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Big Data Hadoop Certification Training

Big Data Hadoop training will make you an expert in HDFS, MapReduce, Hbase, Hive, Pig, Yarn, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain. You will get edureka Hadoop certification at the end of the course.


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Why this course ?

  • Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1% -Forbes
  • McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
  • Average Salary of Big Data Hadoop Developers is $135k (Indeed.com salary data)
  • 121K + satisfied learners. Reviews

Instructor-led live online classes

23

Jun
Fri - Sat ( 5 Weeks )
09:30 PM - 12:30 AM ( EDT )
19995

24

Jun
Sat - Sun ( 5 Weeks )
11:00 AM - 02:00 PM ( EDT )
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25

Jun
Sun - Thu ( 15 Days )
09:30 PM - 11:30 PM ( EDT )
19995

26

Jun
Mon - Fri ( 15 Days )
11:00 AM - 01:00 PM ( EDT )
19995

Early Bird Offer

14

Jul
Fri - Sat ( 5 Weeks )
09:30 PM - 12:30 AM ( EDT )
10% Off
19995
17995
10% Early Bird Off till 25th June

17

Jul
Mon - Fri ( 15 Days )
11:00 AM - 01:00 PM ( EDT )
10% Off
19995
17995
10% Early Bird Off till 25th June

Edureka For Business

Train your employees with exclusive batches and offers and track your employee's progress with our weekly progress report.

Instructor-led Sessions

30hrs of Online Live Instructor-led Classes. Weekend class:10 sessions of 3 hours each and Weekday class:15 sessions of 2 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving Big Data Analytics.

Assignments

Each class will be followed by practical assignments which can be completed before the next class.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have 24x7 online support team available to help you with any technical queries you may have during the course.

Certification

Towards the end of the course, you will be working on a project. Edureka certifies you as an Big Data and Hadoop Expert based on the project.

Forum

We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.

This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on a Big data Analytics project after selecting a data-set of your choice. You will get edureka Hadoop certification after the project completion.

The edureka hadoop training is designed to help you become a top Hadoop developer. During this course, our expert instructors will train you to-

  • Master the concepts of HDFS and MapReduce framework
  • Understand Hadoop 2.x Architecture
  • Setup Hadoop Cluster and write Complex MapReduce programs
  • Learn data loading techniques using Sqoop and Flume
  • Perform data analytics using Pig, Hive and YARN
  • Implement HBase and MapReduce integration
  • Implement Advanced Usage and Indexing
  • Schedule jobs using Oozie
  • Implement best practices for Hadoop development
  • Understand Spark and its Ecosystem
  • Learn how to work in RDD in Spark
  • Work on a real life Project on Big Data Analytics

Big Data & Hadoop Market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 - Forbes

McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts - Mckinsey Report

Avg salary of Big Data Hadoop Developers is $135k - Indeed.com Salary Data.

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 Professional IT groups, who are continuously enjoying the benefits moving into Big data domain:

  • Developers and Architects
  • BI /ETL/DW professionals
  • Senior IT Professionals
  • Testing professionals
  • Mainframe professionals
  • Freshers

Hadoop practitioners are among the highest paid IT professionals today with salaries ranging till $85K (source: indeed job portal), and the market demand for them is growing rapidly.

You can check a blog related to Why Choose Hadoop As a Career? Also, once your Hadoop training is over, you can check the Top interview questions related edureka blog.

Realtime Analytics is the new market buzz and having Apache Spark skills is a highly preferred learning path after the Hadoop training. Check out the upgraded Spark Course details.

As such, there are no pre-requisites for learning Hadoop. Knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush-up Core-Java skills, Edureka offer you a complimentary self-paced course, i.e. "Java essentials for Hadoop" when you enroll in Big Data Hadoop Certification course.

Your system should have 4GB RAM and i3 processor. In case, your system falls short of these requirements, we can provide you remote access to our Hadoop Cluster.
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 the 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. For any doubt, the 24*7 support team will promptly assist you.Edureka Virtual Machine can be installed on Mac or Windows machine.

    Towards the end of the course, you will work on a live project where you will be using PIG, HIVE, HBase and MapReduce to perform Big Data analytics.
    Following are a few industry-specific Big Data case studies that are included in our Big Data and Hadoop Certification 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:

  • 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.
  • 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.
  • Push the output HDFS and then feed it into PIG, which splits the data into two parts: Category data and Ratings data.
  • Write a fancy Hive Query to analyze the data further and push the output is into relational database (RDBMS) using Sqoop.
  • 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:

  • Get the number of complaints filed under each product
  • Get the total number of complaints filed from a particular location
  • 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:

  • 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
  • Top 20 locations from where most of the trips start based on booked trip count
  • 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:

  • Find list of airports operating in the country
  • Find the list of airlines having zero stops
  • List of airlines operating with code share
  • Which country (or) territory has the highest number of airports
  • 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:

  • Get the user who has rated the most number of movies
  • Get the user who has rated the least number of movies
  • Get the count of total number of movies rated by user belonging to a specific occupation
  • 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

Learning Objectives : In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works.

Topics : 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, Hadoop Different Distributions.

Learning Objectives :In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques, how to setup single node and multi node hadoop cluster.

Topics-Hadoop 2.x Cluster Architecture - Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Single node cluster and Multi node cluster set up Hadoop Administration.

Learning Objectives :In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.

Topics-MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce. Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.

Learning Objectives :In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.

Topics : Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format, Xml file Parsing using MapReduce.

Learning Objectives : In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset.

Topics : About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types, Shell and Utility Commands, Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution ), Pig Streaming, Testing Pig scripts with Punit, Aviation use case in PIG, Pig Demo on Healthcare Data set.

Learning Objectives : This module will help you in understanding Hive concepts, Hive Data types, Loading and Querying Data in Hive, running hive scripts and Hive UDF.

Topics : Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Managing Outputs, Hive Script, Hive UDF, Retail use case in Hive, Hive Demo on Healthcare Data set.

Learning Objectives : In this module, you will understand Advanced Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, optimizations in hive. You will also acquire in-depth knowledge of HBase, HBase Architecture, running modes and its components.

Topics : Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive Indexes and views Hive query optimizers, Hive : Thrift Server, User Defined Functions, HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, Run Modes & Configuration, HBase Cluster Deployment.

Learning Objectives : This module will cover Advanced HBase concepts. We will see demos on Bulk Loading , Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.

Topics : HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading, Getting and Inserting Data, Filters in HBase.

Learning Objectives : In this module you will learn Spark ecosystem and its components, how scala is used in Spark, SparkContext. You will learn how to work in RDD in Spark. Demo will be there on running application on Spark Cluster, Comparing performance of MapReduce and Spark.

Topics : What is Apache Spark, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases, Spark a Polyglot, What is Scala?, Why Scala?, SparkContext, RDD.

Learning Objectives : In this module, you will understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

Topics : Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Oozie for MapReduce, PIG, Hive, and Sqoop, Combine flow of MR, PIG, Hive in Oozie, Hadoop Project Demo, Hadoop Integration with Talend.

"You will never lose any lecture. You can choose either of the two options:
  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch."

edureka is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience.

You can give us a CALL at +91 88808 62004/1800 275 9730 (US Tollfree Number) OR email at sales@edureka.co

  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka's Big Data and Hadoop certificate.
  • edureka certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc. Please be assured.