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Hadoop

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Hadoop

May 27, 2018 - May 27, 2022

$250
Hadaoop training in hyderabad

Professional Hadoop Training

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, technqiues and frameworks.

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Curriculum
Our Courses prepared by Industry experts and Hadoop Consultants
Lab Support
Our technical team is ready to assist you both online and inpremise
Scenarios
Our training covers best practices and real-time scenarios
Certification Assistance
We help you to prepare for certification exam

Trainers

Mr Sudheer Varma
Hadoop Faculty

7+ Years of Experience as Hadoop.

Trained from Hadoop Institute US.

Worked for Microsoft, TCS.

What do our Students say ?

Full marks for the Spoorthy support team for providing excellent support services. Since Hadoop was new to me and I used to have many queries but the support team was very qualified.
Hadoop Expert, Pune
Sumakanth
I am completely satisfied with the Spoorthy big data hadoop training. The trainer came with over a decade of industry experience.
IT Consultant
Rakshitha Jain
I wanted to learn big data since it had a huge scope. My career changed positively upon completion of Spoorthy Big Data Hadoop Online Training. Go with Spoorthy for a Bright Career !!! Thanks.
IT Consultant , Delhi
Vishal Miyank
I am fully satisfied with your services. Thank you for your guidance. I want to make a reference about the quiz at the end of the course that was perfectly designed to gauge my proficiency. Thanks a lot
Consultant , IB* Noida
Sai Kumar

Course Content

Introduction

1. Big Data Introduction

What is Big Data

  •  Data Analytics
  •  Bigdata Challenges
  •  Technologies supported by big data

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:
    •  Apache Hadoop
    •  Cloudera Hadoop
    •  Horton Networks Hadoop
    •  MapR Hadoop

Hadoop Daemon Processes

  •  Name Node
  •  DataNode
  •  Secondary Name Node/High Availability
  •  Job Tracker/Resource Manager
  •  Task Tracker/Node Manager

HDFS (Hadoop Distributed File System)

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

Hadoop Installation Modes and HDFS

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

Hadoop Developer Tasks

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

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

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

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. 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

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
  •  FIFO schedulers
  •  FAIR Schedulers
  •  CAPACITY Schedulers

7. YARN

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

Hadoop Ecosystems

 

 

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
  •  Accessing Hive from PIG

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
  •  Hive UDF
  •  Hive UDAF
  •  Hive UDTF
  •  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

3. HBASE

  •  HBASE concepts
  •  ZOOKEEPER concepts
  •  HBASE and Region server architecture
  •  File storage architecture
  •  NoSQL vs SQL
  •  Defining Schema and basic operations
  •  DDLs
  •  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
  •  OOZIE
  •  OOZIE concepts
  •  OOZIE architecture
  •  Workflow engine
  •  Job coordinator
  •  Install and configuring OOZIE
  •  HPDL and XML for creating Workflows
  •  Nodes in OOZIE
  •  Action nodes
  •  Control nodes
  •  Accessing OOZIE jobs through CLI, and web console
  •  Develop sample workflows in OOZIE on various Hadoop distributions
  •  Run HDFS file operations
  •  Run MapReduce programs
  •  Run PIG scripts
  •  Run HIVE jobs
  •  Run SQOOP Imports/Exports

4. FLUME

  •  FLUME Concepts
  •  FLUME architecture
  •  Installation and configurations
  •  Executing FLUME jobs
  •  IMPALA
  •  What is Impala
  •  How Impala Works
  •  Imapla Vs Hive
  •  Impala’s shortcomings
  •  Impala Hands on
  •  ZOOKEEPER
  •  ZOOKEEPER Concepts
  •  Zookeeper as a service
  •  Zookeeper in production

Integrations

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

Spark

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

Hadoop Administrative Tasks:

1. 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

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
  • Mock interviews for each individual will be conducted on need basis
  • SQL basics on need basi
  • Core Java concepts on need basis
  • Resume preparation and guidance
  • Interview questions

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Details

Start:
May 27, 2018
End:
May 27, 2022
Cost:
$250
Event Category:
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Website:
spoorthysolutions.com

Organizer

Spoorthy Software Solutions
Phone:
+91 040-40208208
Email:
info@spoorthysolutions.com
Website:
https://spoorthysolutions.com

Venue

Spoorthy Software Solutions
#302,15/A, 16/A, 17/A, Nandhini Enclave, Addagutta Society, HMT Hill Road, Hyderabad, India
Hyderabad, Telangana 500090 India
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Phone:
+91 040-40208208
Website:
https://spoorthysolutions.com/

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