<ol><li> What is Big data? </li><li>Sources of Big data </li><li>Categories of Big data </li><li>Characteristics of Big data </li><li>Use-cases of Big data </li><li>Traditional RDBMS vs Hadoop </li><li>What is Hadoop? </li><li>History of Hadoop </li><li> Understanding Hadoop Architecture </li><li>Fundamental of HDFS (Blocks, Name Node, Data Node, Secondary Name Node) </li><li>Block Placement &amp;Rack Awareness </li><li>HDFS Read/Write </li><li>Drawback with 1.X Hadoop </li><li>Introduction to 2.X Hadoop </li><li>High Availability </li></ol>

Module 1 – Introduction to Big data & Hadoop

<ol><li> Making/creating directories </li><li>Removing/deleting directories </li><li>Print working directory </li><li>Change directory </li><li>Manual pages </li><li>Help </li><li>Vi editor </li><li>Creating empty files </li><li>Creating file contents </li><li>Copying file </li><li>Renaming files </li><li>Removing files </li><li>Moving files </li><li>Listing files and directories </li><li>Displaying file contents </li></ol>

Module 2 – Linux

HDFS is a distributed file system that handles large data sets running on commodity hardware. It is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes. HDFS is one of the major components of&nbsp;<a href="https://www.ibm.com/in-en/analytics/hadoop" rel="noopener noreferrer" target="_blank">Apache Hadoop</a>, the others being&nbsp;<a href="https://www.ibm.com/in-en/analytics/hadoop/mapreduce" rel="noopener noreferrer" target="_blank">MapReduce&nbsp;</a>and YARN. HDFS should not be confused with or replaced by&nbsp;<a href="https://www.ibm.com/in-en/analytics/hadoop/hbase" rel="noopener noreferrer" target="_blank">Apache HBase</a>, which is a column-oriented non-relational database management system that sits on top of HDFS and can better support real-time data needs with its in-memory processing engine. <h4>What we will cover?</h4><ol><li>Understanding Hadoop configuration files </li><li>Hadoop Components- HDFS, MapReduce </li><li>Overview of Hadoop Processes </li><li>Overview of Hadoop Distributed File System </li><li>The building blocks of Hadoop </li><li>Hands-On Exercise: Using HDFS commands </li></ol>

Module 3 – HDFS

o MapReduce Introduction o How MapReduce works? o Communication between JobTracker and TaskTracker - Anatomy of a Map Reduce Job Submission ﻿

Module 4 – Map Reduce

Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. <h4>What we will cover?</h4> <ol><li>What is hive? </li><li>Why hive? </li><li>What hive is not? </li><li>Meta store DB in hive </li><li>Architecture of hive </li><li>Internal table </li><li>External table </li><li>Hive operations </li><li>Static Partition </li><li>Dynamic Partition </li><li>Bucketing </li><li>Bucketing with sorting </li><li>File formats </li><li>Hive performance tuning </li></ol>

Module 5 - Hive

Big data is a term that describes large, hard-to-manage volumes of data – both structured and unstructured 

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