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Hadoop Development Training Malleswaram, Bangalore

Course Duraiton: 2 Month

IGEEKS Technologies: Hadoop Training Course Content

Hadoop Development Training course teaches experienced / knowledge peoples on purpose of Hadoop Technology, how to setup Hadoop Cluster, how to store BigData using Hadoop (HDFS) and how to process/analyze the BigData using Map-Reduce Programming or by using other Hadoop ecosystems.

Basic Unix Commands

Core Java (OOPS Concepts, Collections , Exceptions ) — For Map-Reduce Programming

SQL Query knowledge – For Hive Queries

Any Linux flavor OS (Ex: Ubuntu/Cent OS/Fedora/RedHat Linux) with 4 GB RAM (minimum), 100 GB HDD

Java 1.6+

MYSQL Database

Eclipse IDE

VM Ware (To use Linux OS along with Windows OS)

50 Hours, daily 1:30 Hours

Hadoop Training Course Content

High Availability


Advantages and Challenges

What is Big data

Big Data opportunities

Big Data Challenges

Characteristics of Big data

Hadoop Distributed File System

Comparing Hadoop & SQL

Industries using Hadoop.

Data Locality.

Hadoop Architecture.

Map Reduce & HDFS.

Using the Hadoop single node image (Clone).

HDFS Design & Concepts

Blocks, Name nodes and Data nodes

HDFS High-Availability and HDFS Federation.

Hadoop DFS The Command-Line Interface

Basic File System Operations

Anatomy of File Read

Anatomy of File Write

Block Placement Policy and Modes

More detailed explanation about Configuration files.

Metadata, FS image, Edit log, Secondary Name Node and Safe Mode.

How to add New Data Node dynamically.

How to decommission a Data Node dynamically (Without stopping cluster).

FSCK Utility. (Block report).

How to override default configuration at system level and Programming level.

HDFS Federation.

ZOOKEEPER Leader Election Algorithm.

Exercise and small use case on HDFS.

Functional Programming Basics.

Map and Reduce Basics

How Map Reduce Works

Anatomy of a Map Reduce Job Run

Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates

Job Completion, Failures

Shuffling and Sorting

Splits, Record reader, Partition, Types of partitions & Combiner

Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots.

Types of Schedulers and Counters.

Comparisons between Old and New API at code and Architecture Level.

Getting the data from RDBMS into HDFS using Custom data types.

Distributed Cache and Hadoop Streaming (Python, Ruby and R).


Sequential Files and Map Files.

Enabling Compression Codec’s.

Map side Join with distributed Cache.

Types of I/O Formats: Multiple outputs, NLINEinputformat.

Handling small files using CombineFileInputFormat.

Hands on “Word Count” in Map/Reduce in standalone and Pseudo distribution Mode.

Sorting files using Hadoop Configuration API discussion

Emulating “grep” for searching inside a file in Hadoop

DBInput Format

Job Dependency API discussion

Input Format API discussion

Input Split API discussion

Custom Data type creation in Hadoop.


CAP Theorem and Types of Consistency.

Types of NoSQL Databases in detail.

Columnar Databases in Detail (HBASE and CASSANDRA).

TTL, Bloom Filters and Compensation.

HBase Installation

HBase concepts

HBase Data Model and Comparison between RDBMS and NOSQL.

Master & Region Servers.

HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture.

Catalog Tables.

Block Cache and sharding.


DATA Modeling (Sequential, Salted, Promoted and Random Keys).

JAVA API’s and Rest Interface.

Client Side Buffering and Process 1 million records using Client side Buffering.

HBASE Counters.

Enabling Replication and HBASE RAW Scans.

HBASE Filters.

Bulk Loading and Coprocessors (Endpoints and Observers with programs).

Real world use case consisting of HDFS, MR and HBASE.


Introduction and Architecture.

Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)

Meta store

Hive QL


Working with Tables.

Primitive data types and complex data types.

Working with Partitions.

User Defined Functions

Hive Bucketed Tables and Sampling.

External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts

Dynamic Partition

Differences between ORDER BY, DISTRIBUTE BY and SORT BY.

Bucketing and Sorted Bucketing with Dynamic partition.

RC File.



Compression on hive tables and Migrating Hive tables.

Dynamic substation of Hive and Different ways of running Hive

How to enable Update in HIVE.

Log Analysis on Hive.

Access HBASE tables using Hive.

Hands on Exercises


Execution Types

Grunt Shell

Pig Latin

Data Processing

Schema on read

Primitive data types and complex data types.

Tuple schema, BAG Schema and MAP Schema.

Loading and Storing


Grouping & Joining

Debugging commands (Illustrate and Explain).

Validations in PIG.

Type casting in PIG.

Working with Functions

User Defined Functions

Types of JOINS in pig and Replicated Join in detail.

SPLITS and Multiquery execution.

Error Handling, FLATTEN and ORDER BY.

Parameter Substitution.

Nested For Each.

User Defined Functions, Dynamic Invokers and Macros.

How to access HBASE using PIG.

How to Load and Write JSON DATA using PIG.

Piggy Bank.

Hands on Exercises


Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV,Compressing,Control Parallelism, All tables Import)

Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)

Free Form Query Import

Export data to RDBMS,HIVE and HBASE

Hands on Exercises.


Linking with Spark

Initializing Spark

Using the Shell

Resilient Distributed Datasets (RDDs)

Parallelized Collections

External Datasets

RDD Operations

Basics, Passing Functions to Spark

Working with Key-Value Pairs



RDD Persistence

Which Storage Level to Choose?

Removing Data

Shared Variables

Broadcast Variables


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