Hadoop Server Roles: Name Node Secondary Name Node, and Data Node
- Anatomy of File Write and Read.
- Different Components of Hadoop.
- Echo, cat, cp, cut, ls, Cd, touch
- mkdir, rmdir, rm,
- chmod, mv, head, tail
- Date, PS, uniq, diff
- grep,vi editor
- Significance of HDFS in Hadoop
- Features of HDFS
- 5 Daemons of Hadoop
- Name Node and its functionality
- Data Node and its functionality
- Resource Manager and its functionality
- Node Manager and its functionality
- Secondary Name Node and its functionality
- Introduction about Blocks
- Data Replication
- Data storage in Data Nodes
- Replication Configuration
- Fail Over Mechanism
- Replication Factor
- Changing block size for file and Directory
- File Processing in HDFS
- How to process files in Edge node?
- How to load files into HDFS?
- File validation between Source and Target
- Test cases preparation for File processing
- Introduction to Apache PIG
- MapReduce Vs Apache PIG
- SQL Vs Apache PIG
- Physical & Logical Layer
- Different Data types in Apache PIG
- Modes of Execution in Apache PIG
- Local Mode, Map Reduce or Distributed Mode
- Execution Mechanism
- Grunt shell, Script, Embedded
- Transformations in PIG
- How to write a simple PIG Script
- Hands on with PIG lactic script
- Validation on PIG scripts
- Test cases preparation for PIG scripts
- HIVE Introduction
- Hive Architecture and Installation
- Comparison with Traditional Database
- Operators and Functions
- Hive Meta Store and Integration with MySQL
- Hive integration with Hadoop
- SQL vs. HIVE QL
- Partitioning, Dynamic Partitioning and Bucketing
- Hive Tables (Managed Tables and External Tables, Storage Formats, Importing Data, Altering Tables, Dropping Tables)
- Hive data format – Text, ORC, Avro, parquet
- Introduction to SQOOP
- How to connect relational database using SQOOP
- Different Sqoop Commands
- Different flavors of imports, Export, HIVE imports
- Hands on with Examples
- Flume Introduction
- Flume Architecture
- Flume Master, Flume collector and Flume Agent
- Oozie Introduction
- Oozie Architecture
- Oozie Configuration files
- Oozie Job Submission
- Job. Coordinator. properties
- Introduction to Spark
- Spark Vs Map Reduce Processing
- Architecture of Spark
- Spark Shell Introduction
- Creating Spark Context
- File Operations in Spark Shell
- Real time Examples of Spark
- Spark Components
- Spark Core
- Spark SQL
- Spark Streaming
- Spark ML Lib
- Importance of Hadoop Testing
- Responsibilities of Hadoop Tester
- Real time scenarios for Hadoop Tester
- Different approaches for Hadoop Testing
- How to validate data for multiple sources
- Hot to prepare Hadoop Test Plan
- How to prepare Estimates
- How to design Test Cases
- How to design Test Scenarios
- How to prepare Test scripts
- How to prepare Test data for test cases
- How to execute Hadoop Test Cases
- Defect Tracking and Reporting
- Types of defect in Hadoop Testing
- Challenges in Hadoop Testing
- Project Name
- Project Description
- Client Name
- Client Description
- Business Background of Project
- Process followed in the Project
- Tools used in the Project
- Environments in the Project
- Team Size
- Project Architecture
- Testing Life Cycle in the Project
- Query Tracker
- Test Plan preparation
- Test Cases preparation
- Requirement Traceability Matrix
- Review process in Project
- Test case Execution
- Defect Life Cycle in project
- Role of Quality Centre in project
- Regression Testing in Projec:
- Bugs Identified in Project
- Risks Identified in Project
- Challenges identified in Project
- Practical Sessions
- Interview KIT
- Resume Preparation
- Mock Interviews
- Support from Quality Thought to get the job and after got the job also
Quality Thought’s Bigdata Testing Certification Process:
- Quality Thought will provide a certificate to the students who successfully completed their Bigdata Testing training. The certification will be provided within one week of the training completion.
- The certification will be given to the students who have successfully completed their projects and assignments on time.
Frequently asked questions
1. Attending the same session in another batch if student is attending classroom based session.
2. For online sessions, recording of the classes can be accessed by the student at all time to help revisit and listen the sessions missed out.
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