BIG DATA – HADOOP

hadoop training in chennai

hadoop training in chennai

Our Hadoop course includes basic to advanced level and our Hadoop course is designed to get the placement in good MNC companies in chennai as quickly as once you complete the Hadoop certification training course.

In a few ways, Big data Hadoop is  like a fine wine: It shows signs of improvement with age as unpleasant edges (or flavor profiles) are smoothed out, and the individuals who hold up to devour it will presumably have a superior affair. Our Hadoop training chennai review is given as positive by industry experts. Hadoop Administration preparing for System Administrators is intended for specialized operations work force whose employment is to introduce and keep up generation Hadoop groups in true. Most of the peoples looking for Hadoop admin training in Chennai and this is the right place for the person to learn Big data Hadoop.

We have designed our Hadoop course content and syllabus based on students requirement to achieve everyone’s career goal. In our Hadoop training program, you will learn Hadoop Course Content, Use case walkthrough, Sqoop for Developers,Sqoop Introduction,Sqoop Architecture, Hbase Introduction, Hbase Schema Design, Hbase Operations, cluster management, MapReduce for Developers, Introduction, Hadoop in the Enterprise, Architecture, Hadoop CLI, MapReduce Programming, MapReduce Formats, Hadoop File Formats, MapReduce Design Considerations, MapReduce Algorithms, MapReduce Features, Use Case A (Long Exercise), MapReduce Testing, Hadoop Ecosystem,MapReduce Performance Tuning, Development Best Practice and Debugging, Apache Hadoop for Administrators, Hadoop Fundamentals and Architecture, Hadoop Ecosystems Overview, Hardware and Software requirements, Deploy Hadoop ecosystem services, Enable Security – Configure Users, Groups, Secure HDFS, MapReduce, HBase and Hive, Manage and Monitor your cluster, Command Line Interface, Troubleshooting your cluster, Introduction to Big Data and Hadoop, Hadoop Overview, Apache Hive & Pig for Developers, Overview of Hadoop, Hive Introduction, Hive Architecture – Building Blocks, Hive Usecase implementation – (Exercise), Advance Features, Pig Introduction, Pig Latin Programming, Use Cases (working exercise), Advanced Features, UDFs, Best Practices and common pitfalls, Classification, Evaluation (Hands-on exercise), Clustering, Recommendation Systems.

Target Audience:
Students with a background of BE / B.Tech in CSE /IT/ ECE /EEE/E&I/IC/ Mechatronics/Msc Electronics and any other relevant streams.

Software Training is suitable for:

Software Training in Bigdata is suitable for engineering students who are from computers or electronics,mechanical domain can find an opportunity in Software Systems Development Industries. There is a growing demand for Software Engineers , Data Scientists in the Industry.

Course Goal:
Students will become an Industry-ready Software engineer by completing Software Training / Diploma in Advanced Software Technology certified course.

SHORT TERM CERTIFIED COURSE IN BIGDATA USING APACHE HADOOP

Duration :  1 Month(3hrs/day)     Total : 60 Hours
Working Days : Monday to Saturday ( 10.00 AM to 3.00 PM ) 

Module 1: INTRODUCTION TO BIGDATA

  • Introduction and relevance
  • What is Bigdata ?
  • Characteristics of BigData
  • Big Data analtics in various industries like Telecom,E-Commerce,Finance etc
  • Problems with Traditional Large-Scale Systems
  • BigData Challenges
Module 2: HADOOP (BIG DATA) ECOSYSTEM
  • Motivation for Hadoop
  • Different types of projects by Apache
  • Role of projects in the Hadoop Ecosystem
  • Advantages of Hadoop
  • Limitations and Solutions of existing Data Analytics Architecture
  • Comparison of traditional data management systems with Big Data management systems
  • Hadoop Ecosystem & Hadoop 2.x core components
Module 3: BUILDING BLOCKS
  • Quick tour of Java (Hadoop is Written in Java to be discussion )
  • Quick tour of Linux commands ( Basic Commands to traverse the Linux OS)
  • Hadoop Distributed File System Commands\
  • Quick hands on experience of SQL.
  • Introduction to Cloudera VM and usage instructions
Module 4: HADOOP CLUSTER ARCHITECTURE – CONFIGURATION FILES
  • Hadoop Master-Slave Architecture
  • The Hadoop Distributed File System – data storage
  • Explain different types of cluster setups (Fully distributed/Pseudo etc.)
  • Hadoop Cluster set up – Installation
  • Hadoop 2.x Cluster Architecture
Module 5: HADOOP CORE COMPONENTS
  • HDFS
  • MAP REDUCE
  • YARN
  • Difference between 1X and 2X.
Module 6: HDFS & MAP REDUCE OVERVIEW
  • Get the data into Hadoop from local machine (Data Loading Techniques)
  • MapReduce Overview (Traditional way Vs. MapReduce way)
  • Concept of Mapper & Reducer
  • Understanding MapReduce program skeleton
  • Running MapReduce job in Command line/Eclipse
  • Develop MapReduce Program in JAVA
  • Develop MapReduce Program with the streaming API
  • Test and debug a MapReduce program in the design time
  • How Partitioners and Reducers Work Together\
  • Writing Customer Partitioners Data Input and Output

Module 7: DATA INTEGRATION USING SQOOP AND FLUME

  • Sqoop Introduction
  • Sqoop Architecture
  • Integrating Hadoop into an existing Enterprise
  • Loading Data from an RDBMS into HDFS by Using Sqoop
  • Incremental Import
  • Free Form Query
  • Export Opertions
  • Flume Introduction
  • Flume Components
  • Managing Real-Time Data Using Flume

Module 8: DATA ANALYSIS USING PIG

  • Introduction to Hadoop Data Analysis Tools
  • Introduction to PIG – MapReduce Vs Pig, Pig Use Cases
  • Pig Latin Program & Execution
  • Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF
  • Use Pig to automate the design and implementation of MapReduce applications
Module 9: DATA ANALYSIS USING HIVE

  • Introduction to Hive – Hive Vs. PIG – Hive Use Cases
  • Discuss the Hive data storage principle
  • Explain the File formats and Records formats supported by the Hive environment
  • Perform operations with data in Hive
  • Architecture of Hive
  • Hive QL: Joining Tables, Dynamic Partitioning, Custom MapReduce Scripts
  • Hive Script, Hive UDF

Module 10: NOSQL DATABASE – HBASE

  • Introduction to NoSQL Databases and Hbase
  • HBase v/s RDBMS
  • HBase Architecture
  • HBase Components
  • Hbase Commands

Module 11: FINAL PROJECT

  • Real World Use Case Scenarios
  • Understand the implementation of Hadoop in Real World and its benefits.
  • Final project including integration various key components
  • Follow-up session: Tips and tricks for projects

CERTIFIED COURSE IN ADVANCED BIGDATA USING APACHE HADOOP

Duration :  3 Month ( 3hrs/day )     Total : 180 Hours
Working Days : Monday to Saturday ( 10.00 AM to 3.00 PM  )

Module 1: JAVA

  • Overview of Java
  • Classes and Objects
  • Inheritance, Aggregation,Polymorphism
  • Command line argument
  • Abstract class and Interfaces
  • String Handling
  • Exception Handling,Multithreading
  • Collection Framework
Module 2: INTRODUCTION TO BIGDATA
  • Introduction and relevance
  • What is Bigdata ?
  • Characteristics of BigData
  • Big Data analytics in various industries like Telecom,E-commerce,Finance etc
  • Problems with Traditional Large-Scale Systems
  • BigData Challenges
Module 3: HADOOP (BIG DATA) ECOSYSTEM
  • Motivation for Hadoop
  • Different types of projects by Apache
  • Role of projects in the Hadoop Ecosystem
  • Advantages of Hadoop
  • Limitations and Solutions of existing Data Analytics Architecture
  • Comparison of traditional data management systems with Big Data management systems
  • Hadoop Ecosystem & Hadoop 2.x core components
Module 4: BUILDING BLOCKS
  • Quick tour of Java (Hadoop is Written in Java to be discussion )
  • Quick tour of Linux commands ( Basic Commands to traverse the Linux OS)
  • Hadoop Distributed File System Commands
  • Quick hands on experience of SQL.
  • Introduction to Cloudera VM and usage instructions
Module 5: HADOOP CLUSTER ARCHITECTURE – CONFIGURATION FILES
  • Hadoop Master-Slave Architecture
  • The Hadoop Distributed File System – data storage
  • Explain different types of cluster setups (Fully distributed/Pseudo etc.)
  • Hadoop Cluster set up – Installation
  • Hadoop 2.x Cluster Architecture
Module 6: HADOOP CORE COMPONENTS
  • Get the data into Hadoop from local machine (Data Loading Techniques)
  • MapReduce Overview (Traditional way Vs. MapReduce way)
  • Concept of Mapper & Reducer
  • Understanding MapReduce program skeleton
  • Running MapReduce job in Command line/Eclipse
  • Develop MapReduce Program in JAVA
  • Develop MapReduce Program with the streaming API
  • Test and debug a MapReduce program in the design time
  • How Partitioners and Reducers Work Together
  • Writing Customer Partitioners Data Input and Output
Module 7: HDFS & MAP REDUCE OVERVIEW
  • Sqoop Introduction
  • Sqoop Architecture
  • Integrating Hadoop into an existing Enterprise
  • Loading Data from an RDBMS into HDFS by Using Sqoop
  • Incremental Import
  • Free Form Query
  • Export Opertions
  • Flume Introduction
  • Flume Components
  • Managing Real-Time Data Using Flume
Module 8: DATA INTEGRATION USING SQOOP AND FLUME
  • Introduction to Hadoop Data Analysis Tools
  • Introduction to PIG – MapReduce Vs Pig, Pig Use Cases
  • Pig Latin Program & Execution
  • Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF
  • Use Pig to automate the design and implementation of MapReduce applications
Module 9: DATA ANALYSIS USING PIG
  • Introduction to Hive – Hive Vs. PIG – Hive Use Cases
  • Discuss the Hive data storage principle
  • Explain the File formats and Records formats supported by the Hive environment
  • Perform operations with data in Hive
  • Architecture of Hive
  • Hive QL: Joining Tables, Dynamic Partitioning, Custom MapReduce Scripts
  • Hive Script, Hive UDF
Module 10: DATA ANALYSIS USING HIVE
  • Jobs,Stages and Tasks
  • Partitions and Shuffles
  • Broad Cost variables and Accumulators
  • Job Perfomence
Module 11: NOSQL DATABASE – HBASE
  • Introduction to NoSQL Databases and Hbase
  • Hbase v/s RDBMS
  • HBase Architecture
  • HBase Components
  • Hbase Commands
Module 12: FINAL PROJECT
  • Real World Use Case Scenarios
  • Understand the implementation of Hadoop in Real World and its benefits.
  • Final project including integration various key components
  • Follow-up session: Tips and tricks for projects