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Hadoop Training

Hadoop Training in Chennai

Hadoop training

Hadoop Training in Chennai

Learn how to use Hadoop from beginner level to advanced techniques which is taught by experienced working professionals. With our Hadoop Training in Chennai you’ll learn concepts in expert level with practical manner.

What is Hadoop?

Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a parallel distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation. Hadoop makes it possible to run applications on systems with thousands of nodes involving thousands of terabytes of data which is not feasible with traditional systems.

Upcoming Batches

Weekdays Batch

Starts Duration Days Time (IST)
04th Dec 4 Weeks Mon – Fri 09:00AM – 10:30AM
11th Dec 4 Weeks Mon – Fri 07:00PM – 08:30PM
18th Dec 4 Weeks Mon – Fri 07:30AM – 09:00AM
25th Dec 4 Weeks Mon – Fri 09:00AM – 10:30AM
02nd Jan 4 Weeks Mon – Fri 07:00PM – 08:30PM
Schedule does not suit you, contact us @9962528293 | Want to take one-on-one training, contact us now!

Weekend Batch

Starts Duration Days Time (IST)
02nd Dec 6 Weeks Sat & Sun 02:00PM – 04:30PM
09th Dec 6 Weeks Sat & Sun 10:30AM – 01:00PM
16th Dec 6 Weeks Sat & Sun 09:00AM – 11:00AM
23rd Dec 6 Weeks Sat & Sun 11:00AM – 01:30PM
30th Dec 6 Weeks Sat & Sun 02:00PM – 04:30PM
Schedule does not suit you, contact us @9962528293 | Want to take one-on-one training, contact us now!

Why Hadoop?

Today we live in a DATA world. Anything and everything that we do in the internet is becoming a source of business information for the organizations across the globe. The world has seen an exponential growth of data in the last decade or so and more so since last 3 years. Hence, the industry has started to look out for the ways to handle the data and get some business value out of it through data analytics. One such jail-break is “HADOOP”.

Yes, Hadoop is here to stay and lead the industry in helping the business with numerous ways to store, retrieve and analyze data.

What we do at Besant Technologies for Hadoop?

Today we have been presented with an excellent opportunity to align ourselves with what the industry needs. All that industry needs is a Data Scientist / Analyst and that’s exactly what we at BESANT TECHNOLOGIES aim to do. We train aspiring data scientist / data analyst with best faculties available in the market whom have real time hands on experience in Hadoop area and who do project along with industry leading Cloudera Engineers. By giving the best Hadoop Training in Chennai we are getting opportunities to work with Cloudera Inc indirectly.

Whom Hadoop is suitable for?

Hadoop is suitable for all IT professionals who look forward to become Data Scientist / Data Analyst in future and become industry experts on the same. This course can be pursued by Java as well as non- Java background professionals (including Mainframe, DWH etc.)

Whom do we train?

We train professionals across all experience 0 -15 years and we have separate modules like Developer module, Project manager module etc.. We customize the syllabus covered according to the role requirements in the industry.

Job Opportunity for Hadoop

Hadoop is the buzzword in the market right now and there is tremendous amount of job opportunity waiting to be grabbed. In the current state market is short of good Big data professionals. Hence BIG Data means BIG Opportunities with Big bucks. Come grab them with both hands!!!

Certifications and Job opportunity Support

We help the trainees with guidance for Cloudera Developer Certification and also provide guidance to get placed in Hadoop jobs in the industry.

Big Data Hadoop provides wonderful opportunities for the aspiring IT professional both fresher and experienced. This course is suitable for both Java and non- Java professionals like Data-warehousing professionals, Mainframe professionals etc.

All topics will be covered with in-depth concepts and corresponding practical programs.

Hadoop Training Syllabus

Module 1 : Fundamental of Core Java

Module 2 : Fundamental of Basic SQL

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

  • 1. BigData Inroduction
  • 2. Hadoop Introduction
  • 3. HDFS Introduction
  • 4. MapReduce Introduction

Module 4 : Deep Dive in HDFS

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

Module 4A : HDFS File Operation Lifecycle (Supplementary)

  • 1. File Read Cycel from HDFS
  • – DistributedFileSystem
  • – FSDataInputStream
  • 2. Failure or Error Handling When File Reading Fails
  • 3. File Write Cycle from HDFS
    • – FSDataOutputStream
  • 4. Failure or Error Handling while File write fails
  • Module 5 : Understanding MapReduce :

    • 1. JobTracker and TaskTracker
    • 2. Topology Hadoop cluster
    • 3. Example of MapReduce
      • Map Function
      • Reduce Function
    • 4. Java Implementation of MapReduce
    • 5. DataFlow of MapReduce
    • 6. Use of Combiner

      Module 6 : MapReduce Internals -1 (In Detail)

    • 1. How MapReduce Works
    • 2. Anatomy of MapReduce Job (MR-1)
    • 3. Submission & Initialization of MapReduce Job (What Happen ?)
    • 4. Assigning & Execution of Tasks
    • 5. Monitoring & Progress of MapReduce Job
    • 6. 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 Stratgies
    • – In MapFiles
  • Sorting Algorithm
    • – Total Sort (Globally Sorted Data)
    • – InputSampler
    • – Secondary Sort

    Module 8 : Advanced MapReduce Algorithm -2

    • 1. MapReduce Joining
    • – Reduce Side Join
    • – MapSide Join
    • – Semi Join
  • 2. MapReduce Job Chaining
    • – MapReduce Sequence Chaining
    • – MapReduce Complex Chaining

    Module 9 : Apache Pig

    • 1. What is Pig ?
    • 2. Introduction to Pig Data Flow Engine
    • 3. Pig and MapReduce in Detail
    • 4. When should Pig Used ?
    • 5. Pig and Hadoop Cluster
    • 6. Pig Interpreter and MapReduce
    • 7. Pig Relations and Data Types
    • 8. PigLatin Example in Detail
    • 9. Debugging and Generating Example in Apache Pig

    Module 9A : Apache Pig Coding

    • 1. Working with Grunt shell
    • 2. Create word count application
    • 3. Execute word count application
    • 4. Accessing HDFS from grunt shell

    Module 9B : Apache Pig Complex Datatypes

    • 1. Underst7and Map, Tuple and Bag
    • 2. Create Outer Bag and Inner Bag
    • 3. Defining Pig Schema

    Module 9C : Apache Pig Data loading

    • 1. Understand Load statement
    • 2. Loading csv file
    • 3. Loading csv file with schema
    • 4. Loading Tab separated file
    • 5. Storing back data to HDFS.

    Module 9D :Apache Pig Statements

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

    Module 9E : Apache Pig Complex Datatype practice

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

    Module 10 : Fundamental of Apache Hive Part-1

    • 1. What is Hive ?
    • 2. Architecture of Hive
    • 3. Hive Services
    • 4. Hive Clients
    • 5. how Hive Differs from Traditional RDBMS
    • 6. Introduction to HiveQL
    • 7. Data Types and File Formats in Hive
    • 8. File Encoding
    • 9. Common problems while working with Hive

    Module 10A : Apache Hive

    • 1. HiveQL
    • 2. Managed and External Tables
    • 3. Understand Storage Formats
    • 4. Querying Data
    • – Sorting and Aggregation
    • – MapReduce In Query
    • – Joins, SubQueries and Views
  • 5. Writing User Defined Functions (UDFs)
  • 6. Data types and schemas
  • 7. Querying Data
  • 8. HiveODBC
  • 9. User-Defined Functions
  • Module 11 :Step by Step Process creating and Configuring eclipse for writing MapReduce
    Code

    Module 12 : NOSQL Introduction and Implementation

    • 1. What is NoSQL ?
    • 2. NoSQL Characerstics or Common Traits
    • 3. Catgories of NoSQL DataBases
    • – Key-Value Database
    • – Document DataBase
    • – Column Family DataBase
    • – Graph DataBase
  • 4. Aggregate Orientation : Perfect fit for NoSQl
  • 5. NOSQL Implementation
  • 6. Key-Value Database Example and Use
  • 7. Document DataBase Example and Use
  • 8. Column Family DataBase Example and Use
  • 9. What is Polyglot persistence ?
  • Module 12A : HBase Introduction

    • 1. Fundamentals of HBase
    • 2. Usage Scenerio of HBase
    • 3. Use of HBase in Search Engine
    • 4. HBase DataModel
    • – Table and Row
    • – Column Family and Column Qualifier
    • – Cell and its Versioning
    • – Regions and Region Server
  • 5. HBase Designing Tables
  • 6. HBase Data Coordinates
  • 7. Versions and HBase Operation
    • – Get/Scan
    • – Put
    • – Delete

    Module 13 : Apache Sqoop (SQL To Hadoop)

    • 1. Sqoop Tutorial
    • 2. How does Sqoop Work
    • 3. Sqoop JDBCDriver and Connectors
    • 4. Sqoop Importing Data
    • 5. 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

    • 1. Data Acquisition : Apache Flume Introduction
    • 2. Apache Flume Components
    • 3. POSIX and HDFS File Write
    • 4. Flume Events
    • 5. Interceptors, Channel Selectors, Sink Processor

    Module 14A : Advanced Apache Flume

    • 1. Sample Twiteer Feed Configuration
    • 2. Flume Channel
    • – Memory Channel
    • – File Channel
  • 3. Sinks and Sink Processors
  • 4. Sources
  • 5. Channel Selectors
  • 6. Interceptors
  • Module 15 : Apache Spark : Introduction to Apache Spark

    • 1. Introduction to Apache Spark
    • 2. Features of Apache Spark
    • 3. Apache Spark Stack
    • 4. Introduction to RDD’s
    • 5. RDD’s Transformation
    • 6. What is Good and Bad In MapReduce
    • 7. Why to use Apache Spark

    Module 16 : Load data in HDFS using the HDFS commands

    Module 17 : Importing Data from RDBMS to HDFS

    • 1. Without Specifying Directory
    • 2. With target Directory
    • 3. With warehouse directory

    Module 18 : Sqoop Import & Export Module

    • 1. Importing Subset of data from RDBMS
    • 2. Chnaging the delimiter during Import
    • 3. Encoding Null values
    • 4. Importing Entire schema or all tables

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