Complete knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet
• Another Resource Negotiator) and MapReduce
• Comprehensive knowledge of various tools that is a part of Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase
• Capability to ingest data in HDFS using Sqoop and Flume, and analyze those large datasets stored in the HDFS.
• The exposure to many real world industry-based projects which will be executed in CloudLab
• Projects which are very diverse i.e. different from each other covering various data sets from multiple domains such as banking, telecommunication, social media, insurance, and e-commerce.

• Introduction to Data and System
• Types of Data
• Traditional way of dealing large data and its problems
• Types of Systems & Scaling
• What is Big Data
• Challenges in Big Data
• Challenges in Traditional Application
• New Requirements
• What is Hadoop? Why Hadoop?
• Brief history of Hadoop
• Features of Hadoop
• Hadoop and RDBMS
• Hadoop Ecosystem’s overview

• Installation in detail
• Creating Ubuntu image in VMware
• Downloading Hadoop
• Installing SSH
• Configuring Hadoop, HDFS & MapReduce
• Download, Installation & Configuration Hive
• Download, Installation & Configuration Pig
• Download, Installation & Configuration Sqoop
• Download, Installation & Configuration Hive
• Configuring Hadoop in Different Modes

• File System – Concepts
• Blocks
• Replication Factor
• Version File
• Safe mode
• Namespace IDs
• Purpose of Name Node
• Purpose of Data Node
• Purpose of Secondary Name Node
• Purpose of Job Tracker
• Purpose of Task Tracker
• HDFS Shell Commands – copy, delete, create directories etc.
• Reading and Writing in HDFS
• Difference of Unix Commands and HDFS commands
• Hadoop Admin Commands
• Hands on exercise with Unix and HDFS commands
• Read / Write in HDFS – Internal Process between Client, NameNode & DataNodes
• Accessing HDFS using Java API
• Various Ways of Accessing HDFS
• Understanding HDFS Java classes and methods.

• About MapReduce
• Understanding block and input splits
• MapReduce Data types
• Understanding Writable
• Data Flow in MapReduce Application
• Understanding MapReduce problem on datasets
• MapReduce and Functional Programming
• Writing MapReduce Application
• Understanding Mapper function
• Understanding Reducer Function
• Understanding Driver
• Usage of Combiner
• Usage of Distributed Cache
• Passing the parameters to mapper and reducer
• Analysing the Results
• Log files
• Input Formats and Output Formats
• Counters, Skipping Bad and unwanted Records
• Writing Join’s in MapReduce with 2 Input files. Join Types
• Execute MapReduce Job – Insights
• Exercise’s on MapReduce

• Hive concepts
• Hive architecture
• Install and configure hive on cluster
• Different type of tables in hive
• Hive library functions
• Buckets
• Partitions
• Joins in hive
• Inner joins
• Outer Joins
• Hive UDF
• Hive Query Language

• Pig basics
• Install and configure PIG on a cluster
• PIG Library functions
• Pig Vs Hive
• Write sample Pig Latin scripts
• Modes of running PIG
• Running in Grunt shell
• Running as Java program
• PIG UDFs

• HBase concepts
• HBase architecture
• Region server architecture
• File storage architecture
• HBase basics
• Column access
• Scans
• HBase use cases
• Install and configure HBase on a multi node cluster
• Create database, Develop and run sample applications
• Access data stored in HBase using Java API
• Map Reduce client to access the HBase data

• Install and configure Sqoop on cluster
• Connecting to RDBMS
• Installing Mysql
• Import data from Mysql to hive
• Export data to Mysql
• Internal mechanism of import/export

• Resource Manager (RM)
• Node Manager (NM)
• Application Master (AM)