Instructor-led Sessions
36 Hours of Online Live Instructor-Led Classes. Weekend Class : 12 sessions of 3 hours each. Weekday Class : 18 sessions of 2 hours each.
Hadoop Training in Pune
Validate your skills as an Hadoop expert
Achieving an Hadoop certification exemplifies your expertise, 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.
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.
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Training Features
Lifetime Access
You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.
Real-life Case Studies
Live project based on any of the selected use cases, involving implementation of the various Hadoop concepts.
24 x 7 Expert Support
We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.
Assignments
Each class will be followed by practical assignments which can be completed before the next class.
Certification
Towards the end of the course, you will be given access to online Test. Iteanz certifies you as an Hadoop Expert based on the scoring of 60% or above.
Course Outline
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
- HiveArchitecture 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 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 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
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?
- SparkContext
- 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
Why iteanz for Hadoop training?
- Top technical trainers
- Real Time and Hands on Experience Training
- On Time Course Completion
- Comprehensive curriculum
- Innovative & interactive Training
- Superb satisfaction scores
- High Certification Pass Rate
Prerequisites
- No previous Hadoop or programming knowledge is required
- Basic Java Programming Knowledge
- A Windows, Linux or Mac PC/Laptop
Intended Audience
- Anyone who is looking to build a career in Big Data Analytics
- Software Developers and Architects
- Data Analytics Professionals
- Senior IT professionals
- Testing and Mainframe Professionals
- Data Management Professionals
- Business Intelligence Professionals
- Project Managers
- Aspiring Data Scientists
What People Say
I have done Big data And Hadoop course in iteanz. A very interactive, skilled & intelligent trainer. Effective way of teaching. Made Hadoop interesting and easy to learn. One of the best.
