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Hadoop Online Training in Hyderabad, India

Capital Info Solutions offering the Hadoop Online Training in Hyderabad, India under the guidance of real time working experts.

1. Introduction

1.1 Big Data Introduction

  • What is Big Data
  • Data Analytics
  • Bigdata Challenges
  • Technologies supported by big data

1.2 Hadoop Introduction

  • What is Hadoop?
  • History of Hadoop
  • Basic Concepts
  • Future of Hadoop
  • The Hadoop Distributed File System
  • Anatomy of a Hadoop Cluster
  • Breakthroughs of Hadoop
  • Hadoop Distributions:
  1. Apache Hadoop
  2. Cloudera Hadoop
  3. Horton Networks Hadoop
  4. MapR Hadoop

2. Hadoop Daemon Processes

  • Name Node
  • DataNode
  • Secondary Name Node
  • Job Tracker
  • Task Tracker

3. HDFS (Hadoop Distributed File System)

  • Blocks and Input Splits
  • Data Replication
  • Hadoop Rack Awareness
  • Cluster Architecture and Block Placement
  • Accessing HDFS
  1. JAVA Approach
  2. CLI Approach

4. Hadoop Installation Modes and HDFS

  • Local Mode
  • Pseudo-distributed Mode
  • Fully distributed mode
  • Pseudo Mode installation and configurations
  • HDFS basic file operations

5. Hadoop Developer Tasks

5.1 Writing a MapReduce Program

  • Basic API Concepts
  • The Driver Class
  • The Mapper Class
  • The Reducer Class
  • The Combiner Class
  • The Partitioner Class
  • Examining a Sample MapReduce Program with several examples
  • Hadoop’s Streaming API
  • Examining a Sample MapReduce Program with several examples
  • Running your MapReduce program on Hadoop 1.0
  • Running your MapReduce Program on Hadoop 2.0

5.2 Performing several hadoop jobs

  • Sequence Files
  • Record Reader
  • Record Writer
  • Role of Reporter
  • Output Collector
  • Processing XML files
  • Counters
  • Directly Accessing HDFS
  • ToolRunner
  • Using The Distributed Cache

5.3 Advanced MapReduce Programming

  • A Recap of the MapReduce Flow
  • The Secondary Sort
  • Customized Input Formats and Output Formats
  • Map-Side Joins
  • Reduce-Side Joins

5.4 Practical Development Tips and Techniques

  • Strategies for Debugging MapReduce Code
  • Testing MapReduce Code Locally by Using LocalJobRunner
  • Testing with MRUnit
  • Writing and Viewing Log Files
  • Retrieving Job Information with Counters
  • Reusing Objects

5.5 Data Input and Output

  • Creating Custom Writable and Writable-Comparable Implementations
  • Saving Binary Data Using SequenceFile and Avro Data Files
  • Issues to Consider When Using File Compression

5.6 Tuning for Performance in MapReduce

  • Reducing network traffic with Combiner, Partitioner classes
  • Reducing the amount of input data using compression
  • Reusing the JVM
  • Running with speculative execution
  • Input Formatters
  • Output Formatters
  • Schedulers
  1. FIFO schedulers
  2. FAIR Schedulers
  3. CAPACITY Schedulers

5.7 YARN

  • What is YARN
  • How YARN Works
  • Advantages of YARN

6. Hadoop Ecosystems

6.1 PIG

  • PIG concepts
  • Install and configure PIG on a cluster
  • PIG Vs MapReduce and SQL
  • PIG Vs HIVE
  • Write sample PIG Latin scripts
  • Modes of running PIG
  • Programming in Eclipse
  • Running as Java program
  • PIG UDFs
  • PIG Macros

6.2 HIVE

  • Hive concepts
  • Hive architecture
  • Installing and configuring HIVE
  • Managed tables and external tables
  • Partitioned tables
  • Bucketed tables
  • Complex data types
  • Joins in HIVE
  • Multiple ways of inserting data in HIVE tables
  • CTAS, views, alter tables
  • User defined functions in HIVE
  1. Hive UDF
  2. Hive UDAF
  3. Hive UDTF

6.3 SQOOP

  • SQOOP concepts
  • SQOOP architecture
  • Install and configure SQOOP
  • Connecting to RDBMS
  • Internal mechanism of import/export
  • Import data from Oracle/Mysql to HIVE
  • Export data to Oracle/Mysql
  • Other SQOOP commands

6.4 HBASE

  • HBASE concepts
  • ZOOKEEPER concepts
  • HBASE and Region server architecture
  • File storage architecture
  • NoSQL vs SQL
  • Defining Schema and basic operations
  1. DDLs
  2. DMLs
  • HBASE use cases
  • Access data stored in HBASE using clients like CLI, and Java
  • Map Reduce client to access the HBASE data
  • HBASE admin tasks

6.5 OOZIE

  • OOZIE concepts
  • OOZIE architecture
  1. Workflow engine
  2. Job coordinator
  • Install and configuring OOZIE
  • HPDL and XML for creating Workflows
  • Nodes in OOZIE
  1. Action nodes
  2. Control nodes
  • Accessing OOZIE jobs through CLI, and web console
  • Develop sample workflows in OOZIE on various Hadoop distributions
  1. Run HDFS file operations
  2. Run MapReduce programs
  3. Run PIG scripts
  4. Run HIVE jobs
  5. Run SQOOP Imports/Exports

6.6 FLUME

  • FLUME Concepts
  • FLUME architecture
  • Installation and configurations
  • Executing FLUME jobs

6.7 IMPALA

  • What is Impala
  • How Impala Works
  • Imapla Vs Hive
  • Impala’s shortcomings
  • Impala Hands on

6.8 ZOOKEEPER

  • ZOOKEEPER Concepts
  • Zookeeper as a service
  • Zookeeper in production

7. Integrations

  • Mapreduce and HIVE integration
  • Mapreduce and HBASE integration
  • Java and HIVE integration
  • HIVE – HBASE Integration
  • SAS – HADOOP

8. Spark

  • Introduction to Scala
  • Functional Programming in Scala
  • Working with RDDs – Spark

9. Hadoop

  • Administrative Tasks:

  • Setup Hadoop cluster: Apache, Cloudera and VMware
  • Install and configure Apache Hadoop on a multi node cluster
  • Install and configure Cloudera Hadoop distribution in fully distributed mode
  • Install and configure different ecosystems
  • Basic Administrative tasks

10. Course Deliverables

  • Workshop style coaching
  • Interactive approach
  • Course material
  • Hands on practice exercises for each topic
  • Quiz at the end of each major topic
  • Tips and techniques on Cloudera Certification Examination
  • Linux concepts and basic commands
  • On Demand Services
  1. Mock interviews for each individual will be conducted on need basis
  2. SQL basics on need basis
  3. Core Java concepts on need basis
  4. Resume preparation and guidance
  5. Interview questions
    • Duration:60 Hrs
    • Class Duration:1 Hr 30 Mins

    Faculty Name: Nagaraju
    Experience : 10 Yrs in IT 5+ Yrs in Hadoop​

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