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Hadoop Developer

Big Data continues to play a big part in many different aspects of today’s digital world, and it’s still growing. Consequently, Hadoop’s/Spark’s popularity continues to rise, as business professionals increasingly rely on it to handle today’s data glut.

Big Data on the rise, so Hadoop’s and Spark’s demand rises, and that means an increase in Hadoop jobs, particularly Hadoop/Spark Developers. This line of thinking culminates in one fundamental question: How does one become a Hadoop Developer ot Spark Developer?

Fortunately, we have answers. Everything you need to know about Hadoop/Spark training and getting that hot Hadoop and Spark Developer position for yourself . If you’re already a developer and have no intention of significantly changing your job or career path, you can still benefit from upskilling. Consider adding Hadoop and Spark certification to your resume.


Hadoop Developer live online classes

17 Jun, 2020 Weekend Batch

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06 Jul, 2020 Mon to Fri Batch

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  • Part 1 : Getting Started with Hadoop. 

    Chapter 1: Introducing Hadoop and Seeing What It’s Good For.

    Big Data and the Need for Hadoop.

    • Exploding data volumes
    • Varying data structures
    • A playground for data scientists.

    The Origin and Design of Hadoop

    • Distributed processing with MapReduce
    • Apache Hadoop ecosystem.

    Examining the Various Hadoop Offerings

    • Comparing distributions.
    • Working with in-database MapReduce.
    • Looking at the Hadoop toolbox

    Chapter 2: Common Use Cases for Big Data in Hadoop.

    • Log Data Analysis
    • Data Warehouse Modernization.
    • Fraud Detection.
    • Risk Modeling.
    • Social Sentiment Analysis
    • Image Classification.
    • Graph Analysis.
    • To Infinity and Beyond.

    Chapter 3: Setting Up Your Hadoop Environment

    Choosing a Hadoop Distribution

    Choosing a Hadoop Cluster Architecture

    • Pseudo-distributed mode (single node).
    • Fully distributed mode (a cluster of nodes)

    Hadoop Installation hands on

  • Part 2: How Hadoop Works  

    Chapter 4: Storing Data in Hadoop: The Hadoop

    • Distributed File System
    • Data Storage in HDFS
    • Taking a closer look at data blocks
    • Replicating data blocks.
    • Slave node and disk failures.

    Sketching Out the HDFS Architecture.

    • Looking at slave nodes
    • Keeping track of data blocks with NameNode
    • Checkpointing updates.

    HDFS Federation.

    HDFS High Availability.

    Chapter 5: Reading and Writing Data

    • Compressing Data
    • Managing Files with the Hadoop File System Commands

    Chapter 6: MapReduce Programming.

    • Thinking in Parallel.
    • Seeing the Importance of MapReduce
    • Doing Things in Parallel: Breaking Big Problems into
    • Many Bite-Size Pieces
    • Looking at MapReduce application flow.
    • Understanding input splits..
    • Seeing how key/value pairs fit into the
    • MapReduce application flow

    Chapter 7: Frameworks for Processing Data in Hadoop

    YARN and MapReduce.

    Running Applications Before Hadoop 2.

    • Tracking JobTracker
    • Tracking TaskTracker.
    • Launching a MapReduce application

    Chapter 8: Pig: Hadoop Programming Made Easier .

    • Admiring the Pig Architecture.
    • Going with the Pig Latin Application Flow

    Working through the ABCs of Pig Latin.

    • Uncovering Pig Latin structures.
    • Looking at Pig data types and syntax.
    • Evaluating Local and Distributed Modes of Running Pig scripts.
    • Checking Out the Pig Script Interfaces..
    • Scripting with Pig Latin.

    Chapter 9: Developing and Scheduling Application Workflows with Oozie .

    Getting Oozie in Place.

    Developing and Running an Oozie Workflow.

    • Writing Oozie workflow definitions.
    • Configuring Oozie workflows.
    • Running Oozie workflows

    Scheduling and Coordinating Oozie Workflows.

    • Time-based scheduling for Oozie coordinator jobs.
    • Time and data availability-based scheduling for Oozie
    • Coordinator jobs.
    • Running Oozie coordinator jobs.
  • Part 3: Hadoop and Structured Data. 

    Chapter 10: Hadoop and the Data Warehouse: Friends or Foes?.

    Comparing and Contrasting Hadoop with Relational Databases

    • NoSQL data stores.
    • ACID versus BASE data stores.
    • Structured data storage and processing in Hadoop

    Modernizing the Warehouse with Hadoop.

    • The landing zone.
    • A queryable archive of cold warehouse data
    • Hadoop as a data preprocessing engine.
    • Data discovery and sandboxes..

    Chapter 11: Extremely Big Tables: Storing Data in HBase

    Say Hello to HBase

    • Sparse.
    • It’s distributed and persistent
    • It has a multidimensional sorted map

    Understanding the HBase Data Model.

    Understanding the HBase Architecture.

    • RegionServers .
    • MasterServer.
    • Zookeeper and HBase reliability..

    Taking HBase for a Test Run

    • Creating a table..
    • Working with Zookeeper.

    Getting Things Done with HBase.

    Working with an HBase Java API client example

    • HBase and the RDBMS world.
    • Knowing when HBase makes sense for you?.
    • ACID Properties in HBase..

    Transitioning from an RDBMS model to HBase..

    • Deploying and Tuning HBase
    • Hardware requirements
    • Deployment Considerations
    • Tuning prerequisites..
    • Understanding your data access patterns..
    • Pre-Splitting your regions.
    • The importance of row key design
    • Tuning major compactions.

    Chapter 12: Applying Structure to Hadoop Data with Hive.


    • Saying Hello to Hive
    • Seeing How the Hive is Put Together.
    • Getting Started with Apache Hive.

    Examining the Hive Clients

    • The Hive CLI client..

    The web browser as Hive client

    • SQuirreL as Hive client with the JDBC Driver.
    • Working with Hive Data Types.
    • Creating and Managing Databases and Tables.
    • Managing Hive databases.

    Creating and managing tables with Hive.

    • Seeing How the Hive Data Manipulation Language Works.
    • LOAD DATA examples.
    • INSERT examples.
    • Create Table As Select (CTAS) examples.

    Querying and Analyzing Data

    • Joining tables with Hive
    • Improving your Hive queries with indexes
    • Windowing in HiveQL.
    • Other key HiveQL features.

    Chapter 13: Integrating Hadoop with Relational Databases Using Sqoop.

    The Principles of Sqoop Design.

    Scooping Up Data with Sqoop

    • Connectors and Drivers.
    • Importing Data with Sqoop
    • Importing data into HDFS.
    • Importing data into Hive.
    • Importing data into HBase.
    • Importing incrementally
    • Benefiting from additional Sqoop import features

    Sending Data Elsewhere with Sqoop.

    • Exporting data from HDFS.
    • Sqoop exports using the Insert approach
    • Sqoop exports using the Update and Update Insert approach.
    • Sqoop exports using call stored procedures.
    • Sqoop exports and transactions.

    Looking at Your Sqoop Input and Output Formatting Options

    • Getting down to brass tacks: An example of output
    • line-formatting and input-parsing
  • Part 4: Administering and Configuring Hadoop 

    Chapter 14: Deploying Hadoop

    • Rack considerations..
    • Master nodes
    • Slave nodes..
    • Edge nodes
    • Networking.

    Hadoop Cluster Configurations

    • Small.
    • Medium
    • Large
    • Alternate Deployment Form Factors
    • Virtualized servers

    Cloud deployments

    • Sizing Your Hadoop Cluster.

    Chapter 15: Administering Your Hadoop Cluster

    • Achieving Balance: A Big Factor in Cluster Health
    • Mastering the Hadoop Administration Commands
    • Understanding Factors for Performance
    • Hardware
    • MapReduce
    • Benchmarking
    • Tolerating Faults and Data Reliability
    • Putting Apache Hadoop’s Capacity Scheduler to Good Use
    • Setting Security: The Kerberos Protocol
    • Expanding Your Toolset Options
    • Hue
    • Ambari
    • Hadoop User Experience (Hue)
    • The Hadoop shell
    • Basic Hadoop Configuration Details

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  • How will I execute projects in this Hadoop Training Course? 

    You will execute all your Big Data Hadoop Course Assignments/Case Studies on your Cloud LAB environment whose access details will be available on your LMS. You will be accessing your Cloud LAB environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

  • What is CloudLab? 

    CloudLab is a cloud-based Hadoop and Spark environment that GoSkills offers with the Hadoop Training course where you can execute all the in-class demos and work on real-life Big Data Hadoop projects in a fluent manner.

    This will not only save you from the trouble of installing and maintaining Hadoop or Spark on a virtual machine, but will also provide you an experience of a real Big Data and Hadoop production cluster.

    You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.

  • What are the system requirements for this Hadoop Training? 

    You don’t have to worry about the system requirements as you will be executing your practicals on a Cloud LAB environment. This environment already contains all the necessary software that will be required to execute your practicals.

  • Which projects will be a part of this Big Data Hadoop Online Training Course? 

    GoSkills’s Big Data & Hadoop Training includes multiple real-time, industry-based projects, which will hone your skills as per current industry standards and prepare you for the upcoming Big Data roles & Hadoop jobs.

    Project #1:

    Industry: Stock Market

    Problem Statement:TickStocks, a small stock trading organization, wants to build a Stock Performance System. You have been tasked to create a solution to predict good and bad stocks based on their history. You also have to build a customized product to handle complex queries such as calculating the covariance between the stocks for each month.

    Project #2:

    Industry: Health-Care

    Problem Statement: MobiHeal is a mobile health organization that captures patient’s physical activities, by attaching various sensors on different body parts. These sensors measure the motion of diverse body parts like acceleration, the rate of turn, magnetic field orientation, etc. You have to build a system for effectively deriving information about the motion of different body parts like chest, ankle, etc.

    Project #3:

    Industry: Social Media

    Problem Statement:Socio-Impact is a social media marketing company which wants to expand its business. They want to find the websites which have a low rank web page. You have been tasked to find the low-rated links based on the user comments, likes etc.


  • Feature

    Instructor-led Sessions

    Duration: 1 Month
    Week Day classes (M-F): 20 Sessions
    Daily 2 Hours per Session
  • Feature

    Real-life Case Studies

    Live project based on any of the selected use cases, involving the implementation of Data Science.
  • Feature


    Every class will be followed by practical assignments which aggregate to a minimum of 60 hours.
  • Feature

    Lifetime Access

    Lifetime access to Learning Management System (LMS) which has class presentations, quizzes, installation guide & class recordings.
  • Feature

    24 x 7 Expert Support

    Lifetime access to our 24x7 online support team who will resolve all your technical queries, through ticket based tracking system.
  • Feature


    Successful completion of the final project will get you certified as a Data Science Professional by GoSkills.


  • What if I miss a class?  

    You will never miss a lecture at GoSkill! 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.
  • Will I get placement assistance?  
    • To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
  • Can I attend a demo session before enrollment?  
    • We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how are the classes conducted, quality of instructors and the level of interaction in a class.
  • Who are the instructors?  
    • All the instructors at GoSkill! 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 to the participants.
  • What if I have more queries?  


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