|Audience and Prerequisites |
Break into Hadoop and Big Data Testing
About The Course
The course is designed to offer best online classes to enable students to master the Hadoop Testing. It helps students in understanding the functional and performance-based testing techniques for detecting, analyzing and rectifying errors in Hadoop framework. Students will be trained in Hadoop software, its architecture, HDFS, MapReduce and several other components like Hive, Pig, Flume, Sqoop, and Oozie. Apart from understanding these techniques, students will be fully trained in various test case scenarios and real-world scenarios.
Audience & Pre-Requisites
The Hadoop Testing training program doesn’t require students to be equipped with any Hadoop developing or testing skills or knowledge. It has been specifically programmed for Big Data and Hadoop Developers, System Administrators, Quality Assurance / Testing / Tech Support professionals.
Take Hadoop Testing Training Course?
While the demand for Hadoop deployment is
already catching pace on the global platform, it is anticipated that the global
Hadoop market will reach to $84.6 billion by the year 2021. Also Hadoop Big
Data is increasingly becoming capable of handling more diverse and complicated
data challenges, owing to which the demand for Hadoop Developers, Hadoop
Architects and Hadoop Testers alike is surging at a high rate. This Hadoop
Testing Training will ensure to embed students with the right set of skills
that will open up new avenues of job opportunities in the Big Data domain.
Of The Course
- Fundamentals of
Hadoop and Hadoop ecosystems
- Learning HDFS
architecture, data replication, data flow, Namenode and Datanode
concepts of MapReduce, Mapper and Reducer functions, Shuffle, Ordering and
- Understanding how to
perform unit testing of Hadoop Mapper on a MapReduce application
- Deploying Pig for Big
Data analysis and Hive for relational data analysis while testing the
- Deep Dive into Hadoop
Testing and the workflow process
formulating and implementing Hadoop test scenarios, test scripts and test
- Using Big Data
testing tools for identifying bugs and rectifying it
- Learning MRUnit
framework for the purpose of testing MapReduce jobs without Hadoop
1. Introduction To Hadoop, Hadoop Ecosystem, HDFS & MapReduce
- What is Big Data, its major factors, introduction to Hadoop and Hadoop Ecosystem.
- Fundamentals of MapReduce and its concepts of Map, Reduce, Shuffle, Concurrency, Ordering, etc.
- Learning the concept of HDFS (Hadoop Distributed File System) and its importance, more of MapReduce concepts such as partioner, execution framework, data types, key pairs and combiner.
- Understanding HDFS architecture, data node, data flow, name node, data replication, parallel copying, Hadoop archives, etc.
- Hands on exercises.
2. In-depth Insights About MapReduce
- Understanding the process of developing MapReduce applications, best practices for writing and developing these applications, joining data sets in MapReduce and debugging MapReduce applications.
3. Introduction To Hive
- Introduction to Hive, data storage and Hive Schema, difference between Hive and traditional databases / Hive and Pig, use cases and understanding Hive based interactions.
- Learning relational data analysis based on Hive through Hive databases and tables, Hive data types, HiveQL syntax, common built in functions, joining data sets, running Hive queries on script, shell and hue.
4. Introduction To Pig
- Introduction to Pig, its features, use cases and understanding interactions with Pig.
- Data analysis with Pig – simple data types, loading data, Pig latin syntax, field definitions, viewing the Schema, data output, filtering and sorting of data and understanding commonly used functions in Pig.
5. Hadoop Stack Integration Testing
- Importance of Hadoop testing, Integration testing, Unit testing, Performance testing, Diagnostics, Benchmark and end to end tests, Functional testing, Nightly QA test, etc.
- Security testing, Scalability Testing, Release certification testing, Commissioning and Decommissioning of Data Nodes Testing, Release testing and Reliability testing,
6. Hadoop Tester’s Roles & Responsibilities
- Understanding the testing requirement, making preparations of the Testing Estimation, Test bed creation, Test Cases, Test Data, Test Execution, Reporting Defect, Defect Retest, Daily Status report delivery, ensuring Test completion, etc.
- ETL testing at every stage (HDFS, HIVE, HBASE) even input is being loaded (logs/files/records etc) using Sqoop / Flume which it includes but is not confined just to data verification.
- Reconciliation, Authentication and User Authorization testing (Groups, Users, Privileges etc), reporting defects to the development team or manager, consolidate all the defects and ensuring timely creation of defect reports, validating new features and identifying issues in Core Hadoop.
7. Testing MapReduce Programs With MR Unit Framework
- Reporting defects to the development team or manager and driving them to closure, consolidate all the defects and creating defect reports.
- Validation of new features and identifying issues in Core Hadoop in addition to creating a testing framework called MR Unit for the purpose of testing of MapReduce programs.
8. Unit Testing
- Automation testing with the help of OOZIE and data validation by making use of query surge tool.
9. Project Work