Hadoop Big data Online Training

Hadoop Big data Online Training

 Hadoop and  Big Data are fast becoming an emerging trend for effectively storing and managing the humongous data that businesses generate by organizing the storage across a distributed server architecture and creating a retrieval process that greatly optimizes the actual process for increased efficiency. BigData is the name given to enormous amount of unstructured data which can be in different formats and file types such as media, text, logs etc as opposed to convention RDBMS (Relational Database Management System) which consists of defined field types.  Hadoop is a platform for managing such big data and create an efficient, secure, robust and optimized storage and retrieval mechanism that can handle such unstructured data. It’s an open-source initiative started by Doug Cutting in 2005 and has been a product of an extensive development process that has been inspired from and then promoted by some of the world’s biggest  data generators-the search engines. It was developed as no existing solution then could efficiently manage the future projection of the amount of data being stored and indexed.

Features

: Hadoop has been designed as an open-source software framework for BigData storage and processing and is implemented using a series of existing parallely aligned servers. It solves the common data storage challenge of hardware failure by organizing the data into portions stored in multiple locations (Done by HDFS –Hadoop Distributed File System) Most importantly, it’s suited for analyzing unstructured data and can easily uncover trends and analytics from raw data which was previously computationally expensive.

It is built on the programmable model of parallel processing and organizes the constituent servers so that they work in tandem on individual bits of processing requirements such as queries and then the result set is put together from all of them (Map Reduce is the programmable part of the framework that controls such assignment)

It’s robust, fault tolerant, highly optimizes processing of data and is very cost-effective for big data storage and processing.

For your Career

: Hadoop and BigData are fast emerging as cost-effective solutions for the enormous amount of data businesses generate.  They have seen a steadily growing number of implementations and skilled Hadoop professionals are very much in demand as it represents the future of data storage and processing which no industry can afford to ignore or overlook. Our extensively elaborate online Hadoop and BigData course modules help you understand the basics and working of the Hadoop framework along with programming in Map Reduce to give you the best advantage in your career aspiration as a Hadoop and BigData professional.
Our Advantage: Our online Hadoop and BigData training course is designed by industry experts who have an in-depth real time knowledge and exposure so that you get the best of the training curriculum. They are developed with a keen sense of practical utility so that you can easily jump start your career. Choose us for being on the right path for a successful career in Hadoop.

Course Syllabus

HADOOP
( Development + Administration + Analytics )

HADOOP BASICS

  •  The Motivation for Hadoop
  •  Problems with traditional large-scale systems
  •  Data Storage literature survey
  •  Data Processing literature Survey
  •  Network Constraints
  •  Requirements for a new approach

Hadoop: Basic Concepts

  •  What is Hadoop?
  •  The Hadoop Distributed File System
  •  Hadoop Map Reduce Works
  •  Anatomy of a Hadoop Cluster
  •  Hadoop demons
  •  Master Daemons
  •  Name node
  •  Job Tracker
  •  Secondary name node
  •  Slave Daemons
  •  Job tracker
  •  Task tracker

 HDFS(Hadoop Distributed File System)

  •  Blocks and Splits
  •  Input Splits
  •  HDFS Splits
  •  Data Replication
  •  Hadoop Rack Aware
  •  Data high availability
  •  Cluster architecture and block placement

CASE STUDIES
Programming Practices & Performance Tuning

  •  Developing Map Reduce Programs in
  •  Local Mode

Running without HDFS

  •  Pseudo-distributed Mode
  • Running all daemons in a single node
  •  Fully distributed mode
  • Running daemons on dedicated nodes

Hadoop Administration

  •  Setup Hadoop cluster of Apache, Cloudera, Hortonworks, Greenplum
  •  Make a fully distributed Hadoop cluster on a single laptop/desktop
  •  Install and configure Apache Hadoop on a multi node cluster in lab.
  •  Install and configure Cloudera Hadoop distribution in fully distributed mode
  •  Install and configure Horton Works Hadoop distribution in fully distributed mode
  •  Install and configure Green Plum distribution in fully distributed mode
  •  Monitoring the cluster
  •  Getting used to management console of Cloudera and Horton Works
  •  Name Node in Safe mode
  •  Meta Data Backup
  •  Ganglia and Nagios – Cluster monitoring

 CASE STUDIES

Hadoop Development

  •  Writing a Map Reduce Program
  •  Examining a Sample Map Reduce Program
  •  With several examples
  •  Basic API Concepts
  •  The Driver Code
  •  The Mapper
  •  The Reducer
  •  Hadoop’s Streaming API
  •  Performing several Hadoop jobs

The configure and close Methods

  •  Sequence Files
  •  Record Reader
  •  Record Writer
  •  Role of Reporter
  •  Output Collector
  •  Counters
  •  Directly Accessing HDFS
  •  Tool Runner
  •  Using The Distributed Cache
  •  Several Map Reduce jobs (In Detailed)
  •  MOST EFFECTIVE SEARCH USING MAP REDUCE
  •  GENERATING THE RECOMMENDATIONS USING MAP REDUCE
  •  PROCESSING THE LOG FILES USING MAP REDUCE
  •  Identity Mapper
  •  Identity Reducer
  •  Exploring well known problems using Map Reduce applications
  •  Debugging Map Reduce Programs
  •  Testing with MRUnit
  •  Logging
  •  Other Debugging Strategies.
  •  Advanced Map Reduce Programming
  •  The Secondary Sort
  •  Customized Input Formats and Output Formats
  •  Joins in Map Reduce
  •  Monitoring and debugging on a Production Cluster
  •  Counters
  •  Skipping Bad Records
  •  Running in local mode
  •  Tuning for Performance in Map Reduce
  •  Reducing network traffic with combiner
  •  Partitioners
  •  Reducing the amount of input data
  •  Using Compression
  •  Reusing the JVM
  •  Running with speculative execution

Other Performance Aspects
CASE STUDIES
CDH4 Enhancements
Name Node High – Availability
Name Node federation
Fencing
Map Reduce Version – 2

HADOOP ANALYST

  •  Hive
  •  Hive concepts
  •  Hive architecture
  •  Install and configure hive on cluster
  •  Different type of tables in hive
  •  Hive library functions
  •  Buckets
  •  Partitions
  •  Joins in hive
  •  Inner joins
  •  Outer Joins
  •  Hive UDF
  •  PIG
  •  Pig basics
  •  Install and configure PIG on a cluster
  •  PIG Library functions
  •  Pig Vs Hive
  •  Write sample Pig Latin scripts
  •  Modes of running PIG
  •  Running in Grunt shell
  •  Running as Java program
  •  PIG UDFs
  •  Pig Macros
  •  Debugging PIG
  •  IMPALA
  •  Difference between Impala Hive and Pig
  •  How Impala gives good performance
  •  Exclusive features of Impala
  •  Impala Challenges
  •  Use cases of Impala

NOSQL

  •  HBase
  •  HBase concepts
  •  HBase architecture
  •  Region server architecture
  •  File storage architecture
  •  HBase basics
  •  Column access
  •  Scans
  •  HBase use cases
  •  Install and configure HBase on a multi node cluster
  •  Create database, Develop and run sample applications
  •  Access data stored in HBase using clients like Java, Python and Pearl
  •  Map Reduce client to access the HBase data
  •  HBase and Hive Integration
  •  HBase admin tasks
  •  Defining Schema and basic operation.
  •  Cassandra Basics
  •  MongoDB Basics

Other EcoSystem Components

  •  Sqoop
  •  Install and configure Sqoop on cluster
  •  Connecting to RDBMS
  •  Installing Mysql
  •  Import data from Oracle/Mysql to hive
  •  Export data to Oracle/Mysql
  •  Internal mechanism of import/export
  •  Oozie
  •  Oozie architecture
  •  XML file specifications
  •  Install and configuring Oozie and Apache
  •  Specifying Work flow
  •  Action nodes
  •  Control nodes
  •  Oozie job coordinator

Flume, Chukwa, Avro, Scribe, Thrift

  •  Flume and Chukwa concepts
  •  Use cases of Thrift, Avro and scribe
  •  Install and configure flume on cluster
  •  Create a sample application to capture logs from Apache using flume
  •  Hadoop Challenges
  •  Hadoop disaster recovery
  •  Hadoop suitable cases

Course Highlights:
An initial demo to help you understand the course content and features before enrolling
Multiple interactive training sessions with recording facility so that you get the best of a trainer guided session at your own pace and convenience.
24/7 technical support and full access to training resources
Guidance on interview questions, effective resume writing and a 100% certification assurance from the BigData University.
Exposure to real-time scenarios, extensive case studies and unmatched training resources
The advantage of completing a full-fledged online Hadoop and BigData course at your own schedule and flexibility.

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