#Apache Hadoop

• Hadoop Distributed File System - Datanode, Namenode, Secondary Namenode

• MapReduce - Map phase, Reduce phase
• Yarn - Node manager, Resource manager
• Hive - MetaStore, Driver, Query compiler, Hive server
• Pig
• HBase - HBase server, Region server
• Mahout
• Zookeeper
• Oozie
• Sqoop
• Flume - Source, Channel, Sink
• Ambari
• Apache Drill
• Apache Spark
• Solr And Lucene
• Scala
• Presto

Scikit and Introduction to Hadoop

• Introduction to Scikit-Learn
• Inbuilt Algorithms for Use
• What is Hadoop and why it is popular
• Distributed Computation and Functional Programming
• Understanding MapReduce Framework Sample MapReduce Job Run

Hadoop and Python

• PIG and HIVE Basics
• Streaming Feature in Hadoop
• Map Reduce Job Run using Python
• Writing a PIG UDF in Python
• Writing a HIVE UDF in Python
• Pydoop and MRjob Basics

HADOOP

• Big Data and Hadoop Introduction
• What is Big Data and Hadoop?
• Challenges of Big Data
• Traditional approach Vs Hadoop
• Hadoop Architecture
• Distributed Model
• Block structure File System
• Technologies supporting Big Data
• Replication
• Fault Tolerance
• Why Hadoop?

• Hadoop Eco-System

• Use cases of Hadoop
• Hadoop Ecosystem

• Fundamental Design Principles of Hadoop

• Comparison of Hadoop Vs RDBMS

Hadoop Cluster Architecture

• Hadoop Cluster and Architecture

• 5 Daemons
• Hands-On Exercise
• Typical Workflow
• Hands-On Exercise
• Writing Files to HDFS
• Hands-On Exercise
• Reading Files from HDFS
• Hands-On Exercise
• Rack Awareness
• Before Map Reduce

Module-9

• Joins & Sub queries, Views
• Integration, Data manipulation with Hive
• User Defined Functions
• Appending Data into existing Hive Table
• Static partitioning vs dynamic partitioning