Big Data andHadoop

Module 1: The big picture of Big Data

This module introduces you to the rudiments of Big Data (data sets too large/ complex for

traditional data processing application software), why we choose to adopt it, and its different

dimensions and implementations. It also discusses its future in the industry.

What is Big Data

Necessity of Big Data in the industry

Paradigm shift - why the industry is shifting to Big Data tools

Different dimensions of Big Data

Data explosion in the industry

Various implementations of Big Data

Different technologies to handle Big Data

Traditional systems and associated problems

Future of Big Data in the IT industry

Module 2: Demystifying Hadoop

We then introduce you to the Hadoop framework, its architecture and design principles, and its

ingredients. This familiarizes you with the Hadoop ecosystem and its components. Finally, we

talk of various flavors of Hadoop.

Why Hadoop is at the heart of every Big Data solution

Introduction to the Hadoop framework

Hadoop architecture and design principles

Ingredients of Hadoop

Hadoop characteristics and data-flow

Components of the Hadoop ecosystem

Hadoop Flavors – Apache, Cloudera, Hortonworks, and more

Module 3: Setup and Installation of Hadoop

This module deals with setting up and installing both single- and multi-node clusters. It teaches

you to configure Hadoop, run it in various modes, and troubleshoot problems observed. You will

also learn how to configure masters and slaves on the cluster.

Setup and Installation of single-node Hadoop cluster

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Hadoop environment setup and pre-requisites

Installation and configuration of Hadoop

Working with Hadoop in pseudo-distributed mode

Troubleshooting encountered problems

Setup and Installation of Hadoop multi-node cluster

Hadoop environment setup on the cloud (Amazon cloud)

Installation of Hadoop pre-requisites on all nodes

Configuration of masters and slaves on the cluster

Playing with Hadoop in distributed mode

Module 4: HDFS – The Storage Layer

Next, we discuss HDFS( Hadoop Distributed File System), its architecture and mechanisms,

and its characteristics and design principles. We also take a good look at HDFS masters and

slaves. Finally, we discuss terminologies and some best practices.

What is HDFS (Hadoop Distributed File System)

HDFS daemons and architecture

HDFS data flow and storage mechanism

Hadoop HDFS characteristics and design principles

Responsibility of HDFS Master – NameNode

Storage mechanism of Hadoop meta-data

Work of HDFS Slaves – DataNodes

Data Blocks and distributed storage

Replication of blocks, reliability, and high availability

Rack-awareness, scalability, and other features

Different HDFS APIs and terminologies

Commissioning of nodes and addition of more nodes

Expanding clusters in real-time

Hadoop HDFS Web UI and HDFS explorer

HDFS best practices and hardware discussion

Module 5: A Deep Dive into MapReduce

After finishing this module, you will be comfortable with MapReduce, the processing layer of

Hadoop, and will be aware of its need, components, and terminologies. MapReduce lets you

process and generate big data sets with a parallel, distributed algorithm on a cluster with map

and reduce methods. We will demonstrate using examples as we move on to optimization of

MapReduce jobs and will introduce you to combiners as we move on to the next module.

What is MapReduce, the processing layer of Hadoop

The need for a distributed processing framework

Issues before MapReduce and its evolution

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List processing concepts

Components of MapReduce – Mapper and Reducer

MapReduce terminologies- keys, values, lists, and more

Hadoop MapReduce execution flow

Mapping and reducing data based on keys

MapReduce word-count example to understand the flow

Execution of Map and Reduce together

Controlling the flow of mappers and reducers

Optimization of MapReduce Jobs

Fault-tolerance and data locality

Working with map-only jobs

Introduction to Combiners in MapReduce

How MR jobs can be optimized using combiners

Module 6: MapReduce – Advanced Concepts

Time to dig deeper into MapReduce! This module takes you to more advanced concepts of

MapReduce- those like its data types and constructs like InputFormat and RecordReader.

Anatomy of MapReduce

Hadoop MapReduce data types

Developing custom data types using Writable & WritableComparable

InputFormat in MapReduce

InputSplit as a unit of work

How Partitioners partition data

Customization of RecordReader

Moving data from mapper to reducer – shuffling & sorting

Distributed cache and job chaining

Different Hadoop case-studies to customize each component

Job scheduling in MapReduce

Module 7: Hive – Data Analysis Tool

Halfway through the course now, we begin to explore Hive, a data warehouse software project.

We take a look at its architecture, various DDL and DML operations, and meta-stores. Then, we

talk of where this would be useful. Finishing this module, you will be able to perform data query

and analysis.

The need for an adhoc SQL based solution – Apache Hive

Introduction to and architecture of Hadoop Hive

Playing with the Hive shell and running HQL queries

Hive DDL and DML operations

Hive execution flow

Schema design and other Hive operations

Schema-on-Read vs Schema-on-Write in Hive

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Meta-store management and the need for RDBMS

Limitations of the default meta-store

Using SerDe to handle different types of data

Optimization of performance using partitioning

Different Hive applications and use cases

Module 8: Pig – Data Analysis Tool

This module teaches you all about Pig, a high-level platform for developing programs for

Hadoop. We will take a look at its execution flow and various operations, and will then compare

it to MapReduce. Pig can execute its jobs in MapReduce.

The need for a high level query language - Apache Pig

How Pig complements Hadoop with a scripting language

What is Pig

Pig execution flow

Different Pig operations like filter and join

Compilation of Pig code into MapReduce

Comparison - Pig vs MapReduce

Module 9: NoSQL Database – HBase

We move on to HBase, an open-source, non-relational, distributed NoSQL database. In this

module, we talk of its rudiments, architecture, datastores, and the Master and Slave model. We

also compare it to both HDFS and RDBMS. Finally, we discuss data access mechanisms.

NoSQL databases and their need in the industry

Introduction to Apache HBase

Internals of the HBase architecture

The HBase Master and Slave Model

Column-oriented, 3-dimensional, schema-less datastores

Data modeling in Hadoop HBase

Storing multiple versions of data

Data high-availability and reliability

Comparison - HBase vs HDFS

Comparison - HBase vs RDBMS

Data access mechanisms

Working with HBase using the shell

Module 10: Data Collection using Sqoop

With Apache Sqoop, you can always go about another helping of data from a relational

database into Hadoop or the other way around. This is a command-line interface application.

The need for Apache Sqoop

Introduction and working of Sqoop

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Importing data from RDBMS to HDFS

Exporting data to RDBMS from HDFS

Conversion of data import/export queries into MapReduce jobs

Module 11: Data Collection using Flume

Apache Flume is a reliable distributed software that lets us efficiently collect, aggregate, and

move large amounts of log data. Here, we talk about its architecture and various tools it has to offer.

What is Apache Flume

Flume architecture and aggregation flow

Understanding Flume components like data Sources and Sinks

Flume channels to buffer events

Reliable & scalable data collection tools

Aggregating streams using Fan-in

Separating streams using Fan-out

Internals of the agent architecture

Production architecture of Flume

Collecting data from different sources to Hadoop HDFS

Multi-tier Flume flow for collection of volumes of data using AVRO

Module 12: Apache YARN & advanced concepts in the latest version

Version 2 of Hadoop brought with it Yet Another Resource Negotiator (YARN). It will allow you

to efficiently allocate resources.

The need for and the evolution of YARN

YARN and its eco-system

YARN daemon architecture

Master of YARN – Resource Manager

Slave of YARN – Node Manager

Requesting resources from the application master

Dynamic slots (containers)

Application execution flow

MapReduce version 2 application over Yarn

Hadoop Federation and Namenode HA

Module 13: Processing data with Apache Spark

This module deals with Apache Spark and its features. This is an open-source distributed

general-purpose cluster-computing framework. We also discuss RDDs (Resilient Distributed

Datasets) and their operations. Then, we understand the Spark programming model and the

entire ecosystem.

Introduction to Apache Spark

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Comparison - Hadoop MapReduce vs Apache Spark

Spark key features

RDD and various RDD operations

RDD abstraction, interfacing, and creation of RDDs

Fault Tolerance in Spark

The Spark Programming Model

Data flow in Spark, The Spark Ecosystem

Hadoop compatibility, & integration

Installation & configuration of Spark

Processing Big Data using Spark

Module 14: Real-Life Project on Big Data

We conclude this course with a live Hadoop project to prepare you for the industry. Here, we

make use of various Hadoop components like Pig, HBase, MapReduce, and Hive to solve realworld

problems in Big Data Analytics.

Web Analytics - Weblogs are web server logs where web servers like Apache record all

events along with a remote IP, timestamp, requested resource, referral, user agent, and

other such data. The objective is to analyze weblogs to generate insights like user

navigation patterns, top referral sites, and highest/lowest traffic-times.

Sentiment Analysis - Sentiment analysis is the analysis of people’s opinions, sentiments,

evaluations, appraisals, attitudes, and emotions in relation to entities like individuals,

products, events, services, organizations, and topics. It is achieved by classifying the

observed expressions as opinions positive or negative.

Crime Analysis - Learn to analyze US crime data and find the most crime-prone areas

along with the time of crime and its type. The objective is to analyze crime data and

generate patterns like time of crime, district, type of crime, latitude, and longitude. This is

to ensure that additional security measures can be taken in crime-prone areas.

IVR Data Analysis - Learn to analyze IVR(Interactive Voice Response) data and use it to

generate multiple insights. IVR call records are meticulously analyzed to help with

optimization of the IVR system in an effort to ensure that maximum calls complete at the

IVR itself, leaving no room for the need for a call-center.

Titanic Data Analysis - Titanic was one of the most colossal disasters in the history of

mankind, and it happened because of both natural events and human mistakes. The

objective of this project is to analyze multiple Titanic data sets to generate essential

insights pertaining to age, gender, survived, class, and embarked.

And so many more projects of retail, telecom, media, etc..