Big Data Training in Chennai - Online

Hope Tutors

Training

Online

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Description

  • Type

    Training

  • Level

    Intermediate

  • Methodology

    Online

  • Class hours

    30h

  • Duration

    Flexible

  • Start date

    Different dates available

  • Online campus

    Yes

  • Delivery of study materials

    Yes

  • Support service

    Yes

  • Virtual classes

    Yes

There is an enormous need for big data analysts in IT industries since information technology handles millions of data every single moment. In order to keep track of and to analyze the data, the contribution of big data analysts is highly appreciated among IT companies.

In that same vein, Hadoop is an open-source software framework which is used to store the data sporadically and to process the datasets of big data. It is done with the aid of MapReduce programming model. With a lot of commodity hardware nodes, Hadoop posits it as possible to run applications on systems.

According to a survey reports, more than 60% of modern corporations are solely depending on big data analytics to raise the organization’s social media marketing abilities. And, more importantly, in another survey conducted by Peer-Research Big Data Survey, they list the benefits of big data for a big company.

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

Analytics professionals
Data analysts
Senior IT professionals
Data management professionals
Project managers
Business intelligence professionals

Big Data Features
The big data Hadoop certification course offered by Hope Tutors is designed to give you the complete insight into the big data framework by giving you a thorough understanding of HDFS, YARN and MapReduce. We will train you to analyse the data that is collectively stored in HDFS. Using Flume for the digestion of data also is trained in our big data training course.

Processing the data with Spark is a major part of our Hadoop big data training course as you will learn different interactive algorithms and how to use Spark SQL to generate, transform and query the data forms. And during our big data training course, you are going to work out industrial level projects in the sectors of telecommunication and social media for the deeper understanding of big data.

MapReduce is the essential component of Apache Hadoop software framework and MapReduce is notable for its ability to sort out through unstructured data sets. So learning MapReduce will enrich your ability to navigate datasets that is in its primal form.

Our best Hadoop big data training course in Chennai also covers PIG and SQOOP aspects as well. With PIG, its structure is so flexible that it can be substantially parallelized. Its textual language is vital to handle very large datasets. In addition, Sqoop is used to transfer the data from Hadoop to the database servers, vice versa.

YARN – Yet Another Resource Negotiator – is a cluster management program. YARN is a wide-reaching operating system for big data applications. All of these will be covered in Hope Tutors’ big data training course. All these tools are put together and easily integrated with Hadoop and the big data will be processed faster.

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Subjects

  • Apache
  • Workflow
  • Import
  • Programming
  • YARN
  • Hadoop
  • Framework
  • Components
  • Execution
  • Program
  • Configuration
  • Architecture
  • Deployment
  • Project

Course programme

COURSE CURRICULUM
  • Hadoop YARN Introduction
  • Hadoop YARN Setup
  • Programming in YARN framework j
Understanding big data and Hadoop
  • Big Data
  • Limitations and Solutions of existing Data Analytics Architecture
  • Hadoop Features
  • Hadoop Ecosystem
  • Hadoop 2.x core components
  • Hadoop Storage: HDFS
  • Hadoop Storage : Azure Data Lake Introduction
  • Hadoop Processing: MapReduce Framework
  • Hadoop Different Distributions.
Hadoop Mapreduce Framework & YARN
  • MapReduce Use Cases
  • Traditional way Vs MapReduce way
  • Why MapReduce
  • Hadoop 2.x MapReduce Architecture
  • Hadoop 2.x MapReduce Components
  • YARN MR Application Execution Flow
  • YARN Workflow
  • Anatomy of MapReduce Program
  • Demo on MapReduce. Input Splits
  • Relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner
  • Demo on de-identifying Health Care Data set
  • Demo on Weather Data set.
Hadoop Architecture and HDFS
  • Hadoop 2.x Cluster Architecture – Federation and High Availability
  • A Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands
  • Hadoop 2.x Configuration Files
  • Single node cluster and Multi node cluster set up Hadoop Administration.
Advanced Mapreduce
  • Counters
  • Distributed Cache
  • Reduce Join
  • Custom Input Format
  • Sequence Input Format
  • Xml file Parsing using MapReduce.
Pig
  • About Pig
  • MapReduce Vs Pig
  • Pig Use Cases
  • Programming Structure in Pig
  • Pig Running Modes
  • Pig components
  • Pig Execution
  • Pig Latin Program
  • Data Models in Pig
  • Pig Data Types
  • Shell and Utility Commands
  • Pig Latin : Relational Operators
  • File Loaders
  • Group Operator
  • COGROUP Operator
  • Joins and COGROUP
  • Union
  • Diagnostic Operators
  • Specialized joins in Pig
  • Built In Functions ( Eval Function
  • Load and Store Functions
  • Math function
  • String Function
  • Date Function
  • Pig UDF
  • Piggybank
  • Parameter Substitution ( PIG macros and Pig Parameter substitution )
  • Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use case in PIG
  • Pig Demo on Healthcare Data set.
Hive
  • Hive Background
  • Hive Use Case
  • About Hive
  • Hive Vs Pig
  • Hive Architecture and Components
  • Metastore in Hive
  • Limitations of Hive
  • Comparison with Traditional Database
  • Hive Data Types and Data Models
  • Partitions and Buckets
  • Hive Tables(Managed Tables and External Tables)
  • Importing Data
  • Querying Data
  • Managing Outputs
  • Hive Script
  • Hive UDF
  • Retail use case in Hive
  • Hive Demo on Healthcare Data set.
Advanced Hive and Hbase
  • Hive QL: Joining Tables
  • Dynamic Partitioning
  • Custom Map/Reduce Scripts
  • Hive Indexes and views Hive query optimizers
  • Hive : Thrift Server
  • User Defined Functions
  • HBase: Introduction to NoSQL Databases and HBase
  • HBase v/s RDBMS
  • HBase Components
  • HBase Architecture
  • Run Modes & Configuration
  • HBase Cluster Deployment.
Advanced Hbase
  • HBase Data Model
  • HBase Shell
  • HBase Client API
  • Data Loading Techniques
  • ZooKeeper Data Model
  • Zookeeper Service
  • Zookeeper
  • Demos on Bulk Loading
  • Getting and Inserting Data
  • Filters in HBase.
Getting started with Sqoop
  • In this module, you will be introduced to Hadoop you will get to know the Traditional database’s application. Also, you will get to know the basics of Sqoop.
  • Sqoop as an Import/Export tool
  • Sqoop Import Process
  • Basic Sqoop Commands
  • Importing Data in HDFS using Sqoop
  • Exporting Data from HDFS
  • :Import /Export Data between RDBMS and Hive/HBase
FLUME
  • Architecture
  • Flume events
  • Inceptors, channel ,sink processor
  • Twitter Data in HDFS
  • Telnet as source and HBase as a sink
  • Twitter Data in HBase
Oozie and Hadoop project

Oozie
  • Oozie Components
  • Oozie Workflow
  • Scheduling with Oozie
  • Demo on Oozie Workflow
  • Oozie Co-ordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • PIG
  • Hive and Sqoop
  • Combine flow of MR
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Integration with Talend.
Understanding Apache Kafka and Kafka Cluster
  • Need for Kafka
  • Core Concepts of Kafka
  • Kafka Architecture
  • Where is Kafka Used
Processing Distributed data with Apache spark
  • What is Apache Spark
  • Spark Ecosystem
  • Spark Components
  • History of Spark and Spark Versions/Releases
  • Spark a Polyglot
  • What is Scala?
  • Why Scala?
  • SparkContext
  • RDD

See related categories

Big Data Training in Chennai - Online

Price on request

The best Other Database courses

See all

See all