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Big Data and Hadoop

Become a Hadoop Expert by mastering MapReduce, Yarn, Pig, Hive, HBase, Oozie, Flume and Sqoop while working on industry based Use-cases and Projects.

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About The Course

Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases.

Course Objectives

At the end of the course, participants should be able to: 
1. Master the concepts of HDFS and MapReduce framework 
2. Understand Hadoop 2.x Architecture 
3. Setup Hadoop Cluster and write Complex MapReduce programs 
4. Learn the data loading techniques using Sqoop and Flume 
5. Perform Data Analytics using Pig, Hive and YARN 
6. Implement HBase and MapReduce Integration 
7. Implement Advanced Usage and Indexing 
8. Schedule jobs using Oozie 
9. Implement best Practices for Hadoop Development 
10. Work on a Real Life Project on Big Data Analytics

Who should go for this course?

Predictions say 2015 will be the year Hadoop finally becomes a cornerstone of your business technology agenda. To stay ahead in the game, Hadoop has become a must-know technology for the following professionals:
1. Analytics Professionals 
2. BI /ETL/DW Professionals 
3. Project Managers
4. Testing Professionals 
5. Mainframe Professionals 
6. Software Developers and Architects 
7. Graduates aiming to build a career in Big Data

Why learn Big Data and Hadoop?

Forrester predicts, CIOs who are late to the Hadoop game will finally make the platform a priority in 2015. Hadoop has evolved as a must-to-know technology and has been a reason for better career, salary and job opportunities for many professionals. 
The following blogs will help you understand the significance of Hadoop training: 

What are the pre-requisites for this Course?

Knowledge of core java concepts is the pre-requisite for this course. We provide a complimentary course i.e. "Java Essentials for Hadoop" to all the participants who enroll for the Hadoop Training.

How will I execute the Practicals?

For doing the practicals we will help you to setup Edureka's Virtual Machine in your System with local access. The detailed installation guides are provided in the LMS for setting up this environment. In case your system doesn't meet the pre-requisites e.g. 4GB RAM, you will be provided remote access to the Edureka cluster for the practicals. In case you come across any doubt, the 24*7 support team will promptly assist you.

Which Case-Studies will be a part of the Course?

Towards the end of the Course, you will be working on a live project where you will be using PIG, HIVE, HBase and MapReduce to perform Big Data analytics. 
Here are the few Industry-wise Big Data case studies e.g. Finance, Retail, Media, Aviation etc. which you can take up as your project work:

Project #1: Analyze social bookmarking sites to find insights
Industry: Social Media
Data: It comprises of the information gathered from sites like, etc which are bookmarking sites and allow you to bookmark, review, rate, search various links on any,, etc. A bookmarking site allows you to bookmark, review, rate, search various links on any topic. The data is in XML format and contains various links/posts URL, categories defining it and the ratings linked with it. 
Problem Statement: Analyze the data in Hadoop Eco-system to:
1. Fetch the data into Hadoop Distributed File System and analyze it with the help of MapReduce, Pig and Hive to find the top rated links based on the user comments, likes etc.
2. Using MapReduce convert the semi-structured format (XML data) into structured format and categorize the user rating as positive and  negative for each of the thousand links.
3. Push the output HDFS and then feed it into PIG, which splits the data into two parts: Category data and Ratings data.
4. Write a fancy Hive Query to analyze the data further and push the output is into relational database (RDBMS) using Sqoop.
5. Use a web server running on grails/java/ruby/python that renders the result in real time processing on a website.

Project #2: Customer Complaints Analysis
Industry: Retail
Data: Publicly available dataset, containing a few lakh observations with attributes like: CustomerId, Payment Mode, Product Details, Complaint, Location, Status of the complaint, etc. 
Problem Statement: Analyze the data in Hadoop Eco-system to:
1. Get the number of complaints filed under each products
2. Get the total number of complaints filed from a particular location
3. Get the list of complaints grouped by location which has no timely response

Project #3: Tourism Data Analysis
Industry: Tourism
Data: The dataset comprises attributes like: City pair (Combination of from and to), Adults traveling, Seniors traveling, Children traveling, Air booking price, Car booking price, etc.
Problem Statement: Find the following insights from the data:
1. Top 20 destinations people travel most : Based on given data we can find the most popular destinations where people travel frequently, based on the specific initial number of trips booked for a particular destination
2. Top 20 locations from where most of the trips start based on booked trip count
3. Top 20 high air-revenue destinations i.e which 20 cities generates high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations

Project #4: Airline Data Analysis
Industry: Aviation
Data: Publicly available dataset which contains the flight details of various airlines like : Airport id, Name of the airport, Main city served by airport, Country or territory where airport is located, Code of Airport, Decimal degrees, Hours offset from UTC,  Timezone, etc.
Problem Statement: Analyze the airlines data to:
1. Find list of Airports operating in the Country
2. Find the list of Airlines having zero stops
3. List of Airlines operating with code share
4. Which country (or) territory has the highest number of Airports
5. Find the list of Active Airlines in the United States

Project #5: Analyze Loan Dataset
Industry: Banking and Finance
Data: Publicly available dataset which contains complete details of all the loans issued, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information.
Problem Statement: Find the number of cases per location and categorize the count with respect to reason for taking loan and display the average risk score

Project #6: Analyze Movie Ratings
Industry: Media
Data: Publicly available data from sites like rotten tomatoes, imdb, etc.
Problem Statement: Analyze the movie ratings by different users to:
1. Get the user who has rated the most number of movies
2. Get the user who has rated the least number of movies
3. Get the count of total number of movies rated by user belonging to a specific occupation 
4. Get the number of under age users

Project #7: Analyze YouTube data
Industry: Social Media
Data: It is about the YouTube videos and contains attributes like : VideoID, Uploader, Age, Category, Length, views, ratings, comments, etc.
Problem Statement: Find out the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, the top 10 most viewed videos
Apart from these there are some twenty more use-cases to choose from :
Market data Analysis
Twitter Data Analysis 
Olympics Data Analysis etc.

Drop Us a Query:

Course Features:

  • +  Online Classes: 30 Hrs
  • 10 live classes of 3 hrs each by Industry practitioners

  • +  Assignments: 40 Hrs
  • Personal assistance/installation guides for setting up the required environment for Assignments / Projects

  • +  Project: 20 Hrs
  • Live project based on any of the selected use cases, involving Big Data Analytics using MapReduce, Pig, Hive, Flume and Sqoop

  • +  Lifetime Access
  • Lifetime access to the learning management system including Class recordings, presentations, sample code and projects

  • +  24 x 7 Support
  • Lifetime access to the support team (available 24/7) in resolving queries during and after the course completion

  • +  Get Certified
  • Edureka certified 'Big Data Expert' based on your project performance, reviewed by our expert panel