Call : +91 8050580888, +442038072367

Hadoop - Big Data

Hadoop Training in Bangalore

Hadoop (1.x / 2.x) Course Content

Module 1: Hadoop Introduction

  1. Introduction to Data and System
  2. Types of Data
  3. Traditional way of dealing large data and its problems
  4. Types of Systems & Scaling
  5. What is Big Data
  6. Challenges in Big Data
  7. Challenges in Traditional Application
  8. New Requirements
  9. What is Hadoop? Why Hadoop?
  10. Brief history of Hadoop
  11. Features of Hadoop
  12. Hadoop and RDBMS
  13. Hadoop Ecosystem’s overview

Module 2: Hadoop Installation

  1. Installation in detail
  2. Creating Ubuntu image in VMware
  3. Downloading Hadoop
  4. Installing SSH
  5. Configuring Hadoop, HDFS & MapReduce
  6. Download, Installation & Configuration Hive
  7. Download, Installation & Configuration Pig
  8. Download, Installation & Configuration Sqoop
  9. Download, Installation & Configuration Hive
  10. Configuring Hadoop in Different Modes

Module 3: Hadoop Distribute File System (HDFS)

  1. File System - Concepts
  2. Blocks
  3. Replication Factor
  4. Version File
  5. Safe mode
  6. Namespace IDs
  7. Purpose of Name Node
  8. Purpose of Data Node
  9. Purpose of Secondary Name Node
  10. Purpose of Job Tracker
  11. Purpose of Task Tracker
  12. HDFS Shell Commands – copy, delete, create directories etc.
  13. Reading and Writing in HDFS
  14. Difference of Unix Commands and HDFS commands
  15. Hadoop Admin Commands
  16. Hands on exercise with Unix and HDFS commands
  17. Read / Write in HDFS – Internal Process between Client, NameNode & DataNodes
  18. Accessing HDFS using Java API
  19. Various Ways of Accessing HDFS
  20. Understanding HDFS Java classes and methods
  21. Commissioning / DeCommissioning DataNode
  22. Balancer
  23. Replication Policy
  24. Network Distance / Topology Script

Module 4: Map Reduce Programming

  1. About MapReduce
  2. Understanding block and input splits
  3. MapReduce Data types
  4. Understanding Writable
  5. Data Flow in MapReduce Application
  6. Understanding MapReduce problem on datasets
  7. MapReduce and Functional Programming
  8. Writing MapReduce Application
  9. Understanding Mapper function
  10. Understanding Reducer Function
  11. Understanding Driver
  12. Usage of Combiner
  13. Usage of Distributed Cache
  14. Passing the parameters to mapper and reducer
  15. Analysing the Results
  16. Log files
  17. Input Formats and Output Formats
  18. Counters, Skipping Bad and unwanted Records
  19. Writing Join’s in MapReduce with 2 Input files. Join Types
  20. Execute MapReduce Job - Insights
  21. Exercise’s on MapReduce

Module 5: Hive

  1. Hive concepts
  2. Hive architecture
  3. Install and configure hive on cluster
  4. Different type of tables in hive
  5. Hive library functions
  6. Buckets
  7. Partitions
  8. Joins in hive
  9. Inner joins
  10. Outer Joins
  11. Hive UDF
  12. Hive Query Language

Module 6: PIG

  1. Pig basics
  2. Install and configure PIG on a cluster
  3. PIG Library functions
  4. Pig Vs Hive
  5. Write sample Pig Latin scripts
  6. Modes of running PIG
  7. Running in Grunt shell
  8. Running as Java program
  9. PIG UDFs

Module 7: Sqoop

  1. Install and configure Sqoop on cluster
  2. Connecting to RDBMS
  3. Installing Mysql
  4. Import data from Mysql to hive
  5. Export data to Mysql
  6. Internal mechanism of import/export

Module 8: HBase

  1. HBase concepts
  2. HBase architecture
  3. Region server architecture
  4. File storage architecture
  5. HBase basics
  6. Column access
  7. Scans
  8. HBase use cases
  9. Install and configure HBase on a multi node cluster
  10. Create database, Develop and run sample applications
  11. Access data stored in HBase using Java API
  12. Map Reduce client to access the HBase data

Module 9: YARN

  1. Resource Manager (RM)
  2. Node Manager (NM)
  3. Application Master (AM)

OUR TRAINERS

"Our trainers are eager to share their knowledge and experience they have gained while working on critical projects in MNCs."

Mr.Shrikanth

8+ years of Java & Open Source Technologies work experience; Overall 15+ years of experience. Worked in multiple domains, including eCommerce, Financial, Healthcare, Electronics etc. Experienced in Hadoop Projects and Analytics;Enterprise Web Applications.

Contact Form

If you have questions, please send us a message.

Back to Top