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Big Data & Hadoop Training

Big Data & Hadoop training will master you in understanding the concepts of the Hadoop framework and prepares you for Big data certification. It is a comprehensive course designed by industry experts considering current industry job requirements to provide in-depth learning on big data and Hadoop Modules. This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. The program begins with Big Data Hadoop and Spark developer course to provide a solid foundation in the Big Data Hadoop framework, then moves on to Apache Spark and Scala to give you an in-depth understanding of real time processing.
Success Rate 100%
Job Placements96%
Professional Growth89%
Live Projects75%

Course Overview

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Big Data & Hadoop training will master you in understanding the concepts of the Hadoop framework and prepares you for Big data certification. It is a comprehensive course designed by industry experts considering current industry job requirements to provide in-depth learning on big data and Hadoop Modules.

This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. The program begins with Big Data Hadoop and Spark developer course to provide a solid foundation in the Big Data Hadoop framework, then moves on to Apache Spark and Scala to give you an in-depth understanding of real time processing.

Course Content

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Section 1: Understanding Big Data and Hadoop 
  • Introduction to Big Data
  • Importance of Big Data
  • Big data and its Hype
  • Structured vs Unstructured Data
  • Big Data users and Scenarios
  • Challenges of Big Data
  • Why Distributed Processing
Section 2: Hadoop Architecture and HDFS 
  • History Of Hadoop
  • Hadoop Ecosystem
  • Hadoop Animal Planet
  • When to use & when not to use Hadoop
  • What is Hadoop?
  • Key Distinctions of Hadoop
  • Hadoop Components/Architecture
  • Understanding Storage Components
  • Understanding Processing Components
  • Anatomy Of a File Write
  • Anatomy of a File Read
Section 3: Hadoop MapReduce Framework 
  • Meet MapReduce
  • Word Count Algorithm – Traditional approach
  • Traditional approach on a Distributed system
  • Traditional approach – Drawbacks
  • MapReduce approach
  • Input & Output Forms of a MR program
  • Map, Shuffle & Sort, Reduce Phases
  • Workflow & Transformation of Data
  • Word Count Code walkthrough
Section 4: Advanced MapReduce 
  • Combiner
  • Partitioner
  • Counters
  • Hadoop Data Types
  • Custom Data Types
  • Input Format & Hierarchy
  • Output Format & Hierarchy
  • Side Data distribution – Distributed cache
  • Joins
  • Map side Join using Distributed cache
  • Reduce side Join
  • MR Unit – An Unit testing framework
Section 5: Pig 
  • What is Pig?
  • Why Pig?
  • Pig vs Sql
  • Execution Types or Modes
  • Running Pig
  • Pig Data types
  • Pig Latin relational Operators
  • Multi Query execution
  • Pig Latin Diagnostic Operators
  • Pig Latin Macro & UDF statements
  • Pig Latin Commands
  • Pig Latin Expressions
  • Schemas
  • Pig Functions
  • Pig Latin File Loaders
  • Pig UDF & executing a Pig UDF
Section 6: Hive 
  • Introduction to Hive
  • Pig Vs Hive
  • Hive Limitations & Possibilities
  • Hive Architecture
  • Metastore
  • Hive Data Organization
  • Hive QL
  • Sql vs Hive QL
  • Hive Data types
  • Data Storage
  • Managed & External Tables
  • Partitions & Buckets
  • Storage Formats
  • Built-in Serdes
  • Importing Data
  • Alter & Drop Commands
  • Data Querying
Section 7: Advanced Hive and Hbase 
  • Introduction to NoSql & HBase
  • Row & Column oriented storage
  • Characteristics of a huge DB
  • What is HBase?
  • HBase Data-Model
  • HBase vs RDBMS
  • HBase architecture
  • HBase in operation
  • Loading Data into HBase
  • HBase shell commands
  • HBase operations through Java
  • HBase operations through MR
Section 8: Processing Distributed Data with Apache Spark
  • You will learn Spark ecosystem and its components
  • how scala is used in Spark, SparkContext.
  • You will learn how to work in RDD in Spark.
  • Demo will be there on running application on Spark Cluster, Comparing performance of MapReduce and Spark. 
Section 9: Oozie and Hadoop Project 

  • You will understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems.
  • We will discuss multiple data sets and specifications of the project.
  • This module will also cover Flume &Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration. 

Who Should Attend

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The Big Data Hadoop Architect is a highly desirable career goal for those seeking to fast-track their career in the Big Data field. With the number of Big Data career opportunities on the rise, the following roles will benefit most from this learning path: 

  • Graduates looking to build a career in Big Data Analytics. 
  • IT, data management, and analytics professionals looking to gain expertise in Big Data. 
  • Anyone interested in Hadoop, HDFS and MapReduce. 
  • For those who want to learn programming in MapReduce. 

 

Learning Outcomes

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  • Master the concepts of HDFS and MapReduce framework
  • Understand Hadoop 2.x Architecture
  • Setup Hadoop Cluster and write Complex MapReduce programs
  • Learn data loading techniques using Sqoop and Flume
  • Perform data analytics using Pig, Hive and YARN
  • Implement HBase and MapReduce integration
  • Implement Advanced Usage and Indexing
  • Schedule jobs using Oozie
  • Implement best practices for Hadoop development
  • Understand Spark and its Ecosystem
  • Learn how to work in RDD in Spark
  • Work on a real life Project on Big Data Analytics

Schedule

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Please look for the next dates in the schedule below:

Modules Start Date Location
Big data & Hadoop Training October 6th;2018  Brampton
Big Data & Hadoop Training(next Batch) November 10th;2018  Brampton