Hadoop Training with experts

Our certified faculty focused on IT industry based curriculum for Hadoop Training Online. Hands-on training with tutorial videos real-time scenarios and certification guidance that helps you to enhance your career skills.

Self-Paced learning

➤ Practical Training
➤ by Certified Faculty
➤ 100% Hands-on Classes
➤ Real time Scenarios
➤ Life Time Course Access
➤ pre-recorded videos
➤ Free Complimentary Materials

Instructor Led Training

➤ Live Instructor Led Classes
➤ 100% Hands-on Classes
➤ Real time Scenarios
➤ Faculty will Provide work environment
➤ Instant Doubt Clarification
➤ Course Duration: 35 Hours
➤ CV, Job and Certification Guidance

Course Overview

➤ 8 years real time experience
➤ Successfully trained more than 60 batches
➤ Concentrates on 30% theoretical and 70% on practical.
➤ Faculty clears all the doubts during the session.

Any graduate can pick their choice to specialize in this particular module.

Yes during the course, we will guide you and give you clear picture about certification procedure.

Yeah, Even after the completion of course, we will provide you some interview questions where you can concentrate on them.

If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within 48 hours of initial registration. Please make a note that refunds will be processed within 30 days of prior request.

This hands-on Introduction to Big Data preparing gives an exceptional way to deal with help you follow up on information for genuine business gain. The center isn’t what a device can do, however what you can do with the yield from the apparatus. Gain the aptitudes you have to store, oversee, process, and dissect huge measures of unstructured information to make a fitting information lake.

Hadoop Installation and setup

The design of Hadoop 2.0 group, what is High Availability and Federation, how to setup a generation bunch, the different shell directions in Hadoop, understanding arrangement records in Hadoop 2.0, introducing single hub bunch with Cloudera Manager, understanding Spark, Scala, Sqoop, Pig and Flume.

Prologue to Big Data Hadoop. Understanding HDFS and Mapreduce

Presenting Big Data and Hadoop, what is Big Data and where does Hadoop fits in, two vital Hadoop biological system componentsnamely Map Reduce and HDFS, inside and out Hadoop Distributed File System – Replications, Block Size, Secondary Name hub, High Availability, top to bottom YARN – Resource Manager, Node Manager.

Hands-on Exercise – HDFS working system, information replication process, how to decide the measure of the square, understanding a DataNode and NameNode.

Profound Dive in Mapreduce

Learning the working component of MapReduce, understanding the mapping and diminishing stages in MR, the different wordings in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle and Sort

Hands-on Exercise – How to compose a Word Count program in MapReduce, how to compose a custom Partitioner, what is a MapReduce Combiner, how to run an occupation in a nearby activity sprinter, sending unit test, what is a guide side join and diminish side join, what is an apparatus sprinter, how to utilize counters, dataset joining with guide side and decrease side joins.

Prologue to Hive

Presenting Hadoop Hive, definite design of Hive, contrasting Hive and Pig and RDBMS, working with Hive Query Language, production of database, table, Group by and different conditions, the different sorts of Hive tables, Hcatalog, putting away the Hive Results, Hive dividing and Buckets.

Hands-on Exercise – Database creation in Hive, dropping a database, Hive table creation, how to change the database, information stacking, Hive table creation, dropping and adjusting table, pulling information by composing Hive inquiries with channel conditions, table apportioning in Hive, what is a gathering by statement

Advance Hive and Impala

The ordering in Hive, the Map side Join in Hive, working with complex information types, the Hive User-characterized Functions, Introduction to Impala, contrasting Hive and Impala, the nitty gritty engineering of Impala

Hands-on Exercise – How to work with Hive inquiries, the way toward joining table and composing lists, outer table and succession table sending, information stockpiling in an alternate table.

Prologue to Pig

Apache Pig presentation, its different highlights, the different information types and diagram in Hive, the accessible capacities in Pig, Hive Bags, Tuples and Fields.

Hands-on Exercise – Working with Pig in MapReduce and nearby mode, stacking of information, constraining information to 4 lines, putting away the information into document, working with Group By,Filter By,Distinct,Cross,Split in Hive.

Flume, Sqoop and HBase

Apache Sqoop presentation, diagram, bringing in and sending out information, execution enhancement with Sqoop, Sqoop restrictions, prologue to Flume and understanding the engineering of Flume, what is HBase and the CAP hypothesis.

Hands-on Exercise – Working with Flume to producing of Sequence Number and expending it, utilizing the Flume Agent to devour the Twitter information, utilizing AVRO to make Hive Table, AVRO with Pig, making Table in HBase, sending Disable, Scan and Enable Table.

Composing Spark Applications utilizing Scala

Utilizing Scala for composing Apache Spark applications, definite investigation of Scala, the requirement for Scala, the idea of article situated programing, executing the Scala code, the different classes in Scala like Getters,Setters, Constructors, Abstract ,Extending Objects, Overriding Methods, the Java and Scala interoperability, the idea of utilitarian programming and unknown capacities, Bobsrockets bundle, looking at the impermanent and permanent accumulations.

Hands-on Exercise – Writing Spark application utilizing Scala, understanding the vigor of Scala for Spark constant examination task.

Start system

Point by point Apache Spark, its different highlights, contrasting and Hadoop, the different Spark segments, consolidating HDFS with Spark, Scalding, prologue to Scala, significance of Scala and RDD.

Hands-on Exercise – The Resilient Distributed Dataset in Spark and how it accelerates enormous information preparing.

RDD in Spark

Understanding the Spark RDD tasks, correlation of Spark with MapReduce, what is a Spark change, stacking information in Spark, sorts of RDD activities viz. change and activity, what is Key Value combine.

Hands-on Exercise – How to send RDD with HDFS, utilizing the in-memory dataset, utilizing record for RDD, how to characterize the base RDD from outside document, conveying RDD by means of change, utilizing the Map and Reduce capacities, taking a shot at word check and tally log seriousness.

Information Frames and Spark SQL

The definite Spark SQL, the essentialness of SQL in Spark for working with organized information preparing, Spark SQL JSON bolster, working with XML information, and parquet documents, making HiveContext, composing Data Frame to Hive, How to peruse a JDBC record, centrality of a Spark Data Frame, how to make a Data Frame, what is mapping manual surmising, how to work with CSV documents, JDBC table perusing, information transformation from Data Frame to JDBC, Spark SQL client characterized capacities. shared variable and collectors, how to question and change information in Data Frames, how Data Frame gives the advantages of both Spark RDD and Spark SQL, sending Hive on Spark as the execution motor.

Hands-on Exercise – Data questioning and change utilizing Data Frames, discovering the advantages of Data Frames over Spark SQL and Spark RDD.

Machine Learning utilizing Spark (Mlib)

Prologue to Spark MLlib, understanding the different calculations, what is Spark iterative calculation, Spark diagram preparing investigation, presenting machine learning, K-Means bunching, Spark factors like shared and communicate factors, what are collectors.

Hands-on Exercise – Writing sparkle code utilizing Mlib.

Start Streaming

Prologue to Spark gushing, the design of Spark Streaming, working with the Spark spilling program, handling information utilizing Spark gushing, asking for tally and Dstream, multi-cluster and sliding window tasks and working with cutting edge information sources.

Hands-on Exercise – Deploying Spark gushing for information in movement and checking the yield is according to the prerequisite.

Hadoop Administration – Multi Node Cluster Setup utilizing Amazon EC2

Make a four hub Hadoop bunch setup, running the MapReduce Jobs on the Hadoop group, effectively running the MapReduce code, working with the Cloudera Manager setup.

Hands-on Exercise – The technique to fabricate a multi-hub Hadoop group utilizing an Amazon EC2 case, working with the Cloudera Manager.

Hadoop Administration – Cluster Configuration

The diagram of Hadoop design, the significance of Hadoop arrangement record, the different parameters and estimations of setup, the HDFS parameters and MapReduce parameters, setting up the Hadoop condition, the Include’ and Exclude setup documents, the organization and upkeep of Name hub, Data hub registry structures and records, What is a File framework picture, understanding Edit log.

Hands-on Exercise – The procedure of execution tuning in MapReduce.

Hadoop Administration – Maintenance, Monitoring and Troubleshooting

Prologue to the Checkpoint Procedure, Name hub disappointment and how to guarantee the recuperation methodology, Safe Mode, Metadata and Data reinforcement, the different potential issues and arrangements, what to search for, how to include and evacuate hubs.

Hands-on Exercise – How to approach guaranteeing the MapReduce File framework Recovery for different distinctive situations, JMX observing of the Hadoop bunch, how to utilize the logs and stack follows for checking and investigating, utilizing the Job Scheduler for planning occupations in a similar group, landing the MapReduce position accommodation stream, FIFO plan, becoming acquainted with the Fair Scheduler and its design

ETL Connectivity with Hadoop Ecosystem

How ETL instruments function in Big information Industry, Introduction to ETL and Data warehousing. Working with noticeable use instances of Big information in ETL industry, End to End ETL PoC indicating enormous information combination with ETL apparatus.

Hands-on Exercise – Connecting to HDFS from ETL apparatus and moving information from Local framework to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce work in ETL device

Undertaking Solution Discussion and Cloudera Certification Tips and Tricks

Progressing in the direction of the arrangement of the Hadoop venture arrangement, its concern articulations and the conceivable arrangement results, getting ready for the Cloudera Certifications, focuses to center for scoring the most astounding imprints, tips for breaking Hadoop inquiries questions.

Hands-on Exercise – The task of a certifiable high esteem Big Data Hadoop application and getting the correct arrangement dependent on the criteria set by the Intellipaat group.

Following themes will be accessible just in self-managed Mode.

Hadoop Application Testing

Why testing is vital, Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and start to finish tests, Functional testing, Release accreditation testing, Security testing, Scalability Testing, Commissioning and Decommissioning of Data Nodes Testing, Reliability testing, Release testing.

Jobs and Responsibilities of Hadoop Testing Professional

Understanding the Requirement, arrangement of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report conveyance, Test fulfillment, ETL testing at each stage (HDFS, HIVE, HBASE) while stacking the information (logs/documents/records and so on) utilizing sqoop/flume which incorporates however not restricted to information confirmation, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges and so on), Report deformities to the improvement group or supervisor and driving them to conclusion, Consolidate every one of the imperfections and make deformity reports, Validating new component and issues in Core Hadoop.

System called MR Unit for Testing of Map-Reduce Programs

Report imperfections to the advancement group or director and driving them to conclusion, Consolidate every one of the deformities and make deformity reports, Responsible for making a testing Framework called MR Unit for testing of Map-Reduce programs.

Unit Testing

Robotization testing utilizing the OOZIE, Data approval utilizing the inquiry flood instrument.

Test Execution

Test plan for HDFS update, Test computerization and result

Test Plan Strategy and composing Test Cases for testing Hadoop Application

Instructions to test introduce and arrange

What Hadoop Projects You will chip away at?

Venture 1 : Working with MapReduce, Hive, Sqoop

Industry : General

Issue Statement : How to effectively import information utilizing Sqoop into HDFS for information investigation.

Themes : As a component of this venture you will deal with the different Hadoop segments like MapReduce, Apache Hive and Apache Sqoop. Work with Sqoop to import information from social database the board framework like MySQL information into HDFS. Send Hive for outlining information, questioning and investigation. Convert SQL inquiries utilizing HiveQL for conveying MapReduce on the exchanged information. You will increase extensive capability in Hive, and Sqoop after culmination of this venture.

Features :

Sqoop information exchange from RDBMS to Hadoop

Coding in Hive Query Language

Information questioning and investigation.

Venture 2 : Work on MovieLens information for discovering top motion pictures

Industry : Media and Entertainment

Issue Statement : How to make the main ten motion pictures list utilizing the MovieLens information.

Points : In this undertaking you will work solely on information gathered through MovieLens accessible rating informational indexes. The undertaking includes composing MapReduce program to examine the MovieLens information and make rundown of best ten motion pictures. You will likewise work with Apache Pig and Apache Hive for working with dispersed datasets and breaking down it.

Features :

MapReduce program for dealing with the information record

Apache Pig for dissecting information

Apache Hive information warehousing and questioning

Task 3 : Hadoop YARN Project – End to End PoC

Industry : Banking

Issue Statement : How to bring the day by day information ( steady information) into the Hadoop Distributed File System.

Points : In this venture we have exchange information which is every day recorded/store in the RDBMS. Presently this information is moved ordinarily into HDFS for further Big Data Analytics. You will take a shot at live Hadoop YARN bunch. YARN is a piece of the Hadoop 2.0 biological community that lets Hadoop to decouple from MapReduce and send increasingly focused preparing and more extensive cluster of utilizations. You will chip away at the YARN focal Resource Manager.

Features :

Utilizing Sqoop directions to bring the information into HDFS

Start to finish stream of exchange information

Working with the information from HDFS

Venture 4 : Table Partitioning in Hive

Industry : Banking

Issue Statement : How to enhance the question speed utilizing Hive information apportioning.

Themes : This venture includes working with Hive table information parceling. Guaranteeing the correct apportioning peruses the information, convey it on the HDFS, and run the MapReduce employments at an a lot quicker rate. Hive gives you a chance to parcel information in numerous ways. This will give you hands-on involvement in apportioning of Hive tables physically, sending single SQL execution in unique dividing, bucketing of information in order to break it into sensible pieces.

Features :

Manual Partitioning

Dynamic Partitioning

Bucketing

Task 5 : Connecting Pentaho with Hadoop Ecosystem

Industry : Social Network

Issue Statement : How to send ETL for information examination exercises.

Points : This undertaking gives you a chance to interface Pentaho with the Hadoop biological system. Pentaho functions admirably with HDFS, HBase, Oozie and Zookeeper. You will interface the Hadoop bunch with Pentaho information coordination, examination, Pentaho server and report creator. This task will give you complete working information on the Pentaho ETL apparatus.

Features :

Working information of ETL and Business Intelligence

Arranging Pentaho to work with Hadoop Distribution

Stacking, Transforming and Extracting information into Hadoop group

Task 6 : Multi-hub bunch setup

Industry : General

Issue Statement : How to setup a Hadoop continuous bunch on Amazon EC2.

Points : This is a venture that gives you chance to deal with genuine world Hadoop multi-hub group setup in an appropriated situation. You will get a total exhibition of working with different Hadoop bunch ace and slave hubs, introducing Java as an essential for running Hadoop, establishment of Hadoop and mapping the hubs in the Hadoop group.

Features :

Hadoop establishment and setup

Running a Hadoop multi-hub utilizing a 4 hub bunch on Amazon EC2

Conveying of MapReduce work on the Hadoop bunch.

Task 7 : Hadoop Testing utilizing MRUnit

Industry : General

Issue Statement : How to test MapReduce applications

Points : In this undertaking you will pick up capability in Hadoop MapReduce code testing utilizing MRUnit. You will find out about genuine situations of conveying MRUnit, Mockito, and PowerMock. This will give you hands-on involvement in the different testing instruments for Hadoop MapReduce. After culmination of this task you will be knowledgeable in test driven improvement and will have the capacity to compose light-weight test units that work explicitly on the Hadoop engineering.

Features :

Composing JUnit tests utilizing MRUnit for MapReduce applications

Doing mock static strategies utilizing PowerMock and Mockito

MapReduce Driver for testing the guide and diminish match

Undertaking 8 : Hadoop Weblog Analytics

Industry : Internet administrations

Issue Statement : How to get bits of knowledge from web log information

Subjects : This undertaking is included with comprehending all the web log information so as to get profitable bits of knowledge from it. You will work with stacking the server information onto a Hadoop group utilizing different systems. The web log information can incorporate different URLs visited, treat information, client socioeconomics, area, date and time of web benefit get to, and so on. In this venture you will transport the information utilizing Apache Flume or Kafka, work process and information purging utilizing MapReduce, Pig or Spark. The understanding therefore determined can be utilized for breaking down client conduct and anticipate purchasing behaviors.

Features :

Collection of log information

Apache Flume for information transportation

Preparing of information and creating investigation

Venture 9 : Hadoop Maintenance

Industry : General

Issue Statement : How to regulate a Hadoop group

Themes : This task is included with taking a shot at the Hadoop group for keeping up and overseeing it. You will deal with various imperative assignments that incorporate recouping of information, recuperating from disappointment, including and expelling of machines from the Hadoop group and onboarding of clients on Hadoop.

Features :

Working with name hub index structure

Review logging, information hub square scanner, balancer.

Failover, fencing, DISTCP, Hadoop document positions.

Undertaking 10 : Twitter Sentiment Analysis

Industry – Social Media

Issue Statement : Find out what is the response of the general population to the demonetization move by India by breaking down their tweets.

Depiction : This Project includes dissecting the tweets of individuals by experiencing what they are stating about the demonetization choice taken by the Indian government. At that point you search for key expressions, words and investigate them utilizing the lexicon and the esteem credited to them dependent on the estimation that it is passing on.

Features :

Download the Tweets and Load into Pig Storage

Gap tweets into words to ascertain assessment

Rating the words from +5 to – 5 on AFFIN lexicon

Sifting the Tweets and investigating assessment.

Task 11 : Analyzing IPL T20 Cricket

Industry – Sports and Entertainment

Issue Statement : Analyze the whole cricket coordinate and find solutions to any question with respect to the subtleties of the match.

Depiction : This venture includes working with the IPL dataset that has data in regards to batting, knocking down some pins, runs scored, wickets taken, and that’s only the tip of the iceberg. This dataset is taken as info and after that it is handled with the goal that the whole match can be broke down dependent on the client questions or needs.

Features :

Load the information into HDFS

Dissect the information utilizing Apache Pig or Hive

In light of client questions give the correct yield

Apache Spark Projects

Venture 1 – Movie Recommendation

Industry : Entertainment

Issue Statement : How to prescribe the most fitting film to a client dependent on his taste

Themes :This is a hands-on Apache Spark venture sent for this present reality utilization of motion picture suggestions. This task causes you increase basic information in Spark MLlib which is a machine learning library, you will realize how to make community oriented separating, relapse, grouping and dimensionality decrease utilizing Spark MLlib. After completing the venture you will have direct involvement in the Apache Spark spilling information examination, inspecting, testing, and measurements among other fundamental abilities.

Features :

Apache Spark MLlib part

Measurable examination

Relapse and grouping

Undertaking 2 – Twitter API Integration for tweet Analysis

Industry : Social Media

Issue Statement : Analyzing the client assumption dependent on the tweet

Subjects :This is a hands-on Twitter examination venture utilizing the Twitter API fo

Upcoming Batches

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On Saturday & Sunday

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If not weekend, We can reschedule

Course Curriculum

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Topic 01. Overview of Big Data
This includes topics such as history of big data, its elements, career related knowledge, advantages, disadvantages and similar topics.

Topic 02. Using Big Data in Businesses
This module should focus on the application perspective of Big Data covering topics such as using big data in marketing, analytics, retail, hospitality, consumer good, defense etc.

Topic 03. Technologies for Handling Big Data
Big Data is primarily characterized by Hadoop. This module cover topics such as Introduction to Hadoop, functioning of Hadoop, Cloud computing (features, advantages, applications) etc

Topic 04. Understanding Hadoop Ecosystem
This includes learning about Hadoop and its ecosystem which includes HDFS, MapReduce, YARN, HBase, Hive, Pig, Sqoop, Zookeeper, Flume, Oozie etc.

Topic 05. Dig Deep to understand the fundamental of MapReduce and HBase
This module should cover the entire framework of MapReduce and uses of mapreduce.

Topic 06. Seeing Big Data Technology Foundations

This module covers the huge information stack for example information source layer, ingestion layer, source layer, security layer, perception layer, representation approaches and so forth.

Topic 07. Databases and Data Warehouses

This module should cover about databases, polygot tirelessness and their related initial learning

Topic 08. Utilizing Hadoop to store information

This incorporates a whole module of HDFS, HBase and their individual approaches to store and oversee information alongside their directions.

Topic 09. Figure out how to Process Data utilizing Map Reduce

This underscores on creating basic mapreduce system and the ideas connected to it.

Topic 10. Testing and Debugging Map Reduce Applications

After the applications are produced, the subsequent stage is to test and investigate it. This modules grants this learning.

Topic 11. Learn Hadoop YARN Architechture

This module covers the foundation of YARN, focal points of YARN, working with YARN, in reverse similarity with YARN, YARN Commands, log the board and so forth.

Topic 12. Investigating Hive

This modules presents you with all the fundamental information of Hive.

Topic 13. Investigating Pig

This modules presents you with all the fundamental information of PIG.

Topic 14. Investigating Oozie

This modules presents you with all the fundamental information of Oozie.

Topic 15. Learn NoSQL Data Management

This modules covers about NoSQL including archive databases, connections, chart databases, mapping less databases, CAP Theorem and so on.

Topic 16. Coordinating R and Hadoop and Understanding Hive in Detail

This module acquaints you with RHadoop, approaches to do content mining and related learning.

In this article, I’ve secured the total schedule of Big Data Technologies. This schedule should give you a thorough diagram of the points that you should cover in your forthcoming enormous information preparing. In the event that you understand, that your preparation doesn’t have any of the referenced module in the schedule, I’d prescribe you to connect with the course executive and get this thing arranged.

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Hadoop Training FAQ's!

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Divya Teja
Divya Teja
India
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usa
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Linson Valappila
London
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india
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Siva Kumar
india
“I am very happy to be a part of SVR Technologies. I have started my course training in a very friendly environment. I am very scared and reserved kind before starting the course but later, I felt very comfortable with the SVR environment. The faculty always supported with positive guidance.”
Shanthan Reddy
Shanthan Reddy
india
“It was nice experience with SVR the teaching process and teacher both were good. Overall good experience.”

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