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Big Data Hadoop Developer Course

Big Data Hadoop Developer Course

  • (13 reviews)

Course Introduction

About Big Data Hadoop Developer Course

Hadoop is gaining popularity among a large number of business organizations as it has helped in removing the issues linked with Big Data.  Hadoop Tutorial is a type of Open Source, a Java-based programming network fundamental specially designed for network professionals. Hadoop Training tool supports the easier processing of a large number of data sets in a distributed computing environment. The fundamentals of the system are based on the Google File System (GFS). The Hadoop Course content here has been designed in such a way that Hadoop certified professionals will quickly land up with a lucrative job prospect in real-time. Hadoop Training Online is provided by real-time industry professionals by considering the actual scenario.

Key Features

  • Aligned to Big Data Certification Exam
  • Real-life industry projects in various domains
  • Training on MapReduce, Pig, Hive, HBase, Apache Spark and few other modules
  • Quick turnaround on the queries
  • Subscription-Based Model for Self Learning
  • Quiz to check the learning at the end of the course

Course Description

Big Data technology is helpful in responding to the technical features of Enterprise Resource Planning (ERP) or Data Lakes. Hadoop stores Big Data in a fault-tolerant & a distributed manner over commodity hardware. The three main configuration modes of Hadoop help in performing parallel data processing over the Hadoop Distributed File System (HDFS). These configuration modes are:

        •    Standalone mode
        •    Pseudo-distributed mode
        •    Fully distributed mode

All these configuration modes offer different features for data storage. The huge demand for Big Data and Hadoop professionals is due to the benefits offered by Big Data Analytics. Companies are looking for Big data &Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, and Sqoop& Flume.

ITToolsTraining Hadoop Training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop Ecosystem. This Hadoop Developer certification training is stepping stone to your Big Data journey and you will get the opportunity to work on various Big data projects.

This course targets those individuals who are new to data science and are also interested in understanding why the era of Big Data has arrived. It is best suited for:

        •    Software Developers, Project Managers
        •    Software Architects
        •    ETL and Data Warehousing Professionals
        •    Data Engineers
        •    Data Analysts & Business Intelligence Professionals
        •    DBAs and DB professionals
        •    Senior IT Professionals
        •    Testing professionals
        •    Mainframe professionals
        •    Graduates looking to build a career in Big Data Field

No pre-requisites are required for the course. However, fundamental knowledge of SQL and Core Java will be an added advantage but not compulsory. 

The Course is planned by industry specialists to make you a Certified Big Data Practitioner. The Big Data Hadoop course offers:

        •    Inside and out learning of Big Data and Hadoop including HDFS (Hadoop Distributed File System), 
              YARN (Yet Another Resource Negotiator) and MapReduce
        •    Far-reaching learning of different apparatuses that fall in Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, 
              Oozie, and HBase
        •    The ability to ingest information in HDFS utilizing Sqoop and Flume, and investigate those enormous 
              datasets put away in the HDFS 
        •    The introduction to numerous certifiable industry-based projects.

Curriculum

  • 1.1 - Big Data Introduction
  • 1.2 - Limitations and Solutions of existing Data Analytics Architecture
  • 1.3 - Hadoop Introduction, Hadoop Features
  • 1.4 - Hadoop Ecosystem, Hadoop 2.x core components, Hadoop Different Distributions
  • 1.5 - Hadoop Storage: HDFS Introduction, Hadoop Processing: MapReduce Framework
  • 2.1 - Hadoop Shell Commands- LINUX & HDFS
  • 2.2 - Hadoop 2.x Architecture, Hadoop Federation
  • 2.3 - High Availability Concepts, Different Hadoop Cluster Modes
  • 2.4 - Hadoop Cluster Demo, Hadoop 2.x Configuration Files Details
  • 2.5 - Single node cluster and Multi node cluster set up
  • 3.1 - MapReduce UseCases, Traditional methods Vs MapReduce
  • 3.2 - Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components
  • 3.3 - YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program
  • 3.4 - Input Splits, Relation between Input Splits and HDFS Blocks
  • 3.5 - Demo on MapReduce
  • 3.6 - MapReduce: Combiner & Partitioner
  • 3.7 - Case Studies: Word Count, Health Care Data set, Weather Data set
  • 4.1 - About Pig, MapReduce Vs Pig, Pig Use Cases
  • 4.2 - Programming Structure in Pig, Pig Running Modes, Pig components
  • 4.3 - Pig Execution, Pig Latin Program, Pig Data Types
  • 4.4 - Data Models in Pig
  • 4.5 - Shell and Utility Commands, Pig Latin : Relational Operators, Pig Latin : File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union
  • 4.6 - Lab, WordCount example in PIG
  • 4.7 - Pig Latin : Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function), Pig UDF
  • 4.8 - Case Studies: HealthCare DataSet Problem Solving
  • 5.1 - Hive Introduction, Hive Use Case, About Hive, Hive Vs Pig
  • 5.2 - Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database
  • 5.3 - Hive Data Types and Data Models, Partitions and Buckets
  • 5.4 - Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Lab
  • 5.5 - Hive Script, Hive UDF, Joins, Lab
  • 5.6 - Case Studies: HealthCare DataSet Problem Solving
  • 6.1 - Hive QL: Dynamic Partitioning, Thrift Server
  • 6.2 - Hive QL: Hive Indexes and views
  • 6.3 - HBase: Introduction to NoSQL Databases and HBase
  • 6.4 - HBase v/s RDBMS, HBase Components
  • 6.5 - Run Modes & Configuration, HBase Architecture
  • 7.1 - HBase Data Model, HBase Shell Demo: Getting and Inserting Data, Filters in Hbase
  • 7.2 - HBase Client API
  • 7.3 - Data Loading Techniques, Bulk Loading, ZooKeeper Data Model, Zookeeper Service, Zookeeper & Hbase
  • 7.4 - Case Studies: Zookeeper Hbase Use Case, Practice codes
  • 8.1 - Apache Spark Introduction, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases
  • 8.2 - What is Scala?, Why Scala?, Scala REPL
  • 8.3 - Spark Lab
  • 8.4 - SparkContext, RDD
  • 8.5 - Case Studies: WordCount on Standalone, Local & HDFS mode
  • 9.1 - Flume Introduction & Demo Lab
  • 9.2 - Sqoop Introduction & Demo Lab
  • 10.1 - Oozie Introduction, Oozie Components, Oozie Workflow, Oozie Web Console
  • 10.2 - Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands
  • 10.3 - Hadoop Project Description
  • 10.4 - Case Studies: Oozie Practicals

FAQ

Just getting enrolled in the course, your Learning Management System (LMS) access will get functional. Immediately, you will be accessible to the entire course content in the form of a complete set of Videos, Quiz, and Assignments. You can start learning right away.

You can pay by Credit Card, Debit Card or NetBanking from all the leading banks. We use a CCAvenue Payment Gateway.

Industry Experts having 10-12 years of real industrial experience

You can contact us at support@ITToolsTraining.com or use Contact Us form from our website.

We do help in preparing for the interviews. Our courses are designed in the way that you can crack the interviews easily provided you have completed the course with assignments and Quizzes.

Reviews

Score Breakdown

4.5 / 5.0

(13 review)

89.23% recommend this course

Score Breakdown
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    (7)
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  • (2)
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Gunjan
Useful course to have introduction to Big data and Hadoop.
Shiva
Good & Easy explanation of basic concepts
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