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Big Data Hadoop Training by Building Projects

  • Get Trained for Microsoft Big Data Certification - Learn More
  • 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


22
Oct
Sun to Thu (3 weeks)
6:30 PM - 8:30 PM PST
$399

28
Oct
Sat and Sun (4 weeks)
7:00 AM - 11:00 AM PST
$399

29
Oct
Sun to Thu (3 weeks)
6:30 PM - 8:30 PM PST
$399

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

About Online Hadoop Training

Project Portfolio

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

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42 hrs live hands-on sessions with industry leaders

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.

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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.

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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.

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Weekly 1-on-1 meetings with Mentor

If you opt for the Microsoft Track, you will get 8 one-on-one meetings with an experienced Hadoop architect who will act as your mentor.

Benefits of Online Hadoop Training

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.

Hadoop FAQ's- Microsoft Track

1) How will I benefit from the Microsoft Hadoop Certification track with Industry Expert?

  • You will get 8 one-to-one Sessions with an experienced Hadoop Architect.
  • You will learn to use Hadoop technology in Microsoft Azure HDInsight to build batch processing, real-time processing and interactive processing big data solutions.
  • Microsoft Hadoop Training track will help you prepare for the “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification exam.
  • “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification is a MCSE (Microsoft Certified Solutions Expert Level – a globally recognized standard for IT professionals) certification level that will help IT professionals demonstrate their ability to build innovative big data solutions on Hadoop HDInsight cluster to the prospective employers.
  • On successful completion of the exam, receive a certificate from Microsoft to verify your big data skills and increase your big data job prospects.

2) Who should take the “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification exam?

This Hadoop certification exam is designed for candidates who want to become certified big data developers, data architects, data engineers, and data scientists. Candidates appearing for this exam must have undergone a comprehensive Hadoop training and should have knowledge of relevant big data technologies like Hadoop, Spark, HBase, Hive, Sqoop, Flume, and HDInsight.

3) What skills are tested in the “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification exam?

This Hadoop certification exam tests a candidate’s ability to implement batch data processing, real-time processing, and interactive processing on Hadoop in HDInsight. The Microsoft Hadoop certification exam 70-775 aims to test a candidates ability to accomplish the below mentioned technical tasks –

  • Administer and Provision HDInsight Clusters.
  • Implement Big Data Real Time Processing Solutions.
  • Implement Big Data Batch Processing Solution.
  • Implement Big Data Interactive Processing Solutions.

4) What is the cost of “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification exam?

The cost for “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification exam is 165 USD. If you have any specific questions regarding the Microsoft track for Big Data and Hadoop Training, please click the Request Info. Button on top of this page.

5) Is the “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification exam descriptive or an MCQ’s exam?

“70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification exam is a multiple choice questions exam.

5) How to prepare for the “70-775 Perform Data Engineering on Microsoft Azure HDInsight” Hadoop certification?

There is no go-to exam guide to prepare for this Hadoop HDInsight Certification exam. The best way to prepare for this exam is to have a good hands-on experience working on big data technologies like Hadoop, HBase, Pig, Hive, YARN, Sqoop, and Spark. DeZyre’s Big Data and Hadoop training will help you prepare for the exam through a big data Hadoop project under the guidance of an industry expert. You can also refer to the Azure HDInsight documentation available on the Microsoft official website to prepare yourself for the “70-775 Perform Data Engineering on Microsoft Azure HDInsight” big data certification exam.

Big Data and Hadoop 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

Project

  • 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.

Microsoft Track

microsoft_learning_logo
  • In a survey of 700 IT professionals, 60 percent said certification led to a new job. (Network World and SolarWinds, IT Networking Study, October 2011
  • 86% of hiring managers indicate IT certifications are a high or medium priority during the candidate evaluation process. (CompTIA, Employer Perceptions of IT Training and Certification, January 2011)
  • 64% of IT hiring managers rate certifications as having extremely high or high value in validating the skills and expertise of job candidates. (CompTIA, Employer Perceptions of IT Training and Certification, January 2011)
Module 1

Learn Hadoop on HDInsight (Linux)

  • What is Hadoop on HDInsight?
  • How is data stored in HDInsight?
  • Information about using HDInsight on Linux
  • Using SSH with Linux clusters from a Linux computer
  • SSH Tunneling to HDInsight Linux clusters
Module 2

Processing Big Data with Hadoop in Azure HDInsight

  • Provision an HDInsight cluster.
  • Connect to an HDInsight cluster, upload data, and run MapReduce jobs.
  • Use Hive to store and process data.
  • Process data using Pig.
  • Use custom Python user-defined functions from Hive and Pig.
  • Define and run workflows for data processing using Oozie.
  • Transfer data between HDInsight and databases using Sqoop.
Module 3

Implementing Real-Time Analytics with Hadoop in Azure HDInsight

  • Use HBase to implement low-latency NoSQL data stores.
  • Use Storm to implement real-time streaming analytics solutions.
  • Use Spark for high-performance interactive data analysis.
Module 4

Implementing Predictive Analytics with Spark in Azure HDInsight

  • Using Spark to explore data and prepare for modeling
  • Build supervised machine learning models
  • Evaluate and optimize models
  • Build recommenders and unsupervised machine learning models
Module 5

Project

  • Implement a Big Data Project under the guidance of a Hadoop Architect
  • Upload your project to DeZyre portfolio and display to recruiters

Hadoop Projects

The Hadoop Projects at DeZyre are based on real use cases from the industry. Working on Hadoop projects will solidify your working knowledge in Hadoop. In any Hadoop project - the process remains the same. Gathering the data, opening it in HDFS, identifying the attributes required for analysis, cleaning the data and then doing the transformation in analysis. But there are different projects that students can practice on, because it is a challenge to understand a particular set of data if you are not from that field.

In the DeZyre 1-1 mentor track, you can choose any of the following projects to work on. You will be assigned an Industry mentor, who will oversee your project and guide you throughout the duration of the Hadoop Project. You will get 6 hours of 1-1 sessions with the mentors.

Once you complete the final project, you will receive the certificate in Big Data and Hadoop from DeZyre. You can also mention this Hadoop project that you worked on, in your resume and on your LinkedIn profile. Let's get started.

  • Hadoop Project - 1: Data Analysis on Medicare Data. (Heathcare Sector)

    Medicare is a Govenrment heatlh insurance program. Every month in your paycheck you will see a deduction for Medicare. The way this program works is - every month from your paycheck, the Govt. deducts an amount, whether you like it or not. Once you turn 65 or when you retire, or if someone turns out to be terminally ill, Medicare will give health insurance benefits throughout the remaining life of that person.

    In this project, you will need to do some analysis of medicare data. This is a complicated data set. This is because private companies administer this program. It means Medicare claims are offered by private companies. The private companies, in order to attract the customers, will list some other benefits, apart from the basic benefits listed by the Govt. scheme and therefore will charge something extra for it. Medicare maintains the details of what plans are available in each county, across the country. Also these details include - what is the plan, who is providing it, what are the benefits offered in this plan and the charges for that. All this information is available in Medicare and is accessible.

    In this project - you will directly get the data from Medicare, to perform analysis on the data. Different private companies offer a variety of Medicare plans in many counties. Since senior citizens have to enroll in these plans every year - it becomes important to choose the right plan, because every plan has different benefits and different charges for availing the benefits. For senior citizens - this is the biggest financial decision they make every year. Medicare data is focused on choosing the right Medicare plan. Some queries that you will be working on are:

    • You have to decide which plans are giving the most benefits.
    • Identify the top 5 plans with lowest premiums in each county.
    • What are the charges and benefits for a Doctor's co-pay?
    • What are the charges for a generalist doctor or a specialist doctor?
    • How do the plans compare for the ambulance services required?
    • What are the plans available for diabetes patients?

    All these questions need to be answered through big data analytics - so that it can help members choose the right kind of plan based on their requirements.

  • Hadoop Project - 2: Perform Call-drop analysis. (Telecom Sector)

    All mobile operators will have call records. Every day, all the calls that are made by people are recorded and a log file is maintained for these records. This is known as the CDR data. In this project, to get real insights on the data - you have to analyse over 100 million calls in the logs(each day). The objective of this analysis is to figure out how to resolve the call-drop issue. For example, if somebody is experiencing more than 17 call-drops in a month, then there is 90% chance that the person will drop out of the network.

    You will need to perform the call-drop analysis on the call log data, on a daily basis, so that-

    • You can figure out who are the customers who are facing this difficulty.
    • Where they are located?
    • What is the reason for the call-drops?
    • Identify the customers who are at risk of getting dropped out.

    The reason this kind of analysis is becoming critical is - for a company, it is always cheaper to retain a customer than to acquire new customers. This will allow u to advise the tech support team to optimize the towers from which more call-drops are occuring, to expand the capacity of these towers from where there are call drops occuring. To do this per day data should be analysed. Per day the data produced for one mobile network is 100 million call records. Since it cannot be fit into one machine - you will need to split that into 2 million blocks - so that all of it can fit into one machine for simultaneous analysis.

  • Hadoop Project - 3: Identifying Mortgage Defaulters (Finance Sector)

    In a bank, Hadoop tools can be used to predict mortgage defaulters and improve market segmentation. It requires you to move the data from multiple data warehouses into the Hadoop cluster and then build some queries on that data.

    For market segmentation for increased business, you need to:

    • Locate the top 10 states where the customers do not have credit cards. This data will allow Banks to sell their credit cards or loan products.
    • Identify customers within the age group of 25-60 years who are not using Mobile Apps. Based on this market segmentation the banks will run a marketing campaign.
    • Profiling the occurances of late payment or defaults, which will let the banks move into these markets strategically, thereby avoiding unnecessary bad debts.

    The requirement of this project, is to load the data into Hadoop clusters and build these queries.

  • Hadoop Project - 4: Real Time Twitter Data Acquisition. (Product Development/Marketing Sector)

    You can build a listening platform based on Twitter data analysis. If a company wants to understand what people think about their products or services and what are the sentiments of their customers, they can turn to social media platforms like Twitter. To extract the comments that are related to the company, its products or brands, Hadoop can be used to gather that data and run some analysis on that data. You can build queries around the data like:

    • Identify which regions the comments are coming from.
    • Perform sentiment analysis.
    • Gauge interest level of the customers in a particular geography.
    • Group by different segments.

    This kind of analysis is useful for the company for marketing campaigns and customer support. This will help curb distatified customers leaving bad comments in social media which could affect their brand.

Upcoming Online Hadoop Training

Oct 22nd

  • Duration: 3 weeks
  • Days: Sun to Thu
  • Time: 6:30 PM - 8:30 PM PST
  • 8 One-to-One session (1 hour) with Hadoop Architect
  • Get Prepared to be Microsoft Certified
  • Implement a Big Data Hadoop Project
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

Oct 28th

  • Duration: 4 weeks
  • Days: Sun to Thu
  • Time: 7:00 AM - 11:00 AM PST
  • 8 One-to-One session (1 hour) with Hadoop Architect
  • Get Prepared to be Microsoft Certified
  • Implement a Big Data Hadoop Project
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

Oct 29th

  • Duration: 3 weeks
  • Days: Sun to Thu
  • Time: 6:30 PM - 8:30 PM PST
  • 8 One-to-One session (1 hour) with Hadoop Architect
  • Get Prepared to be Microsoft Certified
  • Implement a Big Data Hadoop Project
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

What People Are Saying

See all 337 Reviews from DeZyre's Students

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Frequently Asked Questions

  • 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 the Microsoft Certification Track ?

    DeZyre is an authorised Microsoft Training Partner. We train you for the Microsoft Big Data Engineering Certification. We will assign a Hadoop Architect as your mentor. You will get 8,1-to-1 live online sessions with this mentor. You will jointly implement a project. You will also receive study materials from Microsoft. The mentor will also help you prepare for the Microsoft certification. 

  • 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. DeZyres 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.

Hadoop Short Tutorials

View All Short Hadoop Tutorials

These short Hadoop tutorials help boost in-depth knowledge around each of the components in the Hadoop ecosystem as they are in the form of advanced lessons for a quick memory recap of all that you learnt in your Hadoop training course. With to-the-point solutions to every problem a professional might encounter, while using any of the Hadoop components, these short Hadoop tutorials can be your guide to working with Hadoop on a daily basis.

  • 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 – hadoop-env.sh, 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 start-all.sh 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 stop-all.sh 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 –

    CREATE EXTERNAL TABLE DeZyre_Course (
    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.

  Blog  

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Recap of Apache Spark News for June 2017


News on Apache Spark - June 2017 ...

Recap of Hadoop News for June 2017


News on Hadoop - June 2017 ...

Hadoop Cluster Overview: What it is and how to setup one?


What is a Hadoop Cluster? ...

Top 6 Hadoop Vendors providing Big Data Solutions in Open Data Platform


Today, Hadoop is an open-source, catch-all technology solution with incredible scalability, low cost storage systems and fast paced big data analytics with economical server costs.

Top 50 Hadoop Interview Questions


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.

Big Data Analytics- The New Player in ICC World Cup Cricket 2015


With the ICC World Cup Cricket 2015 round the corner; battle is on for the ICC World Cup 2015.The big final is between Australia and New Zealand.

Hadoop 2.0 (YARN) Framework - The Gateway to Easier Programming for Hadoop Users


In this piece of writing we provide the users an insight on the novel Hadoop 2.0 (YARN) and help them understand the need to switch from Hadoop 1.0 to Hadoop 2.0.

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


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

Difference between Pig and Hive-The Two Key Components of Hadoop Ecosystem


In this post we will discuss about the two major key components of Hadoop i.e. Hive and Pig and have a detailed understanding of the difference between Pig and Hive.

5 Reasons why Java professionals should learn Hadoop


Hadoop is entirely written in Java, so it is but natural that Java professionals will find it easier to learn Hadoop. One of the most significant modules of Hadoop is MapReduce and the platform used to create MapReduce programs is Apache Pig.

5 Job Roles Available for Hadoopers


As Hadoop is becoming more popular, the following job roles are available for people with Hadoop knowledge - Hadoop Developers, Hadoop Administrators, Hadoop Architect, Hadoop Tester and Data Scientist.

News

Concept AV kicks Australian Rugby big data goal with Crestron.arnnet.com, July 4, 2017.


Australian Rugby is entering the big data world with deployment from Concept AV that will let the sports governing body enhance their insights by looking at player footage and other related data. Video feeds create huge amounts of data for the Australian Rugby Union which are fed back into the training systems for the player and business development. “We will use multiple technologies together to be able to give the best insights and analysis of a player’s performance.” - said Kevin Stafford, ARU head of technology. (Source : https://www.arnnet.com.au/article/621413/concept-av-kicks-australian-rugby-big-data-goal-crestron/)

A Remote Chinese Province Uses Its Climate To Grow A Big-Data Industry.npr.org, July 3, 2017.


Guizhou has picked big data as the industry to surge to the forefront of China’s sector.Guizhou has been putting up a big international expo since the last 3 consecutive years to highlights its role as a big data hub with companies like Qualcomm and Dell having booths setup. Central government is offering various incentives to grab the attention of big data firms for establishing experiment zones and pilot programs.Taiwanese electronics maker Foxconn has setup a data centre in an industrial zone in the provincial capital.Foxconn aims to enhance its operational efficiency through the use of networked machines, cloud computing and AI, all of which requires analyzing huge amounts of data and storing it on servers.Chan designed Foxconn;s data centre sits on a natural plateau where the altitude and monsoon climate have an average annual temperature of 59 degrees. Chan has designed a natural wind tunnel to make sure that the 600 data centre that are installed there receive free natural cooling.(Source : http://www.npr.org/sections/alltechconsidered/2017/07/03/535369266/a-remote-chinese-province-uses-its-climate-to-grow-a-big-data-industry)

'Big data analytics to become $16 billion industry by 2025'. EconomicTimes.com,July 2, 2017.


According to Industry Experts, the big data analytics sector in India is anticipated to grow eight times its current value of $2 billion to reach $16 billion by end of 2025.India is among the top ten big data analytics market across the world and NASSCOM aims to bring it to among top three in the next 3 years , considering a CAGR of 26% over the next 5 years. (Source : http://economictimes.indiatimes.com/tech/ites/big-data-analytics-to-become-16-billion-industry-by-2025/articleshow/59410695.cms)

Big Data Tells Mortgage Traders an Amazing Amount About You.Bloomberg.com, June 29, 2017.


TheNumber, a recently launched New-York based startup pulls data from marketing firms, public loan filings, courthouses and dozens of other sources, and sells it to mortgage bond and loan traders. This means that if you have taken any home loans recently chances are that all the activities that you have made are available with these traders. This will help them identify the likelihood of paying the loans by borrowers. However, this is raising a concern among consumer-rights watchdogs that it is invading the privacy of borrowers. There may be a situation where you may have not been granted loan based on your gender or race. To some extent sharing of this information will benefit the mortgage traders, as they can take quick decisions which earlier use to take weeks or even months. (Source: https://www.bloomberg.com/news/articles/2017-06-29/big-data-can-tell-mortgage-traders-an-amazing-amount-about-you)

Big data investments to top $76B worldwide by 2020. HealthDataManagement.com, June 29, 2017.


Majority of the big companies have shifted their focus on big data over the last decade.According to SNS Research, over $57Bn worth big data investments are likely to be made in 2017 alone. Big data investments are anticipated to grow at CAGR of 10% in the next 3 years reaching $76 billion by end of 2020. As the data is growing exponentially in terms of its size, complexity and versatility, so is the need to process the increasing amount of data growing at a rapid pace. Big Data has found its implementation in the most diverse market segments like healthcare, fraud detection, weather forecasting etc. Many countries have implemented big data analytics to solve most complex problems to rejuvenate their economies. (Source: https://www.healthdatamanagement.com/news/big-data-investments-to-top-76b-worldwide-by-2020)

Hadoop Jobs

View all Hadoop Jobs

Big Data ETL Developer

Company Name: DoubleVerify,Inc.
Location: New York, NYC
Date Posted: 04th Jul, 2017
Description:
  • Developing ETL processes that process billions of records a day efficiently
  • Delivering insightful data to clients, partners and internal users in various ways by implementing robust, scalable data delivery applications and APIs.
  • Collaborating and participating in project meetings.
  • Analyzing data to test the correctness and effectiveness of ETL processes.

Senior Big Data Developer

Company Name: AAA East Central
Location: Costa Mesa, CA
Date Posted: 04th Jul, 2017
Description:

* Defining technical requirements, architecting data pipelines and integration routines with internal/external sources.

* Developing data integration/ingestion processes.

* Building SQL/HIVE databases and tables.

* Writing PIG scripts.

* Job automation and scheduling using Control-M, unit and system testing.

* Performance tuning and analytical and business user support.

Big Data Developer

Company Name: Perficient
Location: US-CO-Denver
Date Posted: 26th Jun, 2017
Description:
  • Participate in technical planning & requirements gathering phases including Design, code, test, troubleshoot, and document engineering software applications.
  •  Ensuring that technical software development process is followed on the project, familiar with industry best practices for software development.
  • Demonstrate the ability to adapt and work with team members of various experience level
  • End-to-End Cloud Data Solutioning and data stream design, experience with tools of the trade: Hadoop, Spark,Storm, Hive, Pig,...