Edureka Big Data and Hadoop Tutorial
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The Big Data and Hadoop training course from Edureka is designed to enhance your 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
By the end of the course, you will:
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 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
11. Understand Spark and its Ecosystem
12. Learn how to work in RDD in Spark
Who should go for this Course?
Today, Hadoop has become a cornerstone of every business technology professional. 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 successful career around Big Data
Why learn Big Data and Hadoop?
CIOs are making Hadoop their platform of choice in 2016. For better career prospects, bigger job opportunities and financial growth, Hadoop is a must-know.
Check out these Edureka blogs to know why Hadoop training is critical:
How essential is Hadoop training?
Gartner Predicts How Big Data Can Lead Economic Growth
What are the pre-requisites for this Course?
You can master Hadoop, irrespective of your IT background. While basic knowledge of Core Java and SQL might help, it is not a pre-requisite for learning Hadoop.
In case you wish to brush-up your Java skills, Edureka offers you a complimentary self-paced course: “Java essentials for Hadoop”.
How will I execute the Practicals?
For 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 your 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. In case you experience any issues, our 24*7 support team will be happy to 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-specific Big Data case studies e.g. Finance, Retail, Media, Aviation etc. which you can consider foryour project work:
Project #1: Analyze social bookmarking sites to find insights
Industry: Social Media
Data: It comprises of the information gathered from sites like reddit.com, stumbleupon.com which are bookmarking sites and allow you to bookmark, review, rate, search various links on any topic.reddit.com, stumbleupon.com, 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 the Hadoop ecosystem to:
1. Fetch the data into a 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 a 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 the Hadoop ecosystem to:
1. Get the number of complaints filed under each product
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 frequently travel to: 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 the 20 cities that generate 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 such as: 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 underage users
Project #7: Analyze YouTube data
Industry: Social Media
Data: It is about the YouTube videos and contains attributes such as: VideoID, Uploader, Age, Category, Length, views, ratings, comments, etc.
Problem Statement: Identify the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, and the top 10 most viewed videos.
Apart from these there are some twenty more use-cases to choose:
Market data Analysis
Twitter Data Analysis
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