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

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.

32 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.

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 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 ProjectPro 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 ProjectPro 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, ProjectPro 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. ProjectPro’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 ProjectPro portfolio and display to recruiters

Hadoop Projects

The Hadoop Projects at ProjectPro 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 ProjectPro 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 ProjectPro. 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.

What People Are Saying

In a short span of time, we have helped many people move up in their careers or change their career paths.

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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 ?

    ProjectPro 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. 

  • Where can I find best hadoop projects for beginners?

    ProjectPro's hadoop training follows a complete hands-on approach where professionals/students get to work on multiple hadoop projects that are based on real big data use cases in the industry.Apart from this ProjectPro also has hundreds of other big data projects and hadoop projects for practice across diverse business domains that students can enrol for at a nominal fee per project.

  • 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. ProjectPro’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 ProjectPro offer any corporate discounts for Hadoop training course?

    ProjectPro 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. ProjectPro’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 ProjectPro 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 ProjectPro instead of other providers?
    ProjectPro'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 ProjectPro'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 ProjectPro'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 ProjectPro 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 ProjectPro.

  • 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

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 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

  • 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 easiest way to install hadoop?

    For a beginner getting started with Hadoop who is trying to create hadoop clusters on Linux server there are several modes in which they can install hadoop on Ubuntu. Usually, people learning hadoop install it in Pseudo-Distributed Mode. The process for installing hadoop on Ubuntu depends on the flavour of Linux we are using and the hadoop distribution you are working with. The standard process to follow for hadoop installation is –

    • Install Java
    • Setup password less SSH between the root accounts on all nodes.
    • Install the hadoop distribution package repository.
    • Every hadoop distributions comes with installation instruction manual.

    However, the above process to install hadoop is usually followed in production implementation. The easiest way to install hadoop on Ubuntu for learning hadoop is mentioned under this Step-By-Step Hadoop Installation Tutorial .

     

  • What is the difference between a Hadoop database and a traditional Relational Database?

    Often people confuse hadoop with a database but hadoop is not a database instead it is a distributed file system for storing and processing large amounts of structured and unstructured data. The major difference between a traditional RDBMS and hadoop lies in the type of data they handle. RDBMS handle only relational data whilst Hadoop works well with unstructured data and provides support for different data formats like Avro, JSON, XML, etc. Hadoop and RDBMS have similar functionalities like collecting, storing, processing, retrieving and manipulating data, they both are different in the manner of processing data.

    RDBMS works well with small/medium scale defined database schema for real-time OLTP processing but it does not deliver fast results with vertical scalability even after adding additional storage or CPU’s. To the contrary, Hadoop effectively manages large sized structured and unstructured data in parallelism with superior performance at high fault-error tolerance rendering credible results at economical cost.

    If you would like to get in-depth insights on how hadoop differs from RDBMs, enrol now for Online Hadoop Training.

  • What is the significance of a Job Tracker in hadoop?

    Job Tracker is the core process involved in the execution of Hadoop MapReduce jobs. On a given Hadoop cluster, only one job tracker can run that submits and tracks all the MapReduce jobs. Job Tracker always runs on a separate node and not on the DataNode. The prime functionality of a job tracker is to manage the task trackers (resource management), track the availability of resources (locating task tracker nodes that have available slots for data), and task life cycle management (fault tolerance, tracking the progress of jobs, etc.).

    Job Tracker is a critical process within a Hadoop cluster because the execution of hadoop MapReduce jobs cannot be started until the Job Tracker is up and running. When the Job Tracker is down, the HDFS will still be functional but the execution of MapReduce jobs will be halted.

  • What is a NoSQL Database?

    NoSQL Databases - as the name suggests is 'not SQL' database. Any database which is not modeled after relational databases in tabular format with defined schemas - is a NoSQL database. NoSQL databases works on the paradigm that there are alternate storage solutions or mechanisms available when particular software is designed, that can be used based on the needs of the type of data. The data structures used by NoSQL databases like key/value pairs, graphs, documents, etc. differ from relational databases which makes operations on NoSQL databases faster. NoSQL databases solves the problems that relational databases were not able to cope with - increasing scale of data storage and agility, fast computing processes and cheap storage.

  • What is the difference between JobTracker and TaskTracker

    The JobTracker is responsible for taking in requests from a client and assigning Task tracker which task to be performed, whereas the TaskTracker accepts task from the JobTracker. The task tracker keeps sending a heartbeat message to the job tracker to notify that it is alive.

  • What are NameNodes and DataNodes?

    Name node is the master node which has all the metadata information. It contains the information about no. of blocks, size of blocks, no. of vacant blocks, no. of replicated blocks etc. DataNode is a slave node, which sends information to the Name node about the files and blocks stored in and responds to the Name node for all file.

  • What is the recommended Hardware requirement for efficient execution of Hadoop?

    Any Hadoop cluster will have these basic 4 roles; NameNode, JobTracker, TaskTracker and DataNode. The machines in the cluster will perform the task of Data storage and processing. In order to operate DataNode/TaskTracker in a balanced Hadoop cluster, the below mentioned configuration is recommended: -- 64 to 512 GB of RAM -- 2 quad-/hex-/octo-core CPUs (Minimum Operating Frequency 2.5 GHz) -- 10 Gigabit Ethernet or Bonded Gigabit Ethernet -- 12-24 1-4TB hard disks in a JBOD Hardware requirement for running NameNode and JobTracker are relaxed in terms of RAM size and memory, nearly one third; though it depends upon the requirement and redundancy.

  • When is MapReduce preferred over Spark?

    Hadoop MapReduce is a programming paradigm designed for database that exceeds the system memory, whereas on dedicated clusters Spark has less latency especially when all the data can be stored in system memory. In terms of ease of use, Hadoop MapReduce is difficult to program compared to Spark, but since it is widely used in Industry, a lot of tools are available to make it easier. Both of the technology offer more or less same amount of compatibility for data types and sources. As a conclusion, Hadoop MapReduce is better suited for Batch Processing whereas Spark is meant for Data Processing.

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Top 50 Hadoop Interview Questions


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


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News

Mining equipment-maker uses BI on Hadoop to dig for data.TechTarget.com, September 26, 2018.


Milwaukee based maker of mining equipment Count Komatsu Mining Corp. is looking to churn more data in place and share BI analytics of the data within and outside the organization.To enhance the efficiency, Count Komatsu has combined several big data tools that include Spark, Hadoop, Kafka , Kudu, and Impala from Cloudera. It has also included on-cluster analytics software from BI on Hadoop analytics toolmaker Arcadia Data. This big data platform has been assembled to analyse sensor data collected by the equipments in the field to keep a track on wear and tear of massive shovels and earth movers.The company forsees a future in which the platform will utilize IoT application data for better predictive and prescriptive equipment maintenance. (Source - https://searchdatamanagement.techtarget.com/feature/Mining-equipment-maker-uses-BI-on-Hadoop-to-dig-for-data )

Big-data project aims to transform farming in world’s poorest countries.September 24, 2018, Nature.com


Big data is really changing the way we use data for agriculture. FAO, the Bill and Melinda Gates Foundation and national governments have launched a US$500-million effort to help developing countries collect data on small-scale farmers to help fight hunger and and promote rural development. Collecting accurate information about seed varieties ,farmer’s technological capacity, and farmers income will help coalition members understand how ongoing agricultural investments are making an impact.This data will also enable governments to customize policies to help farmers. (Source - https://www.nature.com/articles/d41586-018-06800-8)

Microsoft’s SQL Server gets built-in support for Spark and Hadoop. September 24, 2018. Techcrunch.com,


Microsoft has announced the addition of new connectors which will allow businesses to use SQL server to query other databases like MongoDB, Oracle, and Teradata. This will make Microsoft SQL server into a virtual integration layer where the data will never have to be replicated or moved to the SQL server. SQL server in 2019 will come with in-built support for Hadoop and Spark. SQL server will provide support for big data clusters through Google-incubated Kubernetes container orchestration system. Every big data cluster will include SQL server, Hadoop and Spark file system. (Source - https://techcrunch.com/2018/09/24/microsofts-sql-server-gets-built-in-support-for-spark-and-hadoop/)

LinkedIn open-sources a tool to run TensorFlow on Hadoop.Infoworld.com, September 13, 2018.


LinkedIn’s open-source project Tony aims at scaling and managing deep learning jobs in Tensorflow using YARN scheduler in Hadoop.Tony uses YARN’s resource and task scheduling system to run Tensorflow jobs on a Hadoop cluster. LinkedIn’s open source project Tony can also schedule GPU based tensorflow jobs through Hadoop,allocate memory separately for Tensorflow nodes , request different types of resources (CPU’s vs GPU’s), and ensures that the job outcomes are saved at regular intervals on HDFS and resumed from where the jobs were interrupted or crashed.LinkedIn claims that there is no additional overhead for Tensorflow jobs when using Tony because it is present at a layer which orchestrates distributed Tensorflow and does not interrupt the execution of tensorflow jobs.Tony is also used for visualizing, optimization, and debugging of Tensorflow apps. (Source - https://www.infoworld.com/article/3305590/tensorflow/linkedin-open-sources-a-tool-to-run-tensorflow-on-hadoop.html )

Hortonworks unveils roadmap to make Hadoop cloud-native.Zdnet.com, September 10, 2018


Hortonworks unveils roadmap to make Hadoop cloud-native.Zdnet.com, September 10, 2018 Considering the importance cloud, Hortonworks is partnering with RedHat and IBM to transform Hadoop into a cloud-native platform.Today Hadoop can run in the cloud but it cannot exploit the capabilities of the cloud architecture to the fullest.The idea to make hadoop cloud-native is not a mere matter of buzzword compliance,but the goal is to make it more fleet-footed.25% of workloads from Hadoop incumbents - MapR, Hortonworks, and Cloudera are running in the cloud ,however, by next year it is anticipated that half of all the new big data workloads will be deployed on the cloud.Hortonworks is unveiling the Open Hybrid Architecture initiative for transforming Hadoop into a cloud-native platform that will address containerization, support Kubernetes, and include the roadmap to encompass separating compute from data. (Source - https://www.zdnet.com/article/hortonworks-unveils-roadmap-to-make-hadoop-cloud-native/ )

Hadoop Jobs

Data Engineer-Tech Services

Company Name: Uber
Location: San Francisco, CA
Date Posted: 14th Sep, 2018
Description:

Responsibilities

  • Work with large internal data sets and then lead the transformation of this data into important business insights.
  • You’ll build our data pipelines, create new tools and play a critical role in helping Uber integrate corporate data, analyze operational trends, make predictions and have a huge overall impact on Uber’s business efficiency.

Data Engineer

Company Name: Dropbox
Location: Mountain View, CA
Date Posted: 12th Sep, 2018
Description:

 

Responsibilities 

  • You will help define company data assets (data model), spark, sparkSQL and hiveSQL jobs to populate data models
  • You will help define/design data integrations, data quality frameworks and design/evaluate open source/vendor tools for data lineage
  • You will work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture

Data Developer

Company Name: Horizon Media
Location: New York
Date Posted: 24th Aug, 2018
Description:

Responsibilities:

Strategy/Business: 

  • Knowledge of Complex Event Processing (CEP) models for streaming and/or Near Real Time (NRT) data updates
  • Experience with topic creation using stream processing tools such as Kafka and/or Map R Streams
  • Development expertise with Lambda architecture models including Hadoop, Apache Flink, Apache Spark, or similar
  • Ability to provide batch processing direction when coupled with Lambda NRT processing
  • Deep knowledge of coding SQL RDBMS structures including Functions (Tabular & Scalar), Stored Procedures, in-memory processing objects, etc.
  • Knowledge of MDX OLAP query language and familia...