Hadoop Training in Chennai Big Data Training

LearnCloud TECHNOLOGIES is a Hadoop Learning Center. LearnCloud TECHNOLOGIES Offers its students a range of technical/functional Hadoop courses in accordance with Indusrty standards. Hadoop introduces a new, student centric approach to training: a range of interactive web-based courses designed specifically to deliver effective knowledge transfer whenever it is needed. These courses are targeted to working professionals, experienced consultants, and new graduates
LearnCloud TECHNOLOGIES also help provides Placement assistance post training.

Hadoop Course Content

Introduction to Big Data and Hadoop

  • What is Big Data?
  • What are the challenges for processing big data?
  • What technologies support big data?
  • Distributed systems
  • What is Hadoop?
  • Why Hadoop?
  • history of Hadoop
  • Use Cases of Hadoop
  • Hadoop eco System
  • HDFS
  • Map Reduce
  • Statistics

Understanding the Cluster

  • Typical workflow
  • Writing files to HDFS
  • Reading files from HDFS
  • Rack Awareness
  • 5 daemons

Best Practices for Cluster Setup

  • Best Practices
  • How to choose the right hadoop distribution
  • How to choose right hardware

Cluster Setup

  • Install Pseudo cluster
  • Install Multi node cluster
  • configuration
  • Setup cluster on Cloud – EC2
  • Tools
  • security
  • Benchmarking the cluster

Routine Admin procedures

  • Metadata & Data Backups
  • Filesystem check (fsck)
  • File system Balancer
  • Commissioning and decommissioning nodes
  • Upgrading
  • Using DFSAdmin

Monitoring the Cluster

  • Using the Web user interfaces
  • Hadoop Log files
  • Setting the log levels
  • Monitoring with Nagios

Install ,Configure and use

  • PIG
  • HIVE
  • HBASE
  • Flume and Sqoop
  • Zookeeper

Hadoop developer

Introduction to Big Data and Hadoop

  • What is Big Data?
  • What are the challenges for processing big data?
  • What technologies support big data?
  • Distribution systems.
  • What is Hadoop?
  • Why Hadoop?
  • History of Hadoop
  • Use Cases of Hadoop
  • Hadoop eco System
  • HDFS
  • Map Reduce
  • Statistics

Understanding the Cluster

  • Typical workflow
  • Writing files to HDFS
  • Reading files from HDFS
  • Rack Awareness
  • 5 daemons

Developing the Map Reduce Application

  • Configuring development environment – Eclipse
  • Writing Unit Test
  • Running locally
  • Running on Cluster
  • MapReduce workflows

How MapReduce works

  • Anatomy of a MapReduce job run
  • Failures
  • Job Scheduling
  • Shuffle and Sort
  • Task Execution

MapReduce Types and Formats

  • MapReduce Types
  • Input Formats – Input splits & records, text input, binary input, multiple inputs & database input
  • Output Formats – text Output, binary output, multiple outputs, lazy output and database output

MapReduce Features

  • Counters
  • Sorting
  • Joins – Map Side and Reduce Side
  • Side Data Distribution
  • MapReduce Combiner
  • MapReduce Partitioner
  • MapReduce Distributed Cache

Hive and PIG

  • Fundamentals
  • When to Use PIG and HIVE
  • Concepts

HBASE

  • CAP Theorem
  • Hbase Architecture and concepts
  • programming

Get in touch,

Call: +91 9789968765 / 044 – 42645495 / 044 – 45122065

Visit us:
# 67, Gandhi Nagar 1st main Rd,
Adyar, Chennai 600020

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