About Big Data Hadoop Course

Big Data Hadoop online training course is designed by certified experts as per industry standards and need to make you quite apt to grab top jobs and start your career as Big Data developer as thousands of other professionals have already done by joining this Hadoop course.


Become Hadoop Big Data expert by learning core Big Data technology and gain hands on knowledge of Hadoop along with its eco-system components like HDFS, Map-Reduce, Hive, Pig, HBase, Sqoop, Flume, Yarn and Apache Spark through this Hadoop course. For extensive hands-on, individual topics are explained using multiple workshops. The online Big data and Hadoop certification course also covers real life use-cases, multiple POCs, live Hadoop project and create foundation of Apache Spark for distributed data processing.

 

Objectives of Big Data Hadoop online Training

  • 1. Bring shift in your career as Big data has bought in IT world

  • 2. Grasp the concepts of HDFS and Map Reduce

  • 3. Become adept in latest version of Apache Hadoop

  • 4. Develop complex Game-Changing MapReduce Application

  • 5. Data analysis using Pig and Hive

  • 6. Play with NoSQL database – Apache HBase

  • 7. Acquire understanding of ZooKeeper service

  • 8. Data loading using Apache Sqoop and Flume

  • 9. Enforce best practices for Hadoop development and deployment

  • 10. Master handling of large data-set using Hadoop ecosystem

  • 11. Work on live project on Big Data analytics to get hands-on Experience

  • 12. Comprehend other Big Data technologies like Apache Spark


Upcoming Batch Schedule

START-DATETIMEDAYSDURATION
22 Sept - 28 Oct10.00 AM – 01.00 PM IST
09.30 PM – 12.30 AM PDT		Sat-Sun40 Hrs
8 Oct - 2 Nov09.00 PM – 11.00 PM IST
8.30 AM – 10.30 AM PDT		Mon-Fri40 Hrs
20 Oct - 25 Nov8.00 PM – 11.00 PM IST
10.30 AM – 01.30 PM EDT		Sat-Sun40 Hrs
10 Nov - 16 Dec10.00 AM – 01.00 PM IST
09.30 PM – 12.30 AM PDT		Sat-Sun40 Hrs
12 Nov - 7 Dec09.00 PM – 11.00 PM IST
8.30 AM – 10.30 AM PDT		Mon-Fri40 Hrs
8 Dec - 13 Jan8.00 PM – 11.00 PM IST
10.30 AM – 01.30 PM EDT		Sat-Sun40 Hrs

Why you should learn Big Data and Hadoop

big data salary Average salary of Big Data Hadoop Developers is $135k
-Indeed


shortage of big data talent McKinsey There will be a shortage of 1.5M Big Data experts by 2018
-McKinsey


big data hadoop market trends Hadoop market will reach $99B by 2022 at the CAGR of 42%
-Forbes


big data hadoop company priority More than 77% of organizations consider Big Data a top priority
-Peer Research


What will you get from this Big Data Course

live online instructor-led Hadoop training 40+ hrs of live online instructor-led Hadoop sessions by industry veterans


practicals, workshops, labs and assignments 100+ hrs of Hadoop practicals, workshops, labs, quiz and assignments


Real life case studies and live Big data project Real life Big Data case studies and live hadoop project to solve real problem


Lifetime access to Hadoop and Big Data Course Lifetime access to Hadoop course, recorded sessions and study materials


Hadoop discussion forum Discussion forum for resolving your queries and interacting with fellow batch-mates


Big Data certification Industry renowned Big Data certification to boost your resume


big data career discussion Personalized one to one career discussion directly with the trainer


resume preparation and Hadoop interviews Mock interview & resume preparation to excel in Hadoop interviews


job assistance and Big Data career Premium job assistance and support to step ahead in Big Data career


course auto upgradation Auto Upgradation of the course and study material in the LMS to latest versions


Who should go for this online Hadoop Course

YOU, yes you should go for this Big Data and Hadoop online course if you want to take a leap in your career as Hadoop developer. This course will be useful for:


  • 1. Software developers, Project Managers and architects

  • 2. BI, ETL and Data Warehousing Professionals

  • 3. Mainframe and testing Professionals

  • 4. Business analysts and Analytics professionals

  • 5. DBAs and DB professionals

  • 6. Professionals willing to learn Data Science techniques

  • 7. Any graduate focusing to build career in Big Data


Pre-requisites to attend Hadoop online course

Nothing can stop you from starting your career in Big Data. As such no prior knowledge of any technology is required to learn Big Data and Hadoop. In case you feel any need to revise your Java concepts, Java course will be provided in your LMS as complimentary with our Big Data and Hadoop tutorial course.



Big Data & Hadoop Course Curriculum

1.Big Picture of Big Data

1.What is Big Data
2.Necessity of Big Data in the industry
3.Paradigm Shift - why industry is shifting to Big Data tools
4.Different dimensions of Big Data
5.Data explosion in industry
6.Various implementations of Big Data
7.Different technologies to handle Big Data
8.Traditional systems and associated problems
9.Future of Big Data in IT industry

2.Demystify what is Hadoop

1.Why Hadoop is at the heart of every Big Data solution
2.Introduction to Hadoop framework
3.Hadoop architecture and design principles
4.Ingredients of Hadoop
5.Hadoop characteristics and data-flow
6.Components of Hadoop ecosystem
7.Hadoop Flavors – Apache, Cloudera, Hortonworks etc.

3.Setup and Installation of Hadoop

Setup and Installation of Single-Node Hadoop Cluster

1.Setup of Hadoop environment and pre-requisites
2.Installation and configuration of Hadoop
3.Work with Hadoop in pseudo-distributed mode
4.Troubleshooting the encountered problems

Setup and Installation of Hadoop multi-node Cluster

1.Setup Hadoop environment on the cloud (Amazon cloud)
2.Install Hadoop pre-requisites on all the nodes
3.Configuration of Masters and Slaves on Cluster
4.Play with Hadoop in distributed mode

4.HDFS – Storage Layer

1.What is HDFS - Hadoop Distributed File System
2.HDFS daemons and its Architecture
3.HDFS data flow and its storage mechanism
4.Hadoop HDFS Characteristics and design principles
5.Responsibility of HDFS Master – NameNode
6.Storage mechanism of Hadoop meta-data
7.Work of HDFS Slaves – DataNodes
8.Data Blocks and distributed storage
9.Replication of blocks, reliability and high availability
10.Rack-awareness, Scalability and other features
11.Different HDFS APIs and terminologies
12.Commissioning of nodes and addition of more nodes
13.Expand the cluster in real-time
14.Hadoop HDFS web UI and HDFS explorer
15.HDFS Best Practices and hardware discussion

5.Deep Dive into MapReduce

1.What is MapReduce - Processing layer of Hadoop
2.Need of distributed processing framework
3.Issues before MapReduce and its evolution
4.List processing Concepts
5.Components of MapReduce – Mapper and Reducer
6.MapReduce terminologies key, values, lists etc.
7.Hadoop MapReduce execution flow
8.Mapping data and reducing them based on keys
9.MapReduce word-count example to understand the flow
10.Execution of Map and Reduce together
11.Control the flow of mappers and reducers
12.Optimization of MapReduce Jobs
13.Fault-tolerance and data locality
14.Work with map-only jobs
15.Introduction to Combiners in MapReduce
16.How MR jobs can be optimized using Combiners

6.MapReduce - Advanced Concepts

1.Anatomy of MapReduce
2.Hadoop MapReduce data-types
3.Develop custom data-types using Writable & WritableComparable
4.What is InputFormats in MapReduce
5.How InputSplit is unit of work
6.How Partitioners partition the data
7.Customization of RecordReader
8.Move data from mapper to reducer – shuffling & sorting
9.Distributed Cache and job chaining
10.Different Hadoop case-studies to customize each component
11.Job scheduling in MapReduce

7.Hive – Data Analysis Tool

1.Need of adhoc SQL based solution – Apache Hive
2.Introduction and architecture of Hadoop Hive
3.Play with Hive shell and run HQL queries
4.Hive DDL and DML operations
5.Hive execution flow
6.Schema Design and other Hive operations
7.Schema on read vs Schema on write in Hive
8.Meta-store management and need of RDBMS
9.Limitation of default meta-store
10.Using serde to handle different types of data
11.Optimization of performance using partitioning
12.Different Hive applications and use cases

8.Pig – Data Analysis Tool

1.Need of high level query language - Apache Pig
2.How pig complements Hadoop with scripting language
3.What is Pig
4.Pig execution flow
5.Different Pig operations like filter and join
6.Compilation of pig code into MapReduce
7.Comparison between Pig vs MapReduce

9.NoSQL Database - HBase

1.NoSQL Databases and their need in the industry
2.Introduction to Apache HBase
3.Internals of HBase architecture
4.HBase master and slave model
5.Column-oriented, 3 dimensional, schema-less datastore
6.Data modeling in Hadoop HBase
7.Store multiple versions of data
8.Data high-availability and reliability
9.Comparison between HBase vs HDFS
10.Comparison between HBase vs RDBMS
11.Data access mechanism
12.Work with HBase using shell

10.Data Collection using Sqoop

1.Need of Apache Sqoop
2.Introduction and working of Sqoop
3.Import data from RDBMS to HDFS
4.Export data to RDBMS from HDFS
5.Conversion of data import / export query into MapReduce job

11.Data Collection using Flume

1.What is Apache Flume
2.Flume architecture and aggregation flow
3.Understand Flume components like data Source and Sink
4.Flume channels to buffer the events
5.Reliable & scalable data collection tool
6.Aggregate streams using Fan-in
7.Separate streams using Fan-out
8.Internals of agent architecture
9.Production architecture of Flume
10.Collect data from different sources to Hadoop HDFS
11.Multi-tier flume flow for collection of volumes of data using avro

12.Apache Yarn & Advanced concepts in latest version

1.Need and evolution of Yarn
2.What is Yarn and its eco-system
3.Yarn daemon architecture
4.Master of Yarn – Resource Manager
5.Slave of Yarn – Node Manager
6.Resource request from Application master
7.Dynamic slots called containers
8.Application execution flow
9.MapReduce version 2 application over Yarn
10.Hadoop Federation and Namenode HA

13. Processing Data with Apache Spark

1. Introduction to Apache Spark
2. Comparison between Hadoop MapReduce vs Apache Spark
2. Spark key Features
3. RDD and various RDD operations
4. RDD Abstraction, interface and creation of RDDs
5. Fault Tolerance in Spark
6. Spark Programming model
7. Data Flow in Spark
8. Spark Ecosystem, its Hadoop compatibility & integration
9. Installation & Configuration of Spark
10. Process Big Data using Spark

14.Real Life Project on Big Data

Live Hadoop project based on industry use-case using Hadoop components like Pig, HBase, MapReduce and Hive to solve real world problems in Big Data Analytics



Big Data & Hadoop Projects


Web Analytics
Weblogs are web server logs, where web servers like apache records all the events along with remote-IP, time-stamp, requested-resource, referral, user-agent, etc. The objective is to analyze the weblogs and generate insights like user navigation pattern, top referral sites, highest/lowest traffic-time, etc.



Sentiment Analysis
Sentiment analysis is the analysis of people’s opinions, sentiments, evaluations, appraisals, attitudes and emotions in relation to entities like individuals, products, events, services, organizations and topics by classifying the expressions as negative / positive opinions




Crime Analysis
Analyze the US crime data and find most crime-prone area along with crime time and its type. The objective is to analyze the crime data and generate crime patterns like time, district, crime-type, latitude, longitude, etc. So that additional security measures can be taken in crime prone area.



IVR Data Analysis
Analyze IVR (Interactive Voice Response) data and generate various insights. The IVR call records are analyzed to optimize to IVR system so that maximum calls are completed at IVR and there will be minimum need for Call-center.





Titanic Data Analysis
Titanic was one of the biggest disasters in the history of mankind, which happened due to natural events and human mistakes. The objective is to analyze Titanic data sets and generate various insights related to age, gender, survived, class, emabrked, etc.




Amazon Data Analysis
Amazon data-sets contains user-reviews of different products, services, star-ratings, etc. The objective of the project is to analyze the users' review data, companies can analyze the sentiments of the users regarding their products and use it for betterment of the same.





Course Plans

Features

 Course mode

 Extensive hands-on practicals

Access Duration

Real-life Project

No of Projects

Discussion Forum Access

Doubt Clearance

Certification

Complementary Courses

Complementary Job Assistance

Resume & Interview Preparation

Interaction in Live class

Personalized career guidance 

Course Objective

Self-Paced Pro Course

Rs. 9990 | $181

Rs. 4990 | $90

Video Based

Yes, in recordings & in LMS

Lifetime

Yes

One

Lifetime

Through discussion forum

Yes, post course completion

Java, with lifetime access

Express Learning

Enroll Now!

Live Instructor-Led Course

Rs. 18990 | $345

Rs. 12990 | $236

Live Online with Trainer

Yes, live with instructor & in LMS

Lifetime

Yes, with support

Five

Lifetime

In regular sessions

Yes, post course completion

Java & Storm, with lifetime access

100% interactive classes

Yes, from instructor

Job readiness

Enroll Now!
Popular!


Customer Reviews

Rohit Totala
Rohit TotalaTrainee Decision Scientist at Mu Sigma
I feel confident about Hadoop and has generated interest in this field .I am looking forward to learn more and make a successful career in it.Course content was up to date and material provided was very useful and sufficient enough ...

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Dinesh Rajput
Dinesh RajputTechnical Lead (Bigdata & analytics), IndiaMART InterMESH Limited
I had a great experience in taking the Hadoop course from Dataflair.It is the only course in the market which facilitates the people from the Non development background to plug themselves into the Hadoop ecosystem. Dataflair has provided a unique ...

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Venkata Veluguri
Venkata VeluguriPrincipal DW Architect, Comcast
After attending the training I got Very good knowledge about Big Data, Tools and technologies available, architecture and some extent hands on experience. The course contents and material provided is very much useful. The trainer is very knowledgeable and interactive. ...

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Akshay Anand
Akshay AnandIT Professional
It was a great experience of learning Hadoop online with DataFlair.The concepts were concise.The materials were quite illustrative and Sir explained it handsomely. The study material is good and very informative. Anish Sir was supportive and made us understand the ...

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Veeresh Kottargi
Veeresh KottargiHadoop developer, Cognizant
Got job in Cognizant as Hadoop developer after training from DataFlair.It's really been great set of classes with DataFlair . Course material is awesome. Simple and understandable by everyone. Our Trainer Anish, is a very good teacher. Has ...

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Roland Lim
Roland LimBSS and Big Data Consultant at GlobeOSS
The training met my objective of getting to know quickly learn the Hadoop and the underlying framework and technology. The Hadoop course content was fine and enhanced my learning with real life use-case studies examples. Also code samples for different ...

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Saurabh Shrivastava
Saurabh ShrivastavaData Architect / Data Modeler, Citi Bank
After attending the demo session on Big Data Hadoop from DataFlair, I was motivated to Learn more in the field of Hadoop. I joined the training as I liked the demo very much. The instructor is highly experienced with great ...

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Nitin Raichandani
Nitin RaichandaniSr. Technical Consultant at Teradata India Pvt. Ltd.
When I started this training I was not having any knowledge about Hadoop or Java. I am altogether from a non-programming side. The complementary Java course of Dataflair helped me a lot in gaining good understanding of Java concepts. And ...

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Hadoop Training FAQs

How will you help me if I miss any Hadoop training session?

If you miss any session, you need not worry as recordings will be uploaded in LMS immediately as the session gets over. You can go through it and get your queries cleared from the instructor during next session. You can also ask him to explain the concepts that you did not understand and were covered in session you missed. Alternatively you can attend the missed session in any other batch running parallely.

How will I do Big Data Hadoop practicals at home?

Instructor will help you in setting virtual machine on your own system at which you can do practicals anytime from anywhere. Manual to set virtual machine will be available in your LMS in case you want to go through the steps again. Virtual machine can be set on MAC or Windows machine also.

How long will the Hadoop course recording available with me?

All the sessions will be recorded and you will have lifetime access to the recordings along with the complete Hadoop study material, POCs, Hadoop project etc.

What things do I need to attend Hadoop online classes?

To attend online Hadoop training, you just need a laptop or PC with a good internet connection of around 1 MBPS (But the lesser speed of 512 KBPS will also work). The broadband connection is recommended but you can connect through data card as well.

How can I get my doubts cleared post class gets over?

If you have any doubt during any session, you can get it cleared from the instructor immediately. If you get queries after the session, you can get it cleared from the instructor in the next session as before starting any session, instructor spends around 15 minutes in doubt clearing. Post training, you can post your query over discussion forum and our support team will assist you. Still if you are not comfortable, you can drop mail to instructor or directly interact with him.

What are the system specifications required to learn Hadoop?

Recommended is minimum of i3 processor, 20 GB disk and 4 GB RAM in order to learn Hadoop although students have learnt Hadoop on 2 GB RAM as well.

How this Hadoop course will help me in getting Big Data job?

Our training includes multiple workshops, POCs, project etc. that will prepare you to the level that you can start working from day 1 wherever you go. You will be assisted in resume preparation. The mock interview will help you in getting ready to face interviews. We will also guide you with the job openings matching to your resume. All this will help you in getting your dream Big Data job in the industry.

What will be the end result of doing this hadoop online course?

You will be skilled with the practical and theoretical knowledge that industry is looking for and will become certified Hadoop professional who is ready to take Big Data Projects in top organizations.

Where does DataFlair students come from?

DataFlair has blend of students from across the globe. Apart from India, we provide Hadoop training in US, UK, Singapore, Canada, UAE, France, Brazil, Ireland, Indonesia, Japan, Sri Lanka, etc to cover the complete globe.

How will I be able to interact with the instructor during training?

Both voice and chat will be enabled during the Big Data Hadoop training sessions. You can talk with the instructor or can also interact via chatting.

Is this Hadoop classroom training or online?

This is completely online training with a batch size of 8-10 students only. You will be able to interact with trainer through voice or chat and individual attention will be provided to all. The trainer ensures that every student is clear of all the concepts taught before proceeding ahead. So there will be complete environment of classroom learning.

Do you provide any Hadoop certification after training?

Yes, you will be provided DataFlair Certification. At the end of this course, you will work on a real time Project. Once you are successfully through the project, you will be awarded a certificate.

For further queries, how can I get in touch with you?

You can feel free to contact us by placing a CALL at +91 8451097879 OR dropping your queries on our email at info@data-flair.com

Why should I learn Hadoop?

Big data is the latest and the most demanding technology with continuously increasing demand in the Indian market and abroad. Hadoop professionals are among the highest paid IT professionals today with salary $135k (source: indeed job portal). You can check our blog related to Why should I learn Big Data?

What type of projects I will be doing during the training?

You will be doing real-time Hadoop projects in different domains like retail, banking, and finance, etc. using different technologies like Hadoop HDFS, MapReduce, Apache Pig, Apache Hive, Apache HBase, Apache Oozie, Apache Flume and Apache Sqoop.

Do you provide placement assistance?

The Hadoop course from DataFlair is 100% job oriented that will prepare you completely for interview and Big Data job perspective. Post Big Data course completion, we will provide you assistance in resume preparation and tips to clear Hadoop interviews. We will also let you know for Hadoop jobs across the globe matching your resume.

Can I attend a demo session before enrolling for Hadoop course?

Yes, you can attend the Hadoop demo class recording on our Big data Hadoop course page itself to understand the quality and level of Big Data training we provide and that creates the difference between DataFlair and other Hadoop online training providers.

Can you guide me about career of Big Data hadoop developer.

Hadoop is one of the hottest career options available today for all the software engineers to boost their professional career. In the US itself there are approximately 12,000 jobs currently for Hadoop developers and demand for Hadoop developers are increasing day by day rapidly far more than the availability.

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Skills to Become a Successful Data Scientist

"Data scientist is termed to be the “sexiest job of the 21st century". In this tutorial we will discuss about the skills you must learn to become a successful data scientist. What are the qualifications needed for data scientist, different data science certification programs, data scientist’s job description.

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Hadoop Tutorial – A Comprehensive Guide

This Hadoop tutorial provides thorough introduction of Hadoop. The tutorial covers what is Hadoop, what is the need of Hadoop, why hadoop is most popular, Hadoop Architecture, data flow, Hadoop daemons, different flavours, introduction of Hadoop components like hdfs, MapReduce, Yarn, etc.

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