Big Data Hadoop Training by Building Projects

  • Become a Hadoop Developer by getting project experience
  • Build a project portfolio to connect with recruiters
    - Check out Toly's Portfolio
  • Get hands-on experience with access to remote Hadoop cluster
  • Stay updated in your career with lifetime access to live classes

Upcoming Live Online Hadoop Training

Sat and Sun(4 weeks)
7:00 AM - 11:00 AM PST

Sun to Thurs(3 weeks)
6:30 PM - 8:30 PM PST

Sun to Thurs(3 weeks)
6:30 PM - 8:30 PM PST

Want to work 1 on 1 with a mentor. Choose the project track

About Online Hadoop Training Course

Project Portfolio

Build an online project portfolio with your project code and video explaining your project. This is shared with recruiters.


42 hrs live hands-on sessions with industry expert

The live interactive sessions will be delivered through online webinars. All sessions are recorded. All instructors are full-time industry Architects with 14+ years of experience.


Remote Lab and Projects

You will get access to a remote Hadoop cluster for this purpose. Assignments include running MapReduce jobs/Pig & Hive queries. The final project will give you a complete understanding of the Hadoop Ecosystem.


Lifetime Access & 24x7 Support

Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.


Weekly 1-on-1 meetings

If you opt for the Mentorship Track with Industry Expert, you will get 6 one-on-one meetings with an experienced Hadoop architect who will act as your mentor.


Money Back Guarantee

DeZyre has a 'No Questions asked' 100% money back guarantee. You can attend the first 2 webinars and if you are not satisfied, please let us know before the 3rd webinar and we will refund your fees.

Benefits of DeZyre Hadoop Training Reviews

How will this help me get jobs?

  • Display Project Experience in your interviews

    The most important interview question you will get asked is "What experience do you have?". Through the DeZyre live classes, you will build projects, that have been carefully designed in partnership with companies.

  • Connect with recruiters

    The same companies that contribute projects to DeZyre also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.

  • Stay updated in your Career

    Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.

What if I have any doubts?

For any doubt clearance, you can use:

  • Discussion Forum - Assistant faculty will respond within 24 hours
  • Phone call - Schedule a 30 minute phone call to clear your doubts
  • Skype - Schedule a face to face skype session to go over your doubts

Do you provide placements?

In the last module, DeZyre faculty will assist you with:

  • Resume writing tip to showcase skills you have learnt in the course.
  • Mock interview practice and frequently asked interview questions.
  • Career guidance regarding hiring companies and open positions.

Online Hadoop Training Course Curriculum

Module 1

Introduction to Big Data

  • Rise of Big Data
  • Compare Hadoop vs traditonal systems
  • Hadoop Master-Slave Architecture
  • Understanding HDFS Architecture
  • NameNode, DataNode, Secondary Node
  • Learn about JobTracker, TaskTracker
Module 2

HDFS and MapReduce Architecture

  • Core components of Hadoop
  • Understanding Hadoop Master-Slave Architecture
  • Learn about NameNode, DataNode, Secondary Node
  • Understanding HDFS Architecture
  • Anatomy of Read and Write data on HDFS
  • MapReduce Architecture Flow
  • JobTracker and TaskTracker
Module 3

Hadoop Configuration

  • Hadoop Modes
  • Hadoop Terminal Commands
  • Cluster Configuration
  • Web Ports
  • Hadoop Configuration Files
  • Reporting, Recovery
  • MapReduce in Action
Module 4

Understanding Hadoop MapReduce Framework

  • Overview of the MapReduce Framework
  • Use cases of MapReduce
  • MapReduce Architecture
  • Anatomy of MapReduce Program
  • Mapper/Reducer Class, Driver code
  • Understand Combiner and Partitioner
Module 5

Advance MapReduce - Part 1

  • Write your own Partitioner
  • Writing Map and Reduce in Python
  • Map side/Reduce side Join
  • Distributed Join
  • Distributed Cache
  • Counters
  • Joining Multiple datasets in MapReduce
Module 6

Advance MapReduce - Part 2

  • MapReduce internals
  • Understanding Input Format
  • Custom Input Format
  • Using Writable and Comparable
  • Understanding Output Format
  • Sequence Files
  • JUnit and MRUnit Testing Frameworks
Module 7

Apache Pig

  • PIG vs MapReduce
  • PIG Architecture & Data types
  • PIG Latin Relational Operators
  • PIG Latin Join and CoGroup
  • PIG Latin Group and Union
  • Describe, Explain, Illustrate
  • PIG Latin: File Loaders & UDF
Module 8

Apache Hive and HiveQL

  • What is Hive
  • Hive DDL - Create/Show Database
  • Hive DDL - Create/Show/Drop Tables
  • Hive DML - Load Files & Insert Data
  • Hive SQL - Select, Filter, Join, Group By
  • Hive Architecture & Components
  • Difference between Hive and RDBMS
Module 9

Advance HiveQL

  • Multi-Table Inserts
  • Joins
  • Grouping Sets, Cubes, Rollups
  • Custom Map and Reduce scripts
  • Hive SerDe
  • Hive UDF
  • Hive UDAF
Module 10

Apache Flume, Sqoop, Oozie

  • Sqoop - How Sqoop works
  • Sqoop Architecture
  • Flume - How it works
  • Flume Complex Flow - Multiplexing
  • Oozie - Simple/Complex Flow
  • Oozie Service/ Scheduler
  • Use Cases - Time and Data triggers
Module 11

NoSQL Databases

  • CAP theorem
  • RDBMS vs NoSQL
  • Key Value stores: Memcached, Riak
  • Key Value stores: Redis, Dynamo DB
  • Column Family: Cassandra, HBase
  • Graph Store: Neo4J
  • Document Store: MongoDB, CouchDB
Module 12

Apache HBase

  • When/Why to use HBase
  • HBase Architecture/Storage
  • HBase Data Model
  • HBase Families/ Column Families
  • HBase Master
  • HBase vs RDBMS
  • Access HBase Data
Module 13

Apache Zookeeper

  • What is Zookeeper
  • Zookeeper Data Model
  • ZNokde Types
  • Sequential ZNodes
  • Installing and Configuring
  • Running Zookeeper
  • Zookeeper use cases
Module 14

Hadoop 2.0, YARN, MRv2

  • Hadoop 1.0 Limitations
  • MapReduce Limitations
  • HDFS 2: Architecture
  • HDFS 2: High availability
  • HDFS 2: Federation
  • YARN Architecture
  • Classic vs YARN
  • YARN multitenancy
  • YARN Capacity Scheduler
Module 15


  • Demo of 2 Sample projects.
  • Twitter Project : Which Twitter users get the most retweets? Who is influential in our industry? Using Flume & Hive analyze Twitter data.
  • Sports Statistics : Given a dataset of runs scored by players using Flume and PIG, process this data find runs scored and balls played by each player.
  • NYSE Project : Calculate total volume of each stock using Sqoop and MapReduce.

Upcoming Classes for Online Hadoop Training

January 21st

  • Duration: 4 weeks
  • Days: Sat and Sun
  • Time: 7:00 AM - 11:00 AM PST
  • 6 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

January 22nd

  • Duration: 3 weeks
  • Days: Sun to Thurs
  • Time: 6:30 PM - 8:30 PM PST
  • 6 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

February 5th

  • Duration: 3 weeks
  • Days: Sun to Thurs
  • Time: 6:30 PM - 8:30 PM PST
  • 6 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

Online Hadoop Training Course Reviews

See all 247 Reviews

FAQs for Online Hadoop Training Online Course

  • How will this Hadoop Training Benefit me?

    - Learn to use Apache Hadoop to build powerful applications to analyse Big Data
    - Understand the Hadoop Distributed File System (HDFS)
    - Learn to install, manage and monitor Hadoop cluster on cloud
    - Learn about MapReduce, Hive and PIG - 3 popular data analysing frameworks
    - Learn about Apache Sqoop,Flume and how to run scripts to transfer/load data
    - Learn about Apache HBase, how to perform real-time read/write access to your Big Data
    - Work on Projects with live data from Twitter, Reddit, StackExchange and solve real case studies

  • What is Apache Hadoop?

    Hadoop is an open source programming framework used to analyse large and sometimes unstructured data sets. Hadoop is an Apache project with contributions from Google, Yahoo, Facebook, Linkedin, Cloudera, Hortonworks etc. It is a Java based programming framework that quickly and cost efficiently processes data using a distributed environment. Hadoop programs are run across individual nodes that make up a cluster. These clusters provide a high level of fault tolerance and fail safe mechanisms since the framework can effortlessly transfer data from failed nodes to other nodes. Hadoop splits programs and data across many nodes in a cluster.

    The Hadoop ecosystem consists of HDFS, MapReduce. This is accompanied by a series of other projects like Pig, Hive, Oozie, Zookeeper, Sqoop, Flume etc. There are various flavours of Hadoop that exist including Cloudera, Hortonworks and IBM Big Insights. Hadoop is increasingly used by enterprises due to its flexibility, scalability, fault tolerance and cost effectiveness. Anyone with a basic sql and database background will be able to learn hadoop.

  • What is Apache Oozie?

    Oozie is a scheduling component on top of hadoop for managing hadoop jobs. It is a java based web application that combines multiple jobs into a single logical unit of work. It was developed to simplify the workflow and coordination of hadoop jobs. Hadoop developers define actions and dependencies between these actions. Oozie then runs the workflow of dependent jobs i.e. it schedules various actions to be executed, once the dependencies have been met. Oozie consists of two important parts -

    1) Workflow Engine - It stores and runs the workflows composed of Hadoop MapReduce jobs, hive jobs or pig jobs.

    2) Coordinator Engine - Runs the workflow jobs based on the availability of data and scheduled time.

    Read more on “How Oozie works?”

  • Do you need SQL knowledge to learn Hadoop?

    It is not necessary for one to have SQL knowledge to begin learning hadoop. For people, who have difficulty in working with java or have no knwoledge about java programming,some basic knowledge of SQL is a plus.However, there is no hard rule that you must know SQL but knowing the basics of SQL will give you the freedom to accomplish your Hadoop job using multiple components like Pig and Hive.

    If you are getting started with Hadoop then you must read this post on -"Do we need SQL knowledge to learn Hadoop?"


  • How to learn hadoop online?

    Learning Hadoop is not a walk in the park, it takes some time to understand and gain practical experience on the hadoop ecosystem and its components. The best way to learn hadoop is to start reading popular hadoop books like – “Hadoop: The Definitive Guide”, read some interesting and informative hadoop blogs or hadoop tutorials that will give you some theoretical knowledge about the hadoop architecture and various tools in the ecosystem. However, to get a hadoop job theoretical knowledge does not suffice and gaining hands-on working experience to get a hang of the hadoop ecosystem is a must to land a top gig as a hadoop developer or hadoop administrator. DeZyre’s online hadoop training covers all the basics right from understanding “What is Hadoop?” to deploying your own big data application on the hadoop cluster. After the hadoop training, you can keep yourselves abreast with the latest tools and technologies in the hadoop ecosystem by working on hadoop projects in various business domains through Hackerday to add an extra feather to the cap on your hadoop resume.

  • What are the various Hadoop Developer job responsibilities?

    A hadoop developer is responsible for actually programming and coding the business logic of big data applications using the various components- Pig, Hive, Hbase, etc of the hadoop ecosystem. The core responsibility of a Hadoop Developer is to load disparate datasets, perform analysis on them and unveil valuable insights. The job responsibilities of a Hadoop developer are like any other software developer but in the big data domain. Read More on Hadoop Developer – Job Responsibilities and Skills.

  • What are various Hadoop Admin job responsibilities?

    Hadoop Admin responsibilities are similar to that of system administrator responsibilities but a hadoop admin deals with the configuration, management and maintenance of hadoop clusters unlike a system admin who deals with servers. Quick overview of Hadoop Admin responsibilities –

    • Installing and configuring new hadoop clusters
    • Maintaining the hadoop clusters
    • Hadoop administrators are also involved in the capacity planning phase.
    • Monitoring any failed hadoop jobs
    • Troubleshooting
    • Backup and Recovery Management

    Read More – Hadoop Admin Job Responsibilities and Skills

  • Does DeZyre offer any corporate discounts for Hadoop training course?

    DeZyre offers corporate discounts for the hadoop course based on the number of students enrolling for the course. Contact us by filling up the Request Info.   form on the top of the hadoop training page. Our career counsellors will get back to you at the earliest and provide you with all the details.

  • Why Hadoop Training and Certification Online?
    Hadoop is the leading framework in use today to analyse big data. This has triggered a large demand for hadoop developers, hadoop administrators and data analysts. Getting trained Hadoop provides valuable skills in the hadoop ecosystem including Pig, Hive, MapReduce, Sqoop, Flume, Oozie, Zookeeper, YARN. Storm and Spark and also becoming relevant in Hadoop related training. DeZyre’s Hadoop training offers 40 hours of live interactive instructor led online courses. This is accompanied by lifetime access to a discussion forum and a hadoop cluster on Amazon AWS.
  • Why do I need the Certificate in Big Data and Hadoop?
    If you are using Internet today - chances are you've come across more than one website that uses Hadoop. Take Facebook, eBay, Etsy, Yelp , Twitter, Salesforce - everyone is using Hadoop to analyse the terabytes of data that is being generated. Hence there is a huge demand for Big Data and Hadoop developers to analyse this data and there is a shortage of good developers. This DeZyre certification in Big Data and Hadoop will significantly improve your chances of a successful career since you will learn the exact skills that industry is looking for. At the end of this course you will have a confident grasp of Hadoop, HDFS, Map-Reduce, HBase, Hive, Pig and Sqoop, flume, Oozie, ZooKeeper etc.
  • Why should I learn Hadoop from DeZyre instead of other providers?
    DeZyre's Hadoop Curriculum is the most in-depth, technical, thorough and comprehensive curriculum you will find. Our curriculum does not stop at the conceptual overviews, but rather provides in-depth knowledge to help you with your Hadoop career. This curriculum has been jointly developed in partnership with Industry Experts, having 9+ years of experience in the field - to ensure that the latest and most relevant topics are covered. Our curriculum is also updated on a monthly basis.
  • How do I qualify for the Certificate in Big Data and Hadoop?
    There are minimum quality checks you will have to clear in order to be Certified. You will have to attend atleast 70% of the live interactive sessions to qualify and you must submit the final project which will be graded after which you will receive the certification.
  • Do I need to know Java to learn Hadoop?
    A background in any programing language will be helpful - C, C++, PHP, Python, PERL, .NET, Java etc. If you don't have a Java background, we will activate a free online Java course for you to brush up your skills. Experience in SQL will also help. Our helpful Faculty and Assistant Faculty will help you ramp up your Java knowledge.
  • What kind of Lab and Project exposure do I get?
    This course provides you with 40 hours of lab and 25 hours of a project.
    You can run the lab exercises locally on your machine (installation docs will be provided) or login to DeZyre's AWS servers to run your programs remotely. You will have 24/7 support to help you with any issues you face. You will get lifetime access to DeZyre's AWS account.
    The project will provide you with live data from Twitter, NASDAQ, NYSE etc and expect you to build Hadoop programs to analyze the data.
  • Who will be my faculty?
    At DeZyre we realize that there are very few people who are truly "Hadoop experts". So we take a lot of care to find only the best. Your faculty will have at-least 9 years of Java + Hadoop experience, will be deeply technical and is currently working on a Hadoop implementation for a large technology company. Students rate their faculty after every module and hence your faculty has grown through a rigorous rating mechanism with 65 data points.
  • Is Online Learning effective to become an expert on Hadoop?
    From our previous Hadoop batches (both offline and online), our research and survey has indicated that online learning is far more effective than offline learning -
    a) You can clarify your doubts immediately
    b) You can learn from outstanding faculty
    c) More flexibility since your don't have to travel to a class
    d) Lifetime access to course materials
  • What is HDFS?

    The Hadoop Distributed File System [HDFS] is a highly fault tolerant distributed file system, that is designed to run on low-cost, commodity hardware. HDFS is a Java-based file system that forms the data management layer of Apache Hadoop. HDFS provides scalable and reliable data storage thereby making it apt for applications with big data sets. In Hadoop, data is broken into small 'blocks' and stored in several clusters so that the data can be analyzed at a faster speed. HDFS has master/slave architecture. The HDFS cluster has one NameNode - a master server that manages the file system and several DataNodes. A large data file is broken into small 'blocks' of data and these blocks are stored in the Data Nodes. Click to read more on HDFS. 

  • What is MapReduce?

    Hadoop MapReduce is a programming framework which provides massive scalability across Hadoop clusters on commodity hardware. MapReduce concept is inspired by the 'Map' and 'Reduce' functions that can be seen in functional programming. MapReduce programs are written in Java. A MapReduce 'job' splits big data sets into independent 'blocks' and distributes them in the Hadoop cluster for fast processing. Hadoop MapReduce performs two separate tasks and operates on [key,value] pairs. The 'map' job takes a set of data' converts it into another set of data which breaks the individual elements into tuples [key,value] pairs. The 'reduce' job comes after the 'map' job. Where the output of the 'map' job is treated as input and these data tuples are combined into smaller set of tuples. Click to read more on MapReduce. 

  • What is Apache HBase?

    HBase is open source, distributed, non relational database which has been modeled after Google's 'BigTable: A Distributed Storage System for Structured Data'. Apache HBase provide BigTable like capabilities on top of Hadoop HDFS. Hbase allows applications to read/write and randomly access Big Data. Hbase is written in Java, built to scale and can handle massive data tables with billions of rows and columns. HBase does not support a structured query language like SQL. With HBase schemas have to be predefined and the column families have to be specified. But HBase schemas are very flexible, as in, new columns can be added to the families at any time - this way HBase adapts to the changing requirement of the applications.

  • What is Apache Pig?

    Apache PIG is a platform which consists of a high level scripting language that is used with Hadoop. Apache PIG was designed to reduce the complexities of Java based MapReduce jobs. The high level language used in the platform is called PIG Latin. Apache PIG abstracts the Java MapReduce idiom into a notation which is similar to an SQL format. Apache PIG does not necessarily write queries for the data, but it allows creating a complex data flow which shows how the data will be transformed, using graphs which include multiple inputs, transforms and outputs. PIG Latin can be extended using UDFs [User Defined Functions] using any other scripting language like Java, Python or Ruby. Click to read more on Apache PIG. 

  • What is Apache Hive?

    Apache Hive was developed at Facebook. Hive runs on top of Apache Hadoop as an open source data warehouse system for querying and analyzing big data sets stored in Hadoop's HDFS. Hive provides a simple SQL like query language - Hive QL, which translates Hadoop MapReduce jobs into SQL like queries. Hive and PIG though perform the same kind of functions, like, data summarization, queries and analysis - Hive is more user friendly, as anyone with a SQL or relational database background can work on it. HiveQL supports custom MapReduce jobs to be plugged into queries. But Hive is not built to support OTPL workloads. It means there can be no real time queries or row level updates made. Click to read more on Apache Hive. 

  • What is Apache Sqoop?

    Sqoop was designed to transfer structured data from relational databases to Hadoop. Sqoop is a 'SQL-to-Hadoop' command line tool which is used to import individual tables or entire databases into files in HDFS. This data is transformed into Hadoop MapReduce and again the data is transferred back to the relational database. It is not possible for MapReduce jobs to join with data directly on separate platforms. The database servers will suffer a high load due to concurrent connections while the MapReduce jobs are running. Instead if MapReduce jobs join with the data that is loaded on HDFS, it will further speed up the process. Sqoop automates this entire process with a single command line.

  • What is Apache Flume?

    Apache Flume is a highly reliable distributed service used for collecting, aggregating and moving huge volumes of streaming data into the centralized HDFS. It has a simple and flexible architecture which works well while collecting and defining unstructured log data from different sources. Flume defines a unit of data as an 'event'. These events will then flow through one or more Flume agents to reach its destination. This agent is a Java process which hosts these 'events' during the data flow. Apache Flume components are a combination of sources, channels and sinks. Apache Flume sources, consumes events. Channels transfer events to their sinks. Sinks provides the Flume agent with pluggable output capability. Click to read more on Apache Flume. 

  • What is Apache Zookeeper?

    Apache Zookeeper(often referred to as the "King of Coordination" in Hadoop) is high-performance, replicated synchronization service which provides operational services to a Hadoop cluster. Zookeeper was originally built at Yahoo in order to centralize infrastructure and services and provide synchronization across a Hadoop cluster. Since then, Apache Zoopkeeper has grown into a full standard of co-ordination on its own. It is now used by Storm, Hadoop, HBase, Elastic search and other distributed computing frameworks. Zookeeper allows distributed processes to co-ordinate with each other through a shared hierarchical name space of data registers knows as znodes. This will look like a normal file system, but Zookeeper provides higher reliability through redundant services.

    Read More on "How Zookeeper works?"

  • How will I benefit from the Mentorship Track with Industry Expert?

    - Learn by working on an end to end Hadoop project approved by DeZyre.

  • What is Big Data?
    The term Big Data refers to both a problem and opportunity that involves analysing large complicated and sometimes unstructured data sets. Businesses can extract crucial information with the right tools to analyse this data. Historically companies have used MS Excel and basic RDBMS to achieve this kind of analysis. More recently tools such as SAS, SPSS, Teradata, Machine Learning, Mahout etc have played a role. Over the last 3-4 years new technologies such as hadoop, spark, storm, R, python etc have become popular tools to analyse big data. Big data is typically characterised by the volume, variety and velocity of the data.

    Big Data has triggered the need for a new range of job descriptions including Data Scientists, Data Analysts, Hadoop developers, R programers, Python developers etc. IBM indicates that over 90% of all data created was created in the last 2 years. The industries that deal with Big Data the most are telecom, retail, financial services and ad networks.

Online Hadoop Training short tutorials

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  • What HDFS features make it an ideal file system for distributed systems?
    • HDFS has good scalability i.e. data transfer happens directly with the DataNodes so the read/write capacity scales well with the number of DataNodes.
    • Whenever there is need for disk space, just increase the number of DataNodes and rebalance it.
    • HDFS is fault tolerant, data can be replicated across multiple DataNodes to avoid machine failures.
    • Many other distributed applications like HBase, MapReduce have been built on top of HDFS.


  • Is Namenode a Commodity Hardware in Hadoop?

    The complete Hadoop Distributed File System relies on the Namenode , so Namenode cannot be a commodity hardware. Namenode is the single point of failure in HDFS and has to be a high availability machine and not a commodity hardware.

  • What is a DataNode in Hadoop?

    DataNode also referred to as the Slave in Hadoop architecture is the place where the actual data resides. DataNode in the Hadoop architecture is configured with lots of hard disk space because it is the place for the actual data to be stored. DataNode continuously communicates with the NameNode through the heartbeat signal. When a DataNode is down, the availability of data within the hadoop cluster is not affected. The NameNode replicates the blocks managed by the DataNode that is down.

    Sample DataNode Configuration in Hadoop Architecture

    Processors: 2 Quad Core CPUs running @ 2 GHz

    Network: 10 Gigabit Ethernet

    RAM: 64 GB

    Hard Disk 12-24 x 1TB SATA

  • What is a NameNode in Hadoop architecture?

    NameNode is the single point of failure (centrepiece) in a hadoop cluster and stores the metadata of the Hadoop Distributed File System. NameNode does not store the actual data but consists of the directory tree of all the files present in the hadoop distributed file system. NameNode in Hadoop is configured with lots of memory and is a critical component of the HDFS architecture because if the NameNode is down, the hadoop cluster becomes out-of-the-way.

  • What are the most popular hadoop distributions available in the market?

    Popular Hadoop distributions include –

    1. Cloudera Hadoop Distribution
    2. Hortonworks Hadoop Distribution
    3. MapR Hadoop Distribution
    4. IBM Hadoop Distribution
    5. Pivotal
    6. Amazon

    Read More about Hadoop Distrbutions and Popular Hadoop Vendors

  • What kind of hardware scales best for Apache Hadoop?

    Hadoop scales best with dual core machines or processors having 4 to 8 GB of RAM that use ECC memory based on the requirements of the workflow. The machines chosen for hadoop clusters must be economical i.e. they should cost ½  to 2/3 rd of the  cost of production application servers but should not be desktop class machines. When purchasing hardware for Hadoop, the utmost criteria is look for quality commodity equipment so that the hadoop clusters keep running efficient. When buying hardware for hadoop clusters, there are several factors to be considered including the power, network and any other additional components that might be included in large high-end big data applications.

  • How to install Hadoop on Ubuntu?

    Short Hadoop Tutorial for Beginners - Steps for Hadoop Installation on Ubuntu

    1. Update the bash configuration file present - $HOME/.bashrc
    2. Configure the Hadoop Cluster Configuration files –, core-site.xml, mapred-site.xml and hdfs-site.xml.
    3. Format HDFS through NameNode using the NameNode format command.
    4. Start the Hadoop cluster using the shell script. This will start the NameNode, DataNode, Task Tracker and Job Tracker.
    5. If you want to stop the Hadoop Cluster, you can run the script to stop running all the daemons.
    6. Now, you can run any Hadoop MapReduce job.

    Read more for detailed instructions on Installing Hadoop on Ubuntu

  • What is the purpose of Hadoop Cluster Configuration files?

    There are four hadoop cluster configuration files present in the hadoop/conf directory which should be configured to run HDFS. These configuration files should be modified accordingly based on the requirements of the Hadoop infrastructure. To configure Hadoop, the following four cluster configuration files have to be modified –

    • Core-site.xml file – This cluster configuration file details on the memory allocated for HDFS, memory limit, size of the read and write buffers and the port number that will be used for Hadoop instance.
    • Hdfs-site.xml – This cluster configuration file contains the details on where you want to store the hadoop infrastructure i.e. it contains NameNode and DataNode paths along with the value of replication data.
    • Mapred-site.xml – This cluster configuration file contains the details on as to which MapReduce framework is in use. The default value for this is YARN.
    • Yarn-site.xml – This cluster configuration file is used to configure YARN into Hadoop.
  • What is the difference between Hadoop Mapreduce, Pig and Hive?

    MapReduce vs Pig vs Hive - Professionals learning hadoop are likely to work with these 3 important hadoop components when writing Hadoop jobs. Programmers who know java, prefer to work directly hadoop mapreduce whilst others from a database background work with Pig or Hive components in the hadoop ecosystem. The major difference here is that Hadoop MapReduce is a compiled language based programming paradigm whereas Hive is more like SQL and Pig is a scripting language.Considering in terms of the development efforts that programmers have to spend when working with these hadoop components - pig and hive require less development effort than mapreduce programming.

  • How to create an external table in Hive?

    Whenever developers need to process the data through ETL jobs and want tp load the resultant dataset into hive without manual intervention then external tables in Hive are used. External tables are also helpful when the data is not just being used by Hive but also other applications are using it. Here’s how we can create an External Table in Hive –

    CourseId BIGINT,
    CourseName String,
    No_of_Enrollments INT
    COMMENT ‘DeZyre Course Information’
    LOCATION /user/dezyre/datastore/DeZyre_Course

    The above piece of HiveQL code will create an external table in Hive named DeZyre_Course. The location specifies where the data files would be put in.Name of the folder and the table name should always be the same.

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The demand for Hadoop developers is up 34% from a year earlier. We spoke with several expert Hadoop professionals and came up with this list of top 50 Hadoop interview questions.

Hadoop MapReduce vs. Apache Spark –Who Wins the Battle?

Nov 12 2014
An in-depth article that compares Hadoop and Spark and explains which Big Data technology is becoming more and more popular.

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News on Online Hadoop Training

5 Hadoop Trends to Watch in, January 6,2017.

Where is the powerful distributed computing platform heading to in 2017? Datanami highlights the top 5 Hadoop Trends to Watch Out for in 2017 - i) Though there are rumors of Hadoop’s demise ,however, number back up the claim that the usage of Hadoop is expanding and not shrinking. ii) AtScale survey reveals that more than half of the organizations having big data solutions living on the cloud today that is likely to increase to 3/4th. The future of Hadoop is cloudy. iii) Machine Learning automation sees a breakthrough in 2017. iv) Companies building big data solutions on hadoop will focus on data governance and security menace as a frontier of their big data initiatives in 2017. v) In 2017, we might think of big data as a data fabric. Data fabric concept unites important aspects of data management , security, and self-service aspect in big data platforms.(Source:

Big Data In Gambling: How A 360-Degree View Of Customers Helps Spot Gambling Addiction., January 5, 2017.

The largest gaming agency in Finland, Veikkaus is using big data to build a 360 degree picture of its customers. Veikkaus merged with Fintoto(Horse Racing) and Ray(Slots and Casinos) in January 2017 to become the largest gaming organization in Europe. Veikkaus has developed a modern data architecture by pulling data from both digital and offline betting channels. The data architecture is based on open source standards Pentaho and is used for managing, preparing and integrating data that runs through their environments including Cloudera Hadoop Distribution, HP Vertica, Flume and Kafka. (Source :

How Hadoop helps Experian crunch credit, January 5, 2017.

Experian implemented a novel data analytics system that takes just few hours of time to process petabytes of data from hundreds of millions customers worldwide instead of months. Experian deployed a new software called data fabric layer based on open source Hadoop along with an API platform and microservices which will help consumers and corporate customers access credit reports and information quickly. (Source :

Big data analytics will help bridge India's tax,December 28,2016.

The future of every country lies in their government and the people living in that country. A major contribution in the country’s development is the revenue generated from taxes. The world largest democratic country, India, have the lowest tax revenue as a % of GDP within BRICS. If we estimate, then only 4-5% of India’s total population pays tax. But, various policies like Benami Property Act, demonetization, GST, Jan Dhan, and Aadhar, will help revenue department in collecting data at a very high speed. But gathering data alone will not help in providing a win-win environment for everyone unless there is vision, a proper approach for making this data intelligent. Emergence of new technologies like Big Data and Hadoop, and analytics will help in co-locating all the data and generate insight from that data which in turn will help the tax departments in distressing the malpractices like circular trading, transfer pricing manipulations, hawala, sales under declarations etc.(Source:

The Impact Of Big Data, Open Source On Oil And,December 28,2016.

The breeze of Open Source technologies combined with high pressure wave from analytics has created a storm, which has transformed the whole IT industry and the perspective with which we have been seeing the data. There is no industry which not have been touched by open source technology specifically Hadoop. The most recent being the Oil and Gas Industry, which is the most critical, accident prone, and amalgamation of varied technologies. This creates the native environment for Hadoop, as we all know, the power lies in processing of all types of data. As the oil and gas industry is growing at a very higher rate, the velocity of data has grown too and the need to have a consolidated view of all the data sets from all sorts of world, like legacy databases, sensors, IT, external sources, etc., has pushed this industry to shift to open source analytics technologies like Hadoop, which has helped them to identify potential failures 3 to 4 times faster. Apart from this, using these technologies, companies are able to monitor safety critical equipment deployed in offshore ships, encrypt system data and transmit it, which are located in remote areas connected to a very minimal network bandwidth in real time.(Source:

Online Hadoop Training Jobs

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Hadoop Architect

Company Name: Booz Allen Hamilton
Location: McLean, VA
Date Posted: 11th Jan, 2017
  • Assist with building a robust and service-oriented common services platform.
  • Build the platform using cutting-edge capabilities and emerging technologies, including Data Lake and Hortonworks data platforms that will be used by thousands of users.
  • Work in a Scrum-based agile team environment using Hadoop. Install and configure the Hadoop and HDFS environment using the Hortonworks data platform.
  • Create ETL and data ingest jobs using Map Reduce, Pig, or Hive. Work with and integrate multiple types of data, includi...

Hadoop Engineer

Company Name: SpotX
Location: Westminster,Colorado
Date Posted: 09th Jan, 2017
  • Loading disparate data sets and performing analysis of vast data stores
  • Testing prototypes and propose best practices for Big Data
  • Developing new tools and technologies in the video ad serving space
  • Be a part of a scrum team comprised of 4 to 5 developers
  • Participate in daily standup meetings and all meetings of the agile SDLC: planning, estimation, retrospectives, demos, etc.


Foundational Services – Data Services Software Engineer

Company Name: Bloomberg
Location: New York, NYC
Date Posted: 06th Jan, 2017

Responsibilities :

  • Design, implement and own critical applications and components of our services stack
  •  Participate in the full SDLC (Software Development Life Cycle) of various components and systems that are required to be highly efficient, robust and scalable
  •  Enhance our infrastructure to fulfill mission critical SLAs, whether low latency or high throughput data retrieval
  •  Get to know development teams across Bloomberg, understand their application requirements and data acc...