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Big Data Hadoop Training in Bangalore

IQ Stream Technologies is one of the top Hadoop Developer (Big Data, Cloud Computing, Devops, Data Visualization, Project Management, Data Science) training institutes in Bangalore with highly experienced and skilled trainers. IQ Stream Technologies Bangalore also offers placement assistance for students who enrolled in Advanced Big Data Hadoop Developer Training Courses. We offer advanced Big Data, Business Intelligence, Cloud Computing, DevOps, Programming, Salesforce learning experiences and advanced tools with high tech Hadoop classes for better learning, understanding and experience.

Expert Hadoop Training Institute

Become and Expert in Big Data Hadoop with IQ Stream Technologies' advanced Hadoop learning programmes. Main highlights of our Hadoop (Big Data, Hadoop, R, FLUME, SQOOP, HBASE) Training in Bangalore (BTM Layout) include Hands-on Training on Hadoop Ecosystem, Data Science Training, Intensive Practical Training, Advanced Learning Materials & Tools, Friendly Classrooms etc. IQ Stream Technologies offer beginner, intermediate and advanced lessons for you to become an expert in the area.

Hadoop Training

Hadoop Training Location:

IQ Stream Technologies Hadoop Training Institute Location: BTM Layout, Bangalore
Mobile : +91 9620196773, +91 8884684156
Landline: +91 80 42073645​
Address: #3, 8/2 Anugraha Complex, 1st E Cross,
20th Main, Maruti Nagar Main Road,
BTM Layout 1st Stage, Bangalore - 560029

Highlights of Our Hadoop Training in Bangalore

- Intensive Practical Training
- Advanced Learning Materials & Tools
- Friendly Classrooms
- Practical Guidance
- Advanced Lab Facility
- Placement Oriented
- Expert and Experienced Trainers

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Big Data Hadoop Syllabus

Introduction to BigData, Hadoop

• Big Data Introduction
• Hadoop Introduction
• What is Hadoop? Why Hadoop?
• Hadoop History?
• Different types of Components in Hadoop?
• HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on…
• What is the scope of Hadoop?
• History of UNIX and LINUX
• Basic concepts of Operating System, Kernel, Shell & File System structure
• Basic concepts of Linux
• Differences between CentOS, Red Hat Enterprise Linux & Fedora

Deep Drive in HDFS (for Storing the Data):-

• Introduction of HDFS
• HDFS Design
• HDFS role in Hadoop
• Features of HDFS
• Daemons of Hadoop and its functionality
o Name Node
o Secondary Name Node
o Job Tracker
o Data Node
o Task Tracker
• Anatomy of File Wright
• Anatomy of File Read
• Network Topology
o Nodes
o Racks
o Data Center
• Parallel Copying using DistCp
• Basic Configuration for HDFS
• Data Organization
o Blocks and
o Replication
• Rack Awareness
• Heartbeat Signal
• How to Store the Data into HDFS
• How to Read the Data from HDFS
• Accessing HDFS (Introduction of Basic UNIX commands)
• CLI commands

MapReduce using Java (Processing the Data):-

• The introduction of MapReduce.
• MapReduce Architecture
• Data flow in MapReduce
o Splits
o Mapper
o Portioning
o Sort and shuffle
o Combiner
o Reducer
• Understand Difference Between Block and InputSplit
• Role of RecordReader
• Basic Configuration of MapReduce
• MapReduce life cycle
o Driver Code
o Mapper
o and Reducer
• How MapReduce Works
• Writing and Executing the Basic MapReduce Program using Java
• Submission & Initialization of MapReduce Job.
• File Input/Output Formats in MapReduce Jobs
o Text Input Format
o Key Value Input Format
o Sequence File Input Format
o NLine Input Format
• Joins
o Map-side Joins
o Reducer-side Joins
• Word Count Example
• Partition MapReduce Program
• Side Data Distribution
o Distributed Cache (with Program)
• Counters (with Program)
o Types of Counters
o Task Counters
o Job Counters
o User Defined Counters
o Propagation of Counters
• Job Scheduling


• Introduction to Apache PIG
• Introduction to PIG Data Flow Engine
• MapReduce vs. PIG in detail
• When should PIG use?
• Data Types in PIG
• Basic PIG programming
• Modes of Execution in PIG
o Local Mode and
o MapReduce Mode
• Execution Mechanisms
o Grunt Shell
o Script
o Embedded
• Operators/Transformations in PIG
• PIG UDF’s with Program
• Word Count Example in PIG
• The difference between the MapReduce and PIG


• Introduction to SQOOP
• Use of SQOOP
• Connect to mySql database
• SQOOP commands
o Import
o Export
o Eval
o Codegen etc…
• Joins in SQOOP
• Export to MySQL
• Export to HBase


• Introduction to HIVE
• HIVE Meta Store
• HIVE Architecture
• Tables in HIVE
o Managed Tables
o External Tables
•Hive Data Types
o Primitive Types
o Complex Types
• Partition
• Joins in HIVE
• HIVE UDF’s and UADF’s with Programs
• Word Count Example


• Introduction to HBASE
• Basic Configurations of HBASE
• Fundamentals of HBase
• What is NoSQL?
• HBase Data Model
o Table and Row
o Column Family and Column Qualifier
o Cell and its Versioning
• Categories of NoSQL Data Bases
o Key-Value Database
o Document Database
o Column Family Database
• HBASE Architecture
o HMaster
o Region Servers
o Regions
o MemStore
o Store
• How HBASE is differed from RDBMS
• HDFS vs. HBase
• Client-side buffering or bulk uploads
• HBase Designing Tables
• HBase Operations
o Get
o Scan
o Put
o Delete

Cluster Setup:--

• Downloading and installing the Ubuntu12.x
• Installing Java
• Installing Hadoop
• Creating Cluster
• Increasing Decreasing the Cluster size
• Monitoring the Cluster Health
• Starting and Stopping the Nodes


• Introduction Zookeeper
• Data Modal
• Operations


• Introduction to OOZIE
• Use of OOZIE
• Where to use?


• Introduction to Flume
• Uses of Flume
• Flume Architecture
o Flume Master
o Flume Collectors
o Flume Agents

Big Data Hadoop Training Reviews

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