Course to maximize your Development & Data Analysis skills on massive data sets in the Hadoop Cluster using SQL and known Scripting languages. 

Target Audience: BI Analysts, BI Developers, Data Analysts, Business Analysts, Quality Analysts, Programmers and Beginners.

MODULE-1: BIG DATA & HADOOP INTRODUCTION

• State of Data
• Big Data Evolution (Volume, Velocity & Variety)
• The Motivation for Hadoop
• Hadoop Distribution (Cloudera, Hortonworks, Map R, IBM, MicroSoft, Amazon etc.)
• Enterprise, Cloud & Local Hadoop

MODULE-2: HADOOP ECO-SYSTEM

• Hadoop Evolution (Gen 1 vs Hadoop Gen 2)
• Hadoop Technology Stack
• Hadoop Core (Common) / Projects / Incubator
• Modern Data Architecture with existing Data Repositories

MODULE-3: HADOOP LOCAL INSTALLATION

• Cloudera Distributed Hadoop (CDH VM)
• HortonWorks Data Platform (HDP)
• Apache Hadoop Overview

MODULE-4: HADOOP DISTRIBUTED FILE SYSTEM (HDFS)

• Data Storage: HDFS
• HDFS Architecture (Name Node, Data Node & Secondary Name Node)
• HDFS Features & Internals
• HDFS Interaction & Management
• HDFS LAB Sessions

MODULE-5: MAPREDUCE & YARN

• Distributed Data Processing: MapReduce, Yet Another Resource Negotiator (YARN)
• MapReduce Architecture (Job Client, Job Tracker, Task Tracker)
• MapReduce Internals (Input, Split, Map, Combine, Shuffle, Sort, Reduce, Output)
• Classic MapReduce (Map Reduce 1) vs YARN (MapReduce 2)
• YARN Architecture (Resource Manager, Node Manager, Application Master)
• MapReduce LAB Sessions with Java Programs

MODULE-6: HIVE INTRODUCTION

• Hive Architecture Overview
• Installing and Running Hive 
• Schema and Data Storage
• Hive Principles - Schema on Read & The Hive Warehouse
• Hive vs. Traditional Relational Databases
• Hive Access Tools (Shell, Web UI, Thrift Client, JDBC/ODBC Driver)
• Hive Services
• Hive Meta Store
• Use Cases

MODULE-7: DEVELOPING WITH HIVE

• Hive Query Language (HiveQL)
• Using Command Line Interface (CLI) and Hue UI to Execute Queries
• Data Types & Type Conversions
• Data Storage & Managing Metadata
• Creating / Altering Databases and Tables 
• Loading Data - Hive Managed and External Tables
• Simplifying Queries with Views
• Joining Datasets (Inner, Outer, Semi & Map)
• Built-In Functions
• Aggregation, Windowing and Analytics Functions
• User Defined Functions (Java) & Streaming (Python) from HiveQL
• SerDe, Performance & Security
• HIVE LAB Sessions

MODULE-8: PIG INTRODUCTION

• Pig Overview
• Installing and Running Pig
• Pig's Features & Use Cases
• Pig Data Model, Execution Modes and Methods
• Pig (Procedural) Vs Hive (Declarative)

MODULE-9: DEVELOPING WITH PIG LATIN

• Pig Latin Basics
• Data Types and Storage Formats
• Loading and Storing Data
• Filtering, Sorting, Grouping & Iterating Grouped Data
• Joining and Splitting Data Sets
• Set Operations
• Commonly-Used Built-In Functions
• Develop User-Defined Functions (Java) and Macros and invoking them from Pig
• Parameter Substitution Methods
• Troubleshooting, Debugging and Logging Pig
• PIG LAB Session

MODULE-10: HADOOP DATA INTEGRATION, SCHEDULING & OPERATIONS (SQOOP & OOZIE) INTRODUCTION

• Data Import / Export between Relational Databases and HDFS / HIVE
• Workflow Development
• Use Cases

MODULE-11: CHOOSING THE  BEST TOOL FOR THE REQUIREMENT

• Comparing MapReduce, Pig, Hive, Impala and Relational Databases

Classes: 22-25 Hours