Learners 1000+

  • About Hadoop -

    Hadoop is a Big Data technology that provides various tools to work with Big Data. Big Data means in general data that starts from tera bytes and peta bytes means very huge data like for banking, call center, insurance, google, facebook etc.

  • About Hadoop / Big Data Certification Course -

    This course can be learnt by both Freshers and Experienced who wish to become a Hadoop Big Data Expert. Learn from basics to adv 10+ techniques by Hadoop to work with Big Data.

  • What You Will Learn ?

    Installation on Linux, Scoop, Flume, Map Reduce etc. 10+ Tools

  • Trainer -

    Hadoop Expert

  • Duration -

    1.5 to 2 months (35 hours)

  • Any Pre-requisites -

    None. Any one from IT & non IT can learn.(No programming)

  • Scope & Job Openings -

    RLimited openings and Comes under Hot Skills hence High Pay.

Syllabus

30 hours

Introduction to Hadoop
  • HDFS - Hadoop Distributed File System
  • Comparing Hadoop & SQL
  • Hadoop Architecture
  • Map Reduce & HDFS
  • Using the Hadoop single node image (Clone)

Hadoop Distributed File System (HDFS)
  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • HDFS High-Availability and HDFS Federation
  • Hadoop DFS The Command-Line Interface
  • Basic File System Operations
  • Anatomy of File Read & Write
  • Block Placement Policy and Modes
  • More detailed explanation about Configuration files
  • Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
  • FSCK Utility. (Block report)
  • HDFS Federation

SQOOP
  • Introduction
  • Import Data
  • Export data

FLUME
  • Introduction
  • Flume Commands

Map Reduce
  • Functional Programming Basics
  • Map and Reduce Basics
  • Anatomy of a Map Reduce Job Run
  • Architecture
  • Job Completion, Failures
  • Shuffling and Sorting
  • Splits, Record reader, Partition, Types of partitions & Combiner
  • Speculative Execution
  • Types of Schedulers and Counters
  • YARN
  • Sequential Files and Map Files
  • Hands on “Word Count” in Map/Reduce in standalone and Pseudo distribution Mode

Hive
  • Installation
  • Introduction and Architecture
  • Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
  • Meta store
  • Hive QL
  • Derby Database
  • Working with Tables
  • Primitive data types and complex data types
  • Working with Partitions
  • Hive Bucketed Tables and Sampling
  • External partitioned tables
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY
  • Log Analysis on Hive
  • Hands on Exercises

NoSql Database
  • Introduction
  • Types
  • Brief Of Nosql Database

HBase
  • HBase Installation
  • HBase concepts
  • HBase Data Model and Comparison between RDBMS and NOSQL
  • Master & Region Servers
  • HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture
  • Catalog Tables
  • Block Cache and sharing
  • SPLITS
  • DATA Modeling
  • HBASE Filters
  • Bulk Loading and Coprocessors
  • Real world use case consisting of HDFS, MR and HBASE

SPARK
  • Spark and its purpose
  • Components of Spark
  • Using Spark with Hadoop
  • Resilient Distributed Dataset (RDD)
  • Working with Key-Value Pairs
  • Downloading and installing Spark Standalone
  • Scala Overview

SCALA
  • Fundamental Of Scala
  • Basic Syntax and Operators
  • Programming with Spark

SOLR
  • Fundamental Of Solr
  • Installation of Solr Standalone
  • Basic Solr Concept
  • Demo

Best Practise
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