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Best Big Data Training in Chennai

Learn from our Bigdata experts from the scratch to extreme level to do the analytics about the data. The syllabus designed to give you training more on practical way with set of scenario based question along with mini projects. It helps you to learn in expert level.

What Is Big Data?

It is very difficult to process complex and large set of data With the help of traditional systems like RDBMS, Enterprise System, etc. Now a days any business like Banking, Finance, Telecom or social media like Twitter, Facebook generating more than 90% of data what they generated few years back. Hadoop is the system or frame work used for storing or handling those large volumn of data including different formats like structured, unstructured semi structured. This system is generally designed to scale up to thousands of servers. Also it is open source provided by Apache. Most of large giants like Google, Twitter, Facebook, Amazon, etc are moved their project into hadoop eco system.

Why should join Big Data Training in Infycle?

This course used to analyze more on about data and you can play more on analytics part which will leads to become an data scientist. This course is best base of all data related technologies like Data Science, Machine Learning, IOT, AI, etc. In this digital world, Going forward every business developement cycle purely based on data and data analytics. So it helps everyone to get into analytics part more.

Introduction to Bigdata and the Hadoop Ecosystem

Why we need Bigdata

Real time use cases of Bigdata Overview

Introduction to Apache Hadoop and the Hadoop Ecosystem

Apache Hadoop Overview

Data Ingestion and Storage

Data Locality

Data Analysis and Exploration

Other Ecosystem Tools

Hadoop Ecosystem Installation

Ubuntu 14.04 LTS Installation through VMware Player

Installing Hadoop 2.7.1 on Ubuntu 14.04 LTS (Single-Node Cluster)

Apache Hive Installation

MySQL Installation

Apache Sqoop Installation

Apache Flume Installation

Apache Kafka Installation

Apache Spark Installation

Scala SBT Installation

Apache Hadoop File Storage (HDFS)

Why we need HDFS

Apache Hadoop Cluster Components

HDFS Architecture

Failures of HDFS 1.0

High Availability and Scaling

Pros and Cons of HDFS

Basics File system Operations

Hadoop FS or HDFS DFS - The Command-Line Interface

Decommission methods for Data Nodes

Exercise and small use case on HDFS

Block Placement Policy and Modes

Configuration files handling

Federation of HDFS

FSCK Utility (Heart Beat and Block report)

Reading and Writing Data in HDFS

Replica Placement Strategy

Fault tolerance


Overview and Architecture of Map Reduce

Components of MapReduce

How MapReduce works

Flow and Difference of MapReduce Version

YARN Architecture

Working with YARN

Types of Input formats & Output Formats

Apache Hive

Hive Installation on Ubuntu 14.04 With MySQL Database Metastore

Hive Overview and Architecture

Hive command execution in shell and HUE

Hive Data Loading methods

Hive Partition and Bucketing

External and Managed tables in Hive

File formats in Hive

Hive Joins

Serde in Hive

Apache Sqoop

Apache Sqoop Overview and Architecture

Apache Sqoop Import

Apache Sqoop Export

Sqoop Incremental load

Managing Directories

File Formats

Boundary Query and Split-by

Delimiter and Handling Nulls

Sqoop import all tables

Apache Pig

Apache Pig Overview and Architecture

MapReduce Vs Pig

Data types of Pig

Pig Data loading methods

Pig Operators and execution modes

Performance Tuning in Pig

Type casting in Pig

Data Validation in Pig

Pig script execution in shell/HUE

Apache Hbase

Introduction to NoSQL/CAP theorem concepts

Apache HBase Overview and Architecture

Apache HBase Commands

HBase and Hive Integration module

Hbase execution in shell/HUE

Apache Spark Basics

What is Apache Spark?

Starting the Spark Shell

Using the Spark Shell

Getting Started with Datasets and Data Frames

Data Frame Operations

Apache Spark Overview and Architecture

RDD and Paired RDD

RDD Overview

RDD Data Sources

Creating and Saving RDDs

RDD Operations

Transformations and Actions

Converting Between RDDs and Data Frames

Key-Value Pair RDDs

Map-Reduce operations

Other Pair RDD Operations

Working with Data Frames, Schemas and Datasets

Creating Data Frames from Data Sources

Saving Data Frames to Data Sources

Data Frame Schemas

Eager and Lazy Execution

Querying Data Frames Using Column Expressions

Grouping and Aggregation Queries

Joining Data Frames

Querying Tables, Files, Views in Spark Using SQL

Comparing Spark SQL and Apache Hive-on-Spark

Creating Datasets

Loading and Saving Datasets

Dataset Operations

Running Apache Spark Applications

Writing a Spark Application

Building and Running an Application

Application Deployment Mode

The Spark Application Web UI

Configuring Application Properties

Apache Flume

Introduction to Flume & features

Flume topology & core concepts

Flume Agents: Sources, Channels and Sinks

Property file parameters logic

Apache Kafka

Apache Kafka Installation

Apache Kafka Overview and Architecture

Consumer and Producer

Deploying Kafka in real world business scenarios

Integration with Spark for Spark Streaming

Apache Zookeeper

Introduction to zookeeper concepts

Overview and Architecture of Zookeeper

Zookeeper principles & usage in Hadoop framework

Use of Zookeeper in Hbase and Kafka

Apache Oozie

Oozie Fundamentals

Oozie workflow creations

Concepts of Coordinates and Bundles