BIG DATA HADOOP TRAINING

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BIG DATA HADOOP TRAINING

DURATION

40hrs

AVAILABLE BATCHES

WEEKDAYS (7.00AM TO 8.30AM) & WEEKENDS (10AM TO 1PM)

MODE OF TRAINING AVAILABLE

ONLINE INSTRUCTOR LED

CLASSROOM TRAINING (MARATHAHALLI, BANGALORE)

SELF PACED VIDEOS

No. 249, 3

rd

Floor, Chirag Towers,

Above Born Babies, Outer Ring Road, Marathahalli,

Bangalore – 560037

Landmark – Next to Kalamandir Showroom

Email

Contact Number

Website

[email protected] +91-7411642061 www.tekclasses.in [email protected] +91-8970005497

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Wanna become a Hadoop and Big Data Expert? Then build your impressive career with Tek Classes. Get the idea of Real-world training from our experts. Check out the detailed information about this course.

COURSE OVERVIEW What is Hadoop?

Hadoop is an open-source framework that allows to store and process big data and helps to organize the massive data. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. We have designed Hadoop Training course as per Cloud Era Certification syllabus. This course is designed to provide knowledge and skills to become a successful Hadoop Developer. The candidate will get in-depth knowledge of core concepts along with implementation to real-time use-cases.

Tek Classes offer Hadoop training in classroom, online and self-paced video modes. 1. Online Training:

Big Data Hadoop Online Training – Tek Classes offer Big Data and Hadoop online training through

various online technologies like Gotomeeting and Webex. These classes are affordable and are for those who are working professionals. Big Data Online training gives you the freedo of attending the session from the comfort of your home or office. Take the benefits of Big Data Hadoop online training and start your career into Big data!

2. Classroom Training:

Big Data and Hadoop Training in Bangalore – Tek Classes is one of the best Hadoop training

institutes in Bangalore. Our customized sessions on Big Data hadoop training in Bangalore, Marathahalli; is the best way to gain hands-on experience and real time knowledge. Get training from the best and experienced trainers who are specialists in Big Data training in Bangalore. 3. Self-Paced Videos:

Learn Hadoop Online through Videos – Tek Classes also offering Hadoop tutorials through

self-paced videos. Learn Hadoop Online through our self self-paced videos which is focused on the concepts, webinars, case studies, and much more. Our Hadoop online course gives you the kind of flexibility to learn from where ever you want. Take this course and upgrade your knowledge on Big data Hadoop.

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Introduction to Big Data

What is Big Data?

What are the challenges for processing big data?

What technologies support big data?

3V’s of BigData and Growing.

Problems with traditional large-scale systems. Introduction to Hadoop

An Overview of Hadoop

History of Hadoop

Hadoop Core

o The Hadoop Distributed File System o MapReduce Programming model

Hadoop Ecosystem

Real Life Use Cases Hadoop Cluster Setup

Setup & Configuration details

Local mode

Pseudo distributed mode

Distributed mode

Using Cloudera CDH.

Hadoop Distributed File System (HDFS)

HDFS Design & Concepts

Building Blocks of Hadoop

o Name Node (NN) and its functionality o Data Node(DN) and its functionality

o SecondaryNameNode(SNN) and its functionality

Replica and Block placement

HDFS user and admin commands.

Basic File System Operations

HDFS Java Client API

Read and Write flow

Safemode

distCP - Data loading into HDFS parallel

Hadoop Data Archives

Data Integrity and Compression Map Reduce

Components of MapReduce

JobTracker and its functionality

TaskTrack and its functionality

Job execution flow

MapReduce Programming Model

Mapper

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Writable and WritableComparator

MapReduce old and new API’s.

Input Formatters and its associated Record Readers

InputSplits

Output Formatters and its associated Record Writers

Configuration and Writing MR jobs in Eclipse.

Running MR Job on Local Mode.

Running MR Job on Cluster/Distributed Mode

Shuffle Sort

Combiner

Partitioner

Job submission flow

Speculative Execution

RawComparator

Different FileFormats (Sequence File, MapFile, Other File Formats)

Hands-on MapReduce Program Examples Advance Map Reduce Programming

Custom Writable

Custom Partitioner

Custom Combiner

Custom Input and output Formatters

Custom Sorting (Secondary Sorting)

Distributed Cache

Counters & Reporter

Compression techniques

Joins

Chaining of MR Jobs

Adding third party libraries to MRJobs Programming Practices

Writing MapReduce Programs with Eclipse IDE

Setup Maven Project for writing MapReduce Jobs.

Web UI for monitoring cluster

Side Data Distribution Techniques

Sending Job specific parameters

Using Distributed Cache

Performance tuning

Partitioning MR Job output into multiple output files. Apache PIG

Introduction to Apache Pig

Setup & Configurations

Pig Latin through Grunt Shell

Data types

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Expressions and Functions

Working with Pig Script

Writing reusable script by parameter substitution

Writing UDF's

Pig Joins

Load and Processing Complex Data with Pig

Hands-on writing Pig Script

DataFu/Piggy Bank Apache Hive

Introduction to Apache Hive

Hive vs SQL

Setup & Configuration

Hive Architecture MetaStore Different DataTypes Hive CLI Hive QL DDL and DML Operations

Hive build in operators and functions

Create Partitioned tables

Create User Defined Functions

Bucketing

Working with different FileFormats

Perform a join of two datasets with Hive

Tuning

Apache HBase

HBase introduction

When Should I Use HBase

HBase Vs HDFS

Setup & Configurations

Key Design

Column families

HBase shell commands

Basic CRUD operations

Web Based UI HBase Architecture HBase Components Zookeeper Compaction HBase Hands-on Mapreduce integration Pig Integration Hive Integration HBase Clients

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Apache Oozie Apache Flume Apache Sqoop

Who can take this course?

Suggested attendees for Hadoop Course may be: Developers, Architects, System Engineers, BI Professionals, Analytics Professionals, Data Analyst and Graduates who want to make a career in HADOOP.

Prerequisite

 Basic knowledge of UNIX Commands

 Knowledge of Core Java.

Key-Takeaways

 30 Hours of training with Lab Exercises with Proprietary VM

 Packed with Latest & Advanced modules like YARN, Flume, Oozie and Sqoop

 Well-Experienced and Real Time Trainers

 Best in Class Infrastructure

 Real time case studies and project integrated into the Curriculum

 24*7 Support from our team of administrators

 Course Material and Lab guides for students reference

 Placement assistance for those who are looking out for a career in Big data Hadoop

Trainer’s Profile

 Cloud era Certified Trainer

 6+ Years of Experience in Java

 2+ Years of experience in Hadoop Classroom Training and Hadoop Online Training

 Working as a Big data Architect in TOP MNC