Big Data & Hadoop (Developer) Certification

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About the Training

This Hadoop preparing is intended to make you an affirmed Big Data professional by giving you rich hands-on preparing on Hadoop biological system and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is venturing stone to your Big Data voyage and you will get the chance to take a shot at a Big information Analytics extend subsequent to selecting an informational collection of your decision.


What you will learn in this training?

1.Master fundamentals of Hadoop 2.7 and YARN and write applications using them

2.Setting up Pseudo node and Multi node cluster on Amazon EC2

3.Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Oozie, Pig, Zookeeper, HBase

4.Learn Spark, Spark RDD, Graphx, MLlib writing Spark applications

5.Master Hadoop administration activities like cluster managing, monitoring, administration and troubleshooting

6.Configuring ETL tools like Pentaho/Talend to work with MapReduce, Hive, Pig, etc

7.Detailed understanding of Big Data analytics

8.Hadoop testing applications using MR Unit and other automation tools.

9.Work with Avro data formats

10.Practice real-life projects using Hadoop and Apache Spark

11.Be equipped to clear Big Data Hadoop Certification.


Who should take this training?

1.Programming Developers and System Administrators

2.Experienced working professionals, Project managers

3.Big DataHadoop Developers eager to learn other verticals like Testing, Analytics, Administration

4.Mainframe Professionals, Architects & Testing Professionals

5.Business Intelligence, Data warehousing and Analytics Professionals

6.Graduates, undergraduates eager to learn the latest Big Data technology can take this Big Data Hadoop Certification online training


What are the prerequisites for this training?

There is no pre-requisite to take this Big data training and to master Hadoop. But basics of UNIX, SQL and java would be good. We provide complimentary unix and Java course with our Big Data certification training to brush-up the required skills so that you are good on you Hadoop learning path


Why you should go for training?

1. Worldwide Hadoop Market to Reach $84.6 Billion by 2021 - Allied Market Research

2. Shortage of 1.4 - 1.9 million Hadoop Data Analysts in only us by 2018 - Mckinsey

3. Hadoop Administrator in the US can get a pay of $123,000 -

4. Enormous Data is quickest developing and most encouraging innovation for taking care of expansive volumes of information for doing information investigation. This Big Data Hadoop preparing will help you to be up and running in the most requesting proficient abilities. All the top MNC are attempting to get into Big Data Hadoop consequently there is an enormous interest for Certified Big Data experts. Our Big Data internet preparing will help you to overhaul your vocation in huge information space.


What Hadoop Projects you will be working on?

Towards the finish of the course, you will chip away at a live venture where you will utilize PIG, HIVE, HBase and MapReduce to perform Big Data examination.

A couple of industry case studies -particular Big Data contextual analyses that are incorporated into our Big Data and Hadoop Certification e.g. Back, Retail, Media, Aviation and so on which you can consider for your extend work.


Project #1: Analyze social bookmarking sites to find insights

Industry: Social Media

Data:It comprises of the information gathered from sites like, 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 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


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


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


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


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

Understanding Big Data and Hadoop

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


Hadoop Architecture and HDFS

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.


Hadoop MapReduce Framework

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


Advanced MapReduce

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.


Advanced Hive and Hbase

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.


Processing Distributed Data with Apache Spark

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.


Oozie and Hadoop Project

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.

Jonathon Smith

Wordpress Teacher

Answer all of your questions

Totaleducator is the pioneer of Hadoop preparing in India. As you probably are aware today the interest for Hadoop experts far surpasses the supply. So it pays to be with the market pioneer like Totaleducator with regards to learning Hadoop to summon best pay rates. As a major aspect of the preparation you will find out about the different parts of Hadoop like MapReduce, HDFS, HBase, Hive, Pig, Sqoop, Flume, Oozie among others. You will get a top to bottom comprehension of the whole Hadoop structure for handling gigantic volumes of information in certifiable situations.
The preparation is the most thorough course, composed by industry specialists remembering the occupation situation and corporate necessities. We likewise give lifetime access to recordings, course materials, every minute of every day Support, and free course material overhaul. Henceforth it is a one-time venture

All our instructors are working experts from the Industry and have no less than 10-12 yrs of significant involvement in different spaces. They are topic specialists and are prepared for giving web based preparing with the goal that members get an awesome learning knowledge.

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

We are resolved to give you a magnificent learning knowledge through world-class substance and best-in-class teachers. We will make a biological community through this preparation, that will empower you to change over circumstances into occupation offers by showing your aptitudes at the season of a meeting. We can help you on resume building and furthermore share imperative inquiries addresses once you are finished with the preparation. In any case, please comprehend that we are not into occupation positions.

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, we offer you a complimentary self-paced course: "Java essentials for Hadoop".

Professionals with Administration experience can take up "Hadoop Administration" course training. It will be a natural career progression. If you are planning for Big Data Architect role then you may consider both Hadoop developer and Hadoop Administration training, sequentially.

These classes will be completely Online Live Instructor-led Interactive sessions. You will have chat option available to discuss your queries with instructor during a class.

Depending on the batch you select, Your Live Classes will be held either every weekend for 5 weeks or for 15 weekdays. It would typically be 6-7 hours of effort needed each week post live sessions. This shall comprise hands-on assignments.

1 Mbps of internet speed is preferable to attend the LIVE classes.

You can pay by Credit Card, Debit Card or Net Banking from all the leading banks. For USD payment, you can pay by PayPal. We also have EMI options available.

You can give us a CALL at +1-828-5448230 OR email at

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