Big Data & Hadoop Development

Course Duration : 60 hrs + 12 hrs (Case Studies)

About Big Data and Hadoop

Big data
Big data is a buzzword, or catch-phrase, used to describe a massive volume of both structured and unstructured data that is so large that it’s difficult to process using traditional database andsoftware techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.
Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.

Course Overview

This foundation course focuses on the following key areas:

  • Understanding Big Data and Hadoop
  • Master the concepts of HDFS and MapReduce framework
  • Understand Hadoop 2.x Architecture
  • Setup Hadoop Cluster and write Complex MapReduce programs
  • Learn data loading techniques using Sqoop and Flume
  • Perform data analytics using Pig, Hive and YARN
  • Implement HBase and MapReduce integration
  • Implement best practices for Hadoop development

What we offer

Case studies & Project work based on real life data
Industry Experienced Faculties
Exclusive Study Material
State-of-Art Labs with latest Infrastructure

Chapter-1: Introduction to BigData, Hadoop

1.1 Big Data Introduction
1.2 Hadoop Introduction
1.3 What is Hadoop? Why Hadoop?
1.4 Hadoop History?
1.5 Different types of Components in Hadoop?
HDFS, Map Reduce, PIG, Hive, SQOOP, HBASE
, OOZIE, Flume, Zookeeper and so on…
1.6 What is the scope of Hadoop?


Chapter-2: Deep Drive in HDFS (for Storing the Data)

2.1 Introduction of HDFS
2.2 HDFS Design
2.3 HDFS role in Hadoop
2.4 Features of HDFS
2.5 Daemons of Hadoopand its functionality
– Name Node
– Secondary Name Node
– Job Tracker
– Data Node
– Task Tracker
2.6 Anatomy of File Wright
2.7 Anatomy of File Read
2.8 Network Topology
– Nodes
– Racks
– Data Center
2.9 Parallel Copying using DistCp
2.10 Basic Configuration for HDFS
2.11 Data Organization
– Blocks
– Replication
2.12 Rack Awareness
2.13 Heartbeat Signal
2.14 How to Store the Data into HDFS
2.15 How to Read the Data from HDFS
2.16 Accessing HDFS (Introduction of Basic UNIX commands)
2.17 CLI commands


Chapter-3: MapReduce using Java (Processing the Data)

3.1 Introduction of  MapReduce.
3.2 MapReduce Architecture
3.3 Dataflow in MapReduce
– Splits
– Mapper
– Portioning
– Sort and shuffle Combiner
– Reducer
3.4 Understand Difference Between Block and InputSplit
3.5 Role of RecordReader
3.6 Basic Configuration of MapReduce
3.7 MapReduce life cycle
– Driver Code
– Mapper
– and Reducer
3.8 How MapReduce Works
3.9 Writing and Executing the Basic MapReduce Program using Java
3.10 Submission & Initialization of MapReduce Job.
3.11 File Input/output
3.12 Formatsin MapReduce Jobs
– Text Input Format
– Key Value Input Format
– Sequence File Input Format
– NLine Input FormatJoins
– Mapside Joins
– Reducer
– Side Joins
3.13 Word Count Example
3.14 Partition MapReduce Program
3.15 Side Data Distribution
– Distributed Cache (with Program)
3.16 Counters (with Program)
– Types of Counters
– Task Counters
– Job Counters
– User Defined Counters
– Propagation of Counters
3.17 Job Scheduling


Chapter-4: PIG

4.1 Introduction to Apache PIG
4.2 Introduction to PIG Data Flow Engine
4.3 MapReduce vs PIG in detail
4.4 When should PIG used?
4.5 Data Types in PIG
4.6 Basic PIG programming
4.7 Modes of Execution in PIG
– Local Modeand
– MapReduce Mode
4.8 Execution Mechanisms
– Grunt Shell
– Script
– Embedded
4.9 Operators/Transformations in PIG
4.10 PIG UDF’swith Program
4.11 Word Count Examplein PIG
4.12 The difference between the MapReduce and PIG


Chapter-5: SQOOP

5.1 Introduction to SQOOP
5.2 Use of SQOOP
5.3 Connect to mySql database
5.4 SQOOP commands
– Import
– Export
– Eval
– Codegen and etc…
5.5 Joins in SQOOP
5.6 Export to MySQL
5.7 Export to HBase


Chapter-6: HIVE

6.1 Introduction to HIVE
6.2 HIVE Meta Store
6.3 HIVE Architecture
6.4 Tables in HIVE
6.5 Managed Tables
– External Tables
6.6 Hive Data Types
– Primitive Types
– Complex Types
6.7 Partition
6.8 Joins in HIVE
6.6 HIVE UDF’s and UADF’s with Programs
6.7 Word Count Example


Chapter-7: HBASE

7.1 Introduction to HBASE
7.2 Basic Configurations of HBASE
7.3 Fundamentals of HBase
7.4 What is NoSQL?
7.5 HBase DataModel
-Table and Row
– Column Family and Column Qualifier
– Cell and its Versioning
7.6 Categories of NoSQL Data Bases
– KeyValue Database
– Document Database
7.7 Column Family Database
7.8 HBASE Architecture
– HMaster
– Region Servers
– Regions
– MemStore
– Store SQL vs NOSQL
7.9 How HBASE is differ from RDBMS
7.10 HDFS vs HBase Client side buffering or bulk uploads
7.11 HBase Designing Tables
7.12 HBase Operations
– Get
– Scan
– Put
– Delete


Chapter-8: MongoDB

8.1 What is MongoDB?
8.2 Where to Use?
8.3 Configuration On Windows
8.4 Insertingthe data into MongoDB?
8.5 Reading the MongoDB data.


Chapter-9: Cluster Setup

9.1 Downloading and installing the Ubuntu12.x
9.2 Installing Java
9.3 Installing Hadoop
9.4 Creating Cluster
9.5 Increasing Decreasing the Cluster size
9.6 Monitoring the Cluster Health
9.7 Starting and Stoppingthe Nodes


Chapter-10: Zookeeper

10.1 Introduction Zookeeper
10.2 Data Modal
10.3 Operations


Chapter-11: OOZIE

11.1 Introduction to OOZIE
11.2 Use of OOZIE
11.3 Where to use?


Chapter-12: Flume

12.1 Introduction to Flume
12.2 Uses of Flume
12.3 Flume Architecture
– Flume Master
– Flume Collectors
– Flume Agents


Chapter-13: Impala

13.1 Over View
13.2 Data Load
13.3 Architecture
13.4 Hands-on
13.5 Hive vs Impala

2 Project Explanations with Architecture

Abhinandan Chakraborty

Guest Faculty (BigData, Hadoop)

Abhinandan, a B.Tech in Computer Science with around 4 Years of experience in Big Data live project Development, Java,Hadoop, MapReduce, Apache Hive,Impala,Apache Spark, HBase, Apache Flume, Apache Kafka, Apache Cassandra, Apache Storm,D3 .Have experience in Big Data live project development and POC. Currently employed with a well-known Big-Data consulting company in Sector-5, Salt Lake. At NIVT, in the last couple of years, he has trained many high profile MNC professionals on Big Data, Hadoop & Apache, Spark and created lots of references for NIVT in the industry.

Gunjan Bhadra

Guest Faculty (BigData, Hadoop/ Apache Spark)

Gunjan, a B.Tech in Information Technology with around 9 Years of experience in Software Industry, with nearly 3 years plus in the field of BigData, Hadoop and Apache Spark with Programing Knowledge on Java/Python. He has worked as a lead developer in several Big Data projects and delivered end-to-end solution to the clients. He is currently employed with a well-known Big-Data consulting company in Sector-5, Salt Lake as a Sr. Big Data Developer.