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Hadoop Training for Project Manager in Chennai

Big Data is breaking new ground in many businesses right now, which is driving the need for a new class of project manager. To get ready, project managers may have to gain new skills, refresh, and learn how to adapt to the needs of Big Data projects. This course is targeted towards Project managers and above who leads the delivery at IT Organisations. All topics will be covered at the concepts level without much insistence on Practical programming.

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Syllabus of Hadoop Training for Project Manager in Chennai

Module 1 : Fundamental of Core Java

Module 2 : Fundamental of Basic SQL

Module 3: Introduction to BigData, Hadoop (HDFS and MapReduce) :

  • BigData Inroduction
  • Hadoop Introduction
  • HDFS Introduction
  • MapReduce Introduction

Module 4 : Deep Dive in HDFS

  • HDFS Design
  • Fundamental of HDFS (Blocks, NameNode, DataNode, Secondary Name Node)
  • Read/Write from HDFS
  • HDFS Federation and High Availability
  • Parallel Copying using DistCp
  • HDFS Command Line Interface

Module 4A : HDFS File Operation Lifecycle (Supplementary)

  • File Read Cycle from HDFS
    • DistributedFileSystem
    • FSDataInputStream
  • Failure or Error Handling When File Reading Fails
  • File Write Cycle from HDFS
    • FSDataOutputStream
  • Failure or Error Handling while File write fails

Module 5: Understanding MapReduce

  • JobTracker and TaskTracker
  • Topology Hadoop cluster
  • Example of MapReduce
    • Map Function
    • Reduce Function
  • Java Implementation of MapReduce
  • DataFlow of MapReduce
  • Use of Combiner

Module 6: MapReduce Internals -1 (In Detail)

  • How MapReduce Works
  • Anatomy of MapReduce Job (MR-1)
  • Submission & Initialization of MapReduce Job (What Happen ?)
  • Assigning & Execution of Tasks
  • Monitoring & Progress of MapReduce Job
  • Completion of Job

Module 7: Advanced MapReduce Algorithm

  • File-Based Data Structure
  • Sequence File
  • MapFile
  • Default Sorting In MapReduce
  • Data Filtering (Map-only jobs)
  • Partial Sorting
  • Data Lookup Strategies
  • In MapFiles
  • Sorting Algorithm
    • Total Sort (Globally Sorted Data)
    • InputSampler
    • Secondary Sort

Module 8 : Advanced MapReduce Algorithm -2

  • MapReduce Joining
  • Reduce Side Join
  • MapSide Join
  • Semi Join
  • MapReduce Job Chaining
  • MapReduce Sequence Chaining
  • MapReduce Complex Chaining

Module 9 : Apache Pig

  • What is Pig ?
  • Introduction to Pig Data Flow Engine
  • Pig and MapReduce in Detail
  • When should Pig Used ?
  • Pig and Hadoop Cluster
  • Pig Interpreter and MapReduce
  • Pig Relations and Data Types
  • PigLatin Example in Detail
  • Debugging and Generating Example in Apache Pig

Module 9A : Apache Pig Coding

  • Working with Grunt shell
  • Create word count application
  • Execute word count application
  • Accessing HDFS from grunt shell

Module 9B : Apache Pig Complex Datatypes

  • Underst7and Map, Tuple and Bag
  • Create the Outer Bag and Inner Bag
  • Defining Pig Schema

Module 9C : Apache Pig Data loading

  • Understand Load statement
  • Loading CSV file
  • Loading CSV file with schema
  • Loading Tab-separated file
  • Storing back data to HDFS.

Module 9D :Apache Pig Statements

  • ForEach statement
  • Example 1 : Data projecting and foreach statement
  • Example 2 : Projection using schema
  • Example 3 : Another way of selecting columns using two dots.

Module 9E : Apache Pig Complex Datatype practice

  • Example 1 : Loading Complex Datatypes
  • Example 2 : Loading compressed files
  • Example 3: Store relation as compressed files
  • Example 4 : Nested FOREACH statements to the solved the same problem.

Module 10: Fundamental of Apache Hive Part-1

  • What is Hive?
  • Architecture of Hive
  • Hive Services
  • Hive Clients
  • How Hive Differs from Traditional RDBMS
  • Introduction to HiveQL
  • Data Types and File Formats in Hive
  • File Encoding
  • Common problems while working with Hive

Module 10A: Apache Hive

  • HiveQL
  • Managed and External Tables
  • Understand Storage Formats
  • Querying Data
  • Sorting and Aggregation
  • MapReduce In Query
  • Joins, SubQueries, and Views
  • Writing User Defined Functions (UDFs)
  • Data types and schemas
  • Querying Data
  • HiveODBC
  • User-Defined Functions

Module 11:  Step by Step Process creating and Configuring eclipse for writing MapReduce
Code

Module 12: NOSQL Introduction and Implementation

  • What is NoSQL?
  • NoSQL Characerstics or Common Traits
  • Catgories of NoSQL DataBases
  • Key-Value Database
  • Document DataBase
  • Column Family DataBase
  • Graph DataBase
  • Aggregate Orientation: Perfect fit for NoSQl
  • NOSQL Implementation
  • Key-Value Database Example and Use
  • Document DataBase Example and Use
  • Column Family DataBase Example and Use
  • What is Polyglot persistence?

Module 12A: HBase Introduction

  • Fundamentals of HBase
  • Usage Scenario of HBase
  • Use of HBase in Search Engine
  • HBase DataModel
    • Table and Row
    • Column Family and Column Qualifier
    • Cell and its Versioning
    • Regions and Region Server
  •  HBase Designing Tables
  • HBase Data Coordinates
  • Versions and HBase Operation
    • Get/Scan
    • Put
    • Delete

Module 13 : Apache Sqoop (SQL To Hadoop)

  • Sqoop Tutorial
  • How does Sqoop Work
  • Sqoop JDBCDriver and Connectors
  • Sqoop Importing Data
  • Various Options to Import Data
    • Table Import
    • Binary Data Import
    • SpeedUp the Import
    • Filtering Import
    • Full DataBase Import Introduction to Sqoop

Module 14: Apache Flume

  • Data Acquisition: Apache Flume Introduction
  • Apache Flume Components
  • POSIX and HDFS File Write
  • Flume Events
  • Interceptors, Channel Selectors, Sink Processor

Module 14A: Advanced Apache Flume

  • Sample Twitter Feed Configuration
  • Flume Channel
    • Memory Channel
    • File Channel
    • Sinks and Sink Processors
    • Sources
    • Channel Selectors
    • Interceptors

Module 15: Apache Spark: Introduction to Apache Spark

  • Introduction to Apache Spark
  • Features of Apache Spark
  • Apache Spark Stack
  • Introduction to RDD’s
  • RDD’s Transformation
  • What is Good and Bad In MapReduce
  • Why use Apache Spark

Module 16: Load data in HDFS using the HDFS commands

Module 17: Importing Data from RDBMS to HDFS

  • Without Specifying Directory
  • With target Directory
  • With the warehouse directory

Module 18: Sqoop Import & Export Module

  • Importing Subset of data from RDBMS
  • Changing the delimiter during Import
  • Encoding Null values
  • Importing Entire schema or all tables

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Upcoming Batch Schedule for Hadoop Training for Project Manager in Chennai

Besant Technologies provides flexible timings to all our students. Here is the Hadoop Training for Project Managers Training Classes in Chennai Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

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Hadoop Training (Project Managers) in Chennai

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Hadoop Training (Project Managers) in Chennai

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Our certification at Besant Technologies is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.

Hadoop Training (Project Managers) in Chennai

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Key Features of Hadoop Training for Project Manager in Chennai

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