Big Data and Hadoop Training Course

Curriculum

1 Introduction to Big Data and Hadoop
Learning objectives:
This module will introduce you to the various concepts of big data analytics, and the seven Vs of big data—Volume, Velocity, Veracity, Variety, Value, Vision, and Visualization. Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3.

Topics:

• Understanding Big Data
• Types of Big Data
• Difference between Traditional Data and Big Data
• Introduction to Hadoop
• Distributed Data Storage In Hadoop, HDFS and Hbase
• Hadoop Data processing Analyzing Services MapReduce and spark, Hive Pig and Storm
• Data Integration Tools in Hadoop
• Resource Management and cluster management Services

2 Big Data Ecosystem
Learning Objectives:
Here you will learn the features in Hadoop 3.x and how it improves reliability and performance. Also, get introduced to MapReduce Framework and know the difference between MapReduce and YARN.

Topics:

• Need of Hadoop in Big Data
• Understanding Hadoop And Its Architecture
• The MapReduce Framework
• What is YARN?
• Understanding Big Data Components
• Monitoring, Management and Orchestration Components of Hadoop Ecosystem
• Different Distributions of Hadoop
• Installing Hadoop 3

Hands-on:
Install Hadoop 3.x

3 Hadoop Cluster Configuration
Learning Objectives:
Learn to install and configure a Hadoop Cluster.

Topics:

• Hortonworks sandbox installation & configuration
• Hadoop Configuration files
• Working with Hadoop services using Ambari
• Hadoop Daemons
• Browsing Hadoop UI consoles
• Basic Hadoop Shell commands
• Eclipse & winscp installation & configurations on VM

Hands-on:
Install and configure eclipse on VM

4 Big Data Processing with MapReduce
Learning Objectives:
Learn about various components of the MapReduce framework, and the various patterns in the MapReduce paradigm, which can be used to design and develop MapReduce code to meet specific objectives.

Topics:

• Running a MapReduce application in MR2
• MapReduce Framework on YARN
• Fault tolerance in YARN
• Map, Reduce & Shuffle phases
• Understanding Mapper, Reducer & Driver classes
• Writing MapReduce WordCount program
• Executing & monitoring a Map Reduce job

Hands-on:
Use case - Sales calculation using M/R

5 Batch Analytics with Apache Spark
Learning Objectives:
Learn about Apache Spark and how to use it for big data analytics based on a batch processing model. Get to know the origin of DataFrames and how Spark SQL provides the SQL interface on top of DataFrame.

Topics:

• SparkSQL and DataFrames
• DataFrames and the SQL API
• DataFrame schema
• Datasets and encoders
• Loading and saving data
• Aggregations
• Joins

Hands-on:
Look at various APIs to create and manipulate DataFrames and dig deeper into the sophisticated features of aggregations, including groupBy, Window, rollup, and cubes. Also look at the concept of joining datasets and the various types of joins possible such as inner, outer, cross, and so on

6 Real Time Analytics with Apache Spark
Learning Objectives:
Understand the concepts of the stream-processing system, Spark Streaming, DStreams in Apache Spark, DStreams, DAG and DStream lineages, and transformations and actions.

Topics:

• A short introduction to streaming
• Spark Streaming
• Discretized Streams
• Stateful and stateless transformations
• Checkpointing
• Operating with other streaming platforms (such as Apache Kafka)
• Structured Streaming

Hands-on: Process Twitter tweets using Spark Streaming

7 Analysis using Pig
Learning Objectives:
Learn to simplify Hadoop programming to create complex end-to-end Enterprise Big Data solutions with Pig.

Topics:

• Background of Pig
• Pig architecture
• Pig Latin basics
• Pig execution modes
• Pig processing – loading and transforming data
• Pig built-in functions
• Filtering, grouping, sorting data
• Relational join operators
• Pig Scripting
• Pig UDF's

8 Analysis using Hive Data Warehousing Infrastructure
Learning Objectives:
Learn about the tools to enable easy data ETL, a mechanism to put structures on the data, and the capability for querying and analysis of large data sets stored in Hadoop files.

Topics:

• Background of Hive
• Hive architecture
• Hive Query Language
• Derby to MySQL database
• Managed & external tables
• Data processing – loading data into tables
• Hive Query Language
• Using Hive built-in functions
• Partitioning data using Hive
• Bucketing data
• Hive Scripting
• Using Hive UDF's

9 Working with HBase
Learning Objectives:
Look at demos on HBase Bulk Loading & HBase Filters. Also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.

Topics:      

• HBase overview
• Data model
• HBase architecture
• HBase shell
• Zookeeper & its role in HBase environment
• HBase Shell environment
• Creating table
• Creating column families
• CLI commands – get, put, delete & scan
• Scan Filter operations

10 Importing and Exporting Data using Sqoop
Learning Objectives:
Learn how to import and export data between RDBMS and HDFS.

Topics:

• Importing data from RDBMS to HDFS
• Exporting data from HDFS to RDBMS
• Importing & exporting data between RDBMS & Hive tables

11 Oozie Workflow Management and Using Flume for Analyzing Streaming Data
Learning Objectives:
Understand how multiple Hadoop ecosystem components work together to solve Big Data problems. This module will also cover Flume demo, Apache Oozie Workflow Scheduler for Hadoop Jobs.

Topics:

• Overview of Oozie
• Oozie Workflow Architecture
• Creating workflows with Oozie
• Introduction to Flume
• Flume Architecture
• Flume Demo

12 Visualizing Big Data
Learning Objectives:
Learn to constantly make sense of data and manipulate its usage and interpretation; it is easier if we can visualize the data instead of reading it from tables, columns, or text files. We tend to understand anything graphical better than anything textual or numerical.

Topics:

• Introduction
• Tableau
• Chart types
• Data visualization tools

Hands-on:
Use Data Visualization tools to create a powerful visualization of data and insights.

13 Introducing Cloud Computing
Learning Objectives:
Learn a simple way to access servers, storage, databases, and a broad set of application services over the internet.

Topics:

• Cloud computing basics
• Concepts and terminology
• Goals and benefits
• Risks and challenges
• Roles and boundaries
• Cloud characteristics
• Cloud delivery models
• Cloud deployment models

Hands-on:
Implement Cloud computing and deploy models.