Bigdata & Hadoop Training

Course Content

Module 1 –Introduction to Data Ware Housing & Business Intelligence

• What is Data Warehouse ?
• Data Warehouse Architecture
• Data Warehouse Vs Data Mart
• OLTP Vs OLAP
• Data Modeling
• Relational
• Dimensional 
• Star Schema / Snowflake Schema
• Normalization
• Data Normalization
• Data De-Normalization
• Dimension Table
• Categories – Normal & Confirmed Dimension
• Slowly Changing Dimension - Type 1, Type 2 & Type 3
• Level & Hierarchy
• Fact Table
• Categories - Summary / Aggregations table
• Type
• Additive
• Semi-Additive
• Non-Additive
• Real Time Data ware housing - Change Data capture
• What is Business Intelligence?

Module 2 – Introduction to Big Data & Hadoop

• What is Big Data ?
• Limitations and Solutions of existing Data Analytics Architecture
• Hadoop&Hadoop Features
• Hadoop Ecosystem
• Hadoop 2.x core components
• Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework
• Anatomy of File Write and Read Awareness

Module 3 – Hadoop Architecture,Installation, Setup& Configuration

• Hadoop 2.x Cluster Architecture - Federation and High Availability
• Hadoop Cluster Modes
• Common Hadoop Shell Commands
• Hadoop 2.x Configuration Files
• Hadoop Job Processes
• MapReduce Job Execution

Module 4 –Hadoop MapReduce & YARN Architecture &Framework

• MapReduce Framework
• Traditional way Vs MapReduce way
• Hadoop 2.x MapReduce Architecture&Components
• YARN Architecture, Components &Workflow
• Anatomy of MapReduce Program
• MapReduce Program 

Module 5 –Sqoop & Flume

• What is Sqoop ?
• Sqoop Installations and Basics
• Importing Data from RDBMS / MySQL to HDFS
• Exporting Data from HDFS to RDBMS / MySQL
• Parallelism
• Importing data from RDBMS / MySQL to Hive
• What is Flume ?
• Flume Model and Goals
• Features of Flume

Module 6 – Pig

• What is Pig ?
• MapReduce Vs Pig
• Pig Use Cases
• Programming Structure in Pig
• Pig Running Modes
• Pig Components
• Pig Execution
• Pig Data Types
• Relational &Group Operators, File Loaders, Union &Joins, Diagnostic Operators& UDF

Module 7 –Hive

• What is Hive ?
• Hive Vs Pig
• Hive Architecture and Components its Limitations
• Metastore in Hive
• Comparison with Traditional Database
• Hive Data Types, Data Models,Partitions and Buckets
• Hive Tables (Managed Tables and External Tables)
• Importing, Querying Data & Managing Outputs
• Hive Script & UDF

Module 8 –Hbase

• HBase Data Model
• HBase Shell
• HBase Client API
• Data Loading Techniques
• ZooKeeper Data Model
• Zookeeper Service
• Zookeeper
• Data Handling
• HBase Filters

Module 9 –Spark

• What is Spark?
• What is Spark Architecture & Components
• Spark Algorithms-Iterative Algorithms, Graph Analysis, Machine Learning
• Spark Core
• Spark Libraries
• Spark Demo

Module 10 –Big Data & Hadoop 10 Project – Sales Analytics

• Towards the end of the course, you will work on a LIVE project where you will be using Sqoop, Flume, PIG, HIVE, Hbase, MapReduce& Spark to perform Big Data Analytics

• You will use the industry-specific Big Data case studies that are included in our Big Data and Hadoop

• You will gain in-depth experience in working with Hadoop & Big Data

• Understand your sales pipeline and uncover what can lead to successful sales opportunities and better anticipate performance gap

• Review product-related information like Cost, Revenue, Price, etc. across Years and Ordering Method. This dataset could also be used in the Explore feature to better understand the hidden trends & patterns

Training Objectives

• Big Data & Hadoop concepts with HDFS and Map Reduce framework& its Architecture
• Setup Hadoop Cluster and write Complex MapReduce programs
• Learn data loading / Ingestion techniques using Sqoop, Flume&HBase
• Perform data analytics using Pig, Hive and YARN and scheduling jobs using Oozie
• Understand Spark and its Eco system &Learn how to work in RDD in Spark
• Work on a real life Project on Big Data Analytics

Pre-Requisites

• As such, there are no pre-requisites for learning Hadoop, Knowledge of Core Java and SQL Basics will be beneficial, but certainly not a mandate. 

• Market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. 

• Here are the few IT Professional, who are continuously enjoying the benefits moving into Big data domain:
• Developers, Java Programmers and Architects
• BI /ETL/DW professionals
• Senior IT Professionals
• Testing professionals
• Mainframe professionals
• Freshers

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