Outcomes:

• Understand business analytics and big data technologies
• Learning data mining concepts, techniques through an open source DM tool
• Understand the role of big data technologies (Hadoop, HBase, Hive) in business analytics
• Acquire the knowledge and learn to use Hadoop (HDFS and MapReduce), HBase and Hive

Course Curriculum

Module 1: Fundamentals of Big data

Module 2: Big Data Management

• Distributed file systems for big data storage, access, and analytics
• Frameworks and tools for big data cyber security analytics
• Performance modelling, simulation and analysis
• Big data applications in cyber security
• Parallel and distributed algorithms for big data analytics
• Big data case studies and applications

Module 3: HADOOP

• Different Components of Hadoop
• Introduction to Apache Pig
• Map Reduce vs. Apache Pig
• SQL vs. Apache Pig
• Different Data Types in Pig
• Modes of Execution in Pig
• Execution Mechanism
• Transformations in Pig
• How to Write a Simple Pig script
• UDFs in Pig

Module 4: HDFS (Hadoop Distributed File System)

• Significance of HDFS in Hadoop
• Features of HDFS
• 5 daemons of Hadoop

Module 5: HIVE

• HIVE Introduction
• HIVE Architecture
• HIVE Meta Store
• HIVE Integration with Hadoop

Module 6: MAP REDUCE

• MapReduce Architecture
• MapReduce Programming Mode
• Different Phases of MapReduce Algorithm
• Different Data Types in MapReduce
• How to Write a Basic MapReduce Program
• Joining Datasets In MapReduce Jobs – Map Joins and Reduce Joins
• Creating Input and Output Formats in MapReduce Jobs
• How to Debug MapReduce Jobs in Local and Pseudo Cluster Mode
• Data Localization in MapReduce
• Combiner (Mini Reducer and Partitioner)

Module 7: Hbase

• Overview
• Instalation
• Hbase Shell
• Hbase General Camand

Module 8: Sqoop

• What sqoop does
• Data Imports
• Parallel data transfer
• Fast Data copies
• Projects