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BIG DATA AND HADOOP TRAINING IN CHANDIGARH

Iteanz helps you to become a sought-after Big Data and Hadoop Professional

About Program

Highest Quality and the Most Productive, High Energy Learning Experience.

Curriculum

Comprehensive Course Curriculum. Customizable to full-fill your trainer needs.

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About Program

Validate your skills as a Hadoop expert

Achieving a Hadoop certification exemplifies your expertise, and commitment and can advance your career.

Learn Apache Hadoop from the world’s leading training company iteanz. Sixty-five percent of the current Fortune 100 are using big data to drive their business. You can too, by getting expert training by iteanz—the industry’s only truly dynamic Hadoop training curriculum that’s updated regularly to reflect the state-of-the-art in big data.

Iteanz prepares you to be an expert with highly valued skills in Hadoop certification. Enroll Today!

Hadoop is open source, it’s too complex and too critical for you to wing it or depend on endless Googling for education. That’s why our Hadoop curriculum helps professionals develop a solid foundation in MapReduce and HDFS, while also providing opportunities to dive deeper into brand-level ecosystems.

Success Factors:

  • High-Quality Training
  • Top Technical Trainers
  • Comprehensive Course Curriculum
  • 100% Placement Assistance
  • Superb Satisfaction Score
  • Internship on Real-Time Project 
  • 3I- Individual Focus, Innovative & Interactive

Curriculum

CHAPTER 1:INTRODUCTION

  • Big Data
  • Limitations and Solutions of existing Data Analytics Architecture
  • Hadoop
  • Hadoop Features
  • Hadoop Ecosystem
  • Hadoop 2.x core components
  • Hadoop Storage: HDFS
  • Hadoop Processing: MapReduce Framework
  • Hadoop Different Distributions

CHAPTER 2: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

CHAPTER 3: HADOOP MAPREDUCE FRAMEWORK

  • 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

CHAPTER 4: ADVANCED MAPREDUCE

  • Counters
  • Distributed Cache
  • MRunit
  • Reduce Join
  • Custom Input Format
  • Sequence Input Format
  • Xml file Parsing using MapReduce

CHAPTER 5: 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

CHAPTER 6:HIVE

  • Hive Background
  • Hive Use Case
  • About Hive
  • Hive Vs Pig
  • Hive Architecture and Components
  • Meta store 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 Output
  • Hive Script
  • Hive UDF
  • Retail use case in Hive
  • Hive Demo on Healthcare Data set

CHAPTER 7:ADVANCED HIVE AND HBASE

  • Hive QL: Joining Tables
  • Dynamic Partitioning
  • Custom Map/Reduce Scripts
  • Hive Indexes and views Hive query optimizer
  • 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

CHAPTER 8: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

CHAPTER 9: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?
  • Spark Context
  • RDD

CHAPTER 10:OOZIE AND HADOOP PROJECT

  • Flume and Sqoop Demo
  • 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
  • PIG
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Integration with Talend

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