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Hadoop Certification Training in Chennai

Looking for Hadoop Training in Chennai with Certification and Placements ?

Learn Hadoop developer, Hadoop administrator, Hadoop testing courses with India’s #1 ranked Hadoop training and placement institute with real world projects and extensive job placement support, all designed to help you become a Hadoop Architect.

Most comprehensive Online Hadoop Training using HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Attend this Hadoop Certification Training Course in our Classroom or Instructor-Led Online Training.

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Hadoop Training in chennai

One to One Training

Get 1-to-1 Live Instructor Led Online Training in flexible timings

Course Price at: ₹ 21,000
Discount Price: ₹ 18,000

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Attend our Instructor Led Online Virtual Classroom

Course Price at: ₹ 18,000
Discount Price: ₹ 15,000

Upcoming Batches - Big Data Hadoop Certification Training Course LIVE Schedule: Limited Enrollments

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Hadoop Course Overview

Hadoop Training in chennaiIn this hands-on Big Data Hadoop training course, you will execute real-life, industry-based projects using Integrated Lab. This is an industry-recognized Hadoop certification Classes that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop Tester and analytics using Apache Spark.

Our Hadoop certification training course lets you master the concepts of the Hadoop framework, preparing you for Cloudera Certified Associate (CCA) AND HDP Certified Developer (HDPCD). Learn how Hadoop Ecosystem components fit into the Big Data Analytics Lifecycle.

What will you learn in this Hadoop online training?

  • Hadoop Certification Training
  • Hadoop Administration Certification Training
  • Apache Spark and Scala Certification Training
  • Python Spark Certification Training using PySpark
  • Apache Kafka Certification Training
  • Splunk Training & Certification- Power User & Admin
  • Hadoop Administration Certification Training
  • ELK Stack Training & Certification
  • Apache Solr Certification Training
  • Comprehensive Pig Certification Training
  • Comprehensive Hive Certification Training
  • Comprehensive HBase Certification Training
  • MapReduce Design Patterns Certification Training
  • Mastering Apache Ambari Certification Training
  • Comprehensive MapReduce Certification Training
  • Apache Storm Certification Training

This Big Data Analytics Certification Courses in Chennai is taught keeping the careers of aspirants in mind. It will move along from introducing you to Popular Big Data Analytics Courses

  • Hadoop and Spark Developer
  • MongoDB Developer and Administrator
  • Apache Scala and Spark
  • Apache Kafka

If an aspirant is new to the IT field, wants to learn and update Big data, and want to pursue a career in Analytics or if an aspirant wants to make a career move from a different technology, this course is just apt. In this course, our trainers would guide  you by providing the most practical things required to get and survive a Big data job.

Below mentioned aspirants can learn Big data course:

  • Any college fresher/graduate can learn Big data training program.
  • Any experienced professional from any other field who wants to switch career into Big data,
  • Any experienced professional, who wants to upgrade themselves to learn  advanced tactics and  to work efficiently and smartly in this field.
  1. Highly Interactive: All of our sessions are highly interactive as we encourage brainstorming sessions.

  2. Curriculum:  Our Syllabus is designed in such a way that it is up to date with the market trend. Hence we not only teach the conventional topics but also the upgraded versions to align ourselves and our students with IT industry pattern.

  3. Practical sessions: We believe in a practical approach and hence after every session we give  assignments in a way that students will get to apply the theory  immediately.

  4. Soft skills:  Emphasis is given on verbal and written communication skills as well ,as we believe in all round expertise.

  5. Resume Preparation and Interview readiness: We have a dedicated team who works on building your resume effectively and make you ready for interviews through mock interview practices.

  6. Support Team: Our  support Team will be in touch with you even after your course is completed via emails for further assistance.

Book now for a free demo session to gauge yourself the quality of this Big data training course that is offered at most affordable price.

We strongly believe in providing  personal/Individual attention to each and every student in order to make them an efficient Big Data Engineer. Hence we keep the batch of minimum size.

  • Training program provided by experienced working professionals who are a expert in Big data field.
  • Instructor-led LIVE training sessions
  • Curriculum designed by taking current Hadoop and Spark technology and the job market into consideration.
  • Practical assignments at the end of every session
  • Emphasis on live project work with examples
  • Resume preparation guidance session by dedicated team.
  • master Hadoop Administration with 14 real-time industry-oriented case-study projects.
  • Interview guidance by conducting mock interview sessions.
  • Job placement assistance with job alerts until you get your first job
  • Free Hadoop and Spark study material accessible.
  • Video recordings available to revise training.
  • Support for passing the Cloudera CCA Spark and Hadoop Developer Certification (CCA175) exam with our premium question bank
  • Course completion certificate (on request)
  • Any college fresher/graduate can learn Big data training program.
  • Any experienced professional from any other field who wants to switch career into Big data Analytics
  • Any experienced professional, who wants to upgrade themselves to learn  advanced tactics and  to work efficiently and smartly in this field.

Become a Cloudera certified big data professional. The right certification can help you rise up in the ranks. These responsibilities are integral to the success of an organization, and achieving a respected certification helps you prove you've got the chops to handle the job.

Below mentioned are the two most popular Big Data certifications:

  1. Cloudera Certified Professional (CCP):  The material is verified by certified CCP experts and many students who actually utilized to ace an exam with high scores.

  2. Cloudera Certified Associate (CCA): The CCA is a higher level certification that requires experience before you can apply.

We value your money. Hence we have set  a highly affordable price when compared to other institutes. Definitely our hands-on Placement oriented training program from experienced professionals and Industry experts is better than any other crash courses offered by other institutes. “Not comprising on quality is our motto”. We will use all our resources and expertise  to make you an aspirant an  efficient Hadoop engineer.

Learn Hadoop Training in Chennai at Adyar. Rated as Best Hadoop Training Institute in Chennai. Call 89399-15577 for Bigdata Courses @ OMR, Navalur, Annanagar, Velachery, Perumbakkam, Tambaram, Adyar & Porur.

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Big Data Hadoop Course Content | Duration : 3 Months

Big Data Hadoop Master Program

This hands-on Hadoop training course makes you proficient in tools and systems used by Hadoop experts and help you act on data for real business gain. This Hadoop course content has been developed by extensive research on 5000+ job descriptions across the globe. The focus is not what a tool can do, but what you can do with the output from the tool.

Career Related Program:

Extensive Program with 9 Courses

200+ Hours of Interactive Learning

Capstone Project

  • All About Bigdata & Hadoop Drive
  • Linux, SQL, ETL, & Datawarehouse Refresh
  • Hadoop HDFS, Map Reduce, YARN Distributed Framework
  • NOSQL - For realtime data storage and search using HBASE & ELASTIC SEARCH
  • Visualization & Desktop - Jibana with Elastic search Integration using Spark
  • Robotic Process Automation (RPA) Using Linux & Spark
  • In Memory stream for Fast Data, Realtime Streaming & Data Formation using Spark, Kafka, Nifi.
  • Reusable Framework creation with logging Framework
  • Cluster formation creation in Cloud environments
  • SDLC, Packaging & Deployment in Bigdata Platform
  • Project execution with Hackathon & Test.
  • Job submission & Orchestration with Scheduling using Oozie
  • All About Bigdata & Hadoop Deep Drive
  • Linux, SQL, ETL, & Datawarehouse Refresh
  • Hadoop HDFS, Map Reduce, YARN Distributed Framework
  • SQOOP - Data ingestion Framework
  • Hive - SQL & OLAP Layer on Hadoop
  • HBASE & Elastic SEARCH - Real Time Random Read/Write NOSQL
  • PHOENIX - SQL Layer on Top of HBASE
  • KIBANA - Realtime Visualization on top Elastic Search
  • OOZIE - Workflow Scheduling & Monitoring tool
  • NIFI- Data Flow Tool for Mediation & Routing og large dataset
  • KAFKA - Distributed & Scalable Messaging queue
  • SPARK - Fast & Distributed In-Memory engine for largescale data
  • SCALA/PYTHON - Scalable, Function based Highlevel Language
  • HUE - GUI for Hadoop Eco System
  • AMBARI - Provisioning, Managing and Monitoring Hadoop Cluster
  • Google Cloud based - Hadoop & Spark CIuster setup
  • HORTONWORKS - Distribution for provisioning Hadoop Cluster
  • AWS Services - EMR, EC2, 53, IAM, SG, ATENA
  • MAVEN & GITHUB - DevOps Continuous Build & Version control
  • Frameworks for Data Masking, Data Validation & Sanitation

We have to first have know all about Big-Data & its Characteristics.

  • Evolution of Data
  • Introduction
  • Classification
  • Size Hierarchy
  • Why Hadoop is Trending
  • I0T, Devops, Cloud Computing, Enterprise Mobility
  • Challenges in Hadoop
  • Characteristics
  • Tools for Hadoop
  • Why Hadoop draws attention in IT Industry
  • What do we do with Hadoop
  • How Hadoop can be analyzed
  • Typical Distributed System
  • Draw backs in Traditional
  • Distrubited Systems
  • Bigdata tools

In this module you will be learning Introduction & Key Components of Linux Dev & Admin

  • History and Evolution
  • Architecture
  • Development Commands
  • Env Variables
  • File Management
  • Directories Management
  • Admin Commands
  • Advance Commands
  • Shell Scripting
  • Groups and User managements
  • Permissions
  • Important directory structure
  • Disk utilities
  • Compression Techniques
  • Misc Cornmands
  • Kernel, Shell
  • Terminal, SSH, GUI
  • Hands On Exercises

In this module you will be lipux shell scripting and automation techniques

  • Automation process using shell scripting
  • Integration of hadoop Eco systems with Linux scripting
  • Looping, conditional, vars methods
  • Key Differences between Linux & Windows
  • Kernel
    • What is the Purpose of Kernel?
    • How Kernel Works?
    • Find Kernel
  • Shell
    • What is the Purpose of Shell?
    • Types of Shell
    • Environment Variables in Shell
    • Hands On Exercises

In this module you will be leaning all about Hadoop

  • What is Hadoop?
  • Evolution of Hadoop
  • Features of Hadoop
  • Characteristic of Hadoop
  • Hadoop compared with Traditional Dist. Systems
  • When to use Hadoop
  • When not to use Hadoop Components of Hadoop IHDFS, MapReduce, YARN)
  • Hadoop Architecture
  • Daemons in Hadoop Version 1 & 2 How Data is stored in Hadoop Cluster, Datacenter, Spilt, Block. Rack Awareness, Replication, Heart beat)
  • Hadoop 1.0 Limitation
  • Name Node High Availability

Hadoop distributed file system concepts with architecture, commands, options, advance options, data management

  • Name node Federation
  • Hadoop version s
  • Anatomy of File Read/Write
  • Hadoop Ouster Formation in VM, Sandbox & GCP Cloud
  • Cluster formation & sizing guide
  • Hadoop Commands Hands-on
  • Hadoop admin hands-on
  • HDFS integration with Lima shell
  • HDFS additional Use cases
  • Data Integrity
  • Serialization
  • Compression techniques
  • Data ingestion to HDFS using different ecosystems

What is FSx, Types of FSx,FSx for Windows server, How does FSx for Windows File Server work, FSx for Lustre, Use cases of FSx, Automatic failover process, Supported clients and access methods, What is a Global Accelerator, How Global Accelerator works, Listeners and Endpoints, What are AWS Organizations, Features of AWS Organizations, Managing multiple accounts, What are ENIs, ENAs and EFAs, Working with network interfaces, Enhanced Networking with ENA, EFA with MPI, Monitoring an EFA

Hands-on Exercise: Creating a shared FSx file system between two windows instances, Accessing one instance with multiple Elastic IPS using ENI, Using Global Accelerator to map instances from 2 regions into one domain name, Enabling Enhanced Networking on an Ubuntu instance

Data ingestion or data acquisition tool for transporting bulk data between RDBMS -> Hadoop & Vice versa

  • Sqoop Introduction & History
  • Technical & Business benefits
  • Installation and configuration
  • Why Sqoop
  • In-depth Architecture
  • Import & Export Properties
  • Sqoop Export Architecture
  • Commands (Import HOSE, HIVE,
    HBase from MYSCIL, ORACLE)
  • Export Command Options
  • Incremental Import
  • Saved Jobs, Sqoop Merge
  • Import All tables, Excludes
  • Best practices & performance tuning
  • Sqoop import/export use cases
  • Advance Sqoop commands
  • Sqoop Realtime use cases
  • Sqoop Hive HBbase Integration

SQL Layer on top of Hadoop for analytical and declarative querie

  • Introduction to Hive
  • Architecture
  • Hive Vs RDBMS Vs NOSQL
  • Detailed Installation (Metastore, Integrating with Hue)
  • Starting Metastore and Hive Server2
  • Data types (Primitive, Collection Array, Struct, Map)
  • Create Tables (Managed, External, Temp)
  • DML operations (load, insert, export)
  • Exploring Indexes
  • HQL Automation using shell scripts
  • Managed Vs External tables
  • HOL Queries using end to end usecases
  • Hive analytical and Hierarchial queries

Hive Components such as partition, bucketing, views, indexes, joins, handlers, udfs etc

  • Hive access through Hive Client, Beeline and Hue
  • File Formats (RC, ORC, Sequence)
  • Partitioning (static and dynamic)
  • partition with external table
  • Drop, Repair Partitions
  • Hive Sqoop, HBase, Integration
  • Hive Storage Handler implementation
  • Bucketing, Partitioning Vs Bucketing
  • Views, different types of joins
  • Aggregation, normalization Queries
  • Add files to the distributed cache, jars to the class path
  • UDF using Python & Scala
  • Generic UDF, UDAF

usecases & POCs on serdes, file formats, schema evolution, SCD concepts etc,

  • Optimized joins (Mapside,join,SMB Bucketing join)
  • Compressions on tables (LZO, Snappy)
  • Serde (XML Serdq, JsonSerde, CSV, Avro, Regex)
  • Parallel execution
  • Sampling data
  • Speculative execution
  • Installation &Configuration
  • Two POCs using the large dataset on the above topics
  • Hive Slowly changing dimension implementation
  • Hive Schema evolution use case using Avro dataset
  • Hive Usecase with retail and banking dataset

Hadoop Processing framework for Distributed processing with multitasking capabilities

  • Introduction to MapReduce
  • Hadoop Ecosystems roadmap
  • Map Reduce Flow
  • Types of Input and Output Format
  • MapReduce in details
  • Different types of files supported (Text, Sequence, map and Awo)
  • MapReduce lob submission in YARN Cluster in details
  • Role of Mappers and reducers
  • Identity Mapper, Identity Reducer
  • Zero Reducer, Custom Partitioning
  • Combiner, Sequence file format
  • Tweaking mappers and reducers
  • Mapreduce package and deployment
  • Code component, walk through
  • Mine, Sequence file format

Hadoop Resource management component for containerization, scheduling with multi tenant feature

  • Introduction to YARN
  • YARN Architecture
  • YARN Components
  • YARN Longlived & Shortlived Daemons
  • YARN Schedulers
  • Job Submission under YARN
  • Multi tenancy support of YARN
  • YARN High Avalability
  • YARN Fault tolerance handling
  • MapReduce job submission using YARN
  • History Server
  • YARN Dynamic allocation
  • Containerisation of YARN


Think beyond SQL with the column oriented datastore for realtime random read write of differential data sets

  • Introduction to NoSQL
  • Types of NOSOL
  • Characteristics of NoSQL
  • CAP Theorem
  • Columnar Datastore
  • What is HBase
  • Brief History
  • Row vs Column oriented
  • Storage Hierarchy ->Characteristics
  • Table Design
  • HMaster & Regions

Think beyond SQL with the column oriented datastore for realtime random read write of differential data sets

  • Region Server & Zookeeper
  • Inside Region Server (Memstore, Blockcache,HFile, WAL)
  • HBase Architecture (Read Path, Write Path, Compactions, Splits )
  • Minor/Major Compactions
  • Region Splits
  • Installiation &Configuration
  • Role of Zookeeper
  • HBase Shell
  • Introduction to Filters
  • Row Key Design
  • Map reduce Integration
  • Performance Tuning
  • Hands on with Medical domain
  • Hive HBase Handler
  • SQoop HBase Integration

SQL Layer on top of HBASE for low latency, real time aggregation queries with joining capabilities

  • Overview of Phoenix
    • Introduction
    • Architecture
    • History
  • Phoenix Hbase Integration
    • HBase table, view creation
    • SQL & UDEs
    • SQL Line & PLSQL Line of Phoenix
  • Phoenix Load & Query engine
    • Understanding coprocessor Configurations
    • Hive -> Mask -> Phoenix integration
    • Creation of views in phoenix
    • Load bulk data using plsql
    • Serverlog Aggregation usecase

In this module, you will do the Hands on and Exploration of the Integration of components

  • Introduction
  • History - Why Oozie
  • Components
  • Architecture
  • Workflow Engine
  • Nodes
  • Workflow
  • Coordinator
  • Action (MapReduce, Hive, Spark, Shell & Sqoop) Introduction to Bundle
  • Email Notification
  • Error Handling
  • Installation
  • Workouts
  • Orchestration of end to end tools
  • Scheduling of data pipeline
  • Invoking shell script. Sqoop. Hive

Learn a scalable, Function based & Object oriented high level programming language

  • Scala Introduction
  • History Why Scala , Scala Installation
  • Function based programming features
  • Variable / Values
  • Conditional structure
  • Looping constructs
  • Execute Pattern Matching in Scala
  • Exception Handling
  • Method creation
  • 00Ps concepts (Classes, Objects. Collections, Inheritance, Abstraction and Encapsulation)
  • Functional Programming in Scala (Closures. Currying, Expressions, Anonymous Functions)
  • Know the concepts of classes in Scala Object Orientation in Scala (Pnmary, Auxiliary Constructors, Singleton Objects, Companion Objects)
  • Trans, Moms & Abstract classes

In this module, you will learn about the Git Workflow and case

  • Python Introduction
  • Evolution
  • Application
  • Features
  • Installation &Configuration
  • Objectives
  • Flow Control
  • Variables
  • Data types
  • Functions
  • Modules
  • OOPS
  • Python for Spark
  • Structures
  • Collection types
  • Looping Constructs
  • Dictionary & Tuples
  • File I/O

Learn the most advanced in- memory, fast, scalable market needed framework for large scale computation

  • Spark Introduction
  • History
  • Overmew
  • MR vs Spark
  • Spark Libraries
  • Why Spark
  • RDDs
  • Spark Internals
  • Pillars of Spark
  • Transformations & Actions
  • DAG , Lazy evaluation & execution
  • Fault Tolerance
  • Lineage
  • TermInologies
  • Ouster types
  • Hadoop Integration
  • Spark SQL
  • Data frames, DataSets
  • Optimizers- Catalyst,Tungsten, AST

Learn the Spark SQL & Streaming data Wrangling and Munging techniques for end to end processing framework

  • Session
  • Structured Streaming
  • SQL Contexts
  • Hive Context
  • RDDs to Relations
  • Spark Streaming
  • Windowing function
  • Why Spark Streaming
  • Insurance Hackathon
  • Data masking techniques
  • Introduction to Spark ML
  • Spark UI
  • lob Submission mto different cluster managers
  • Reusable framework creation
  • SDK implementation of Spark
  • Building of Fat & ean Jars
  • PYSPARK integration
  • Working with PYSPARK Functions
  • Developing applications with PYSPARK
  • Maven Git Eclipsce integration
  • Spark -> NOSQL integration
  • Spark options
  • Integration with multiple sources & targets
  • SCD implementation - Real time use LAWS
  • Ebay auction analysis
  • US customer data analysis
  • End to end real-time integration with NIFI -> Kafka ->; Spark Streaming Amazon 53 -> EC2 -> RDBMS Different Filesystems Hive -: Oozie & Hbase

Publish — Subscriber Distributed Message Queue Cluster creation & integration

  • Kafka Introduction
  • Applications, Cluster Setup
  • Broker fault tolerance
  • Architecture
  • Components
  • Partitions & Replication
  • Distribution of messages
  • Producer & Consumer workload Distribution
  • Topics management
  • Brokers
  • Installation
  • Workouts
  • Console publishing
  • Console Consuming
  • Topic options
  • Offset Management Cluster deployment in cloud

NIFI is a Data flow tool for real time data ingestion into Bigdata platform with tight integration with Kafka & Spark

  • NIFI Introduction
  • Core Components
  • Architecture
  • NIFI Installation &Configuration
  • Fault tolerance
  • Data Provenance Routing,
  • Mediation,transformation & routing
  • Nifi -> Kafka -> Spark integration
  • Workouts
  • Scheduling
  • Real time streaming
  • Kafka producer & consumer
  • File streaming with HDFS integration
  • Data provenenance
  • Packaging NIFI templates
  • Rest Api integration
  • Twitter data capture

UI tools for working and managing Hadoop and Spark eco systems in a self driven way for development and administration

  • Introduction
  • Setting up of Ambari and HDP
  • Cluster formation guide and Implementation
  • Deployment in Cloud
  • Full Visibility into Cluster Health
  • Metrics & Dashboards
  • Heat Maps
  • Configurations
  • Services, Alerts, Admm activities
  • Provisioning, Managing and Monitoring Hadoop Clusters
  • Hue Introduction
  • Access Hive
  • Query executor
  • Data browser
  • Access Hive. HCatalog, Oozie, File Browser

The top level distributions for managing Hadoop and spark ecosystems

  • Installing and configuring HDP using Ambari
  • Configuring Cloudera manager & HDP in sandbox
  • Cluster Design
  • Different nodes (Gateway, Ingestion, Edge)
  • System consideration
  • Commands(fsck,job,dfs admin, distcp,balancer)
  • Schedulers in RM (Capacity, Fair, FIF0)

Full Document search store for NOSQL solution with rich real time visualization & analytics capabilities

  • History
  • Components
  • Why ES
  • Cluster Architecture/Framework
  • All about REST APIs
  • Index Request
  • Search Request
  • Indexing a Document
  • limitations
  • Install/Config
  • Create / Delete / Update
  • Get /Search
  • Realtime data ingestion with hive
  • NIFI integration
  • Spark streaming integration
  • Hands-on Exercises using REST APIs
  • Batch & Realtime Usecases

A Raltime integrated Dashboard with rich Visualization &Dashboards with creation of lines, trends, pies, bars, graphs, word cloud

  • History
  • Components
  • Why Kibana
  • Trend analysis
  • Install/Config
  • Creation of different types of visualizations
  • Visualization integration into dashboard
  • Setting of indexes, refresh and lookup
  • Discovery of index data with search
  • Sense plugin integration
  • Deep Visualizations
  • Deep Dashboards
  • Create custom Dashboards
  • End to end flow integration with Nift,
  • Kafka, Spark, ES & Kibana

Repository & Version controller for code management and package generation for dependency Management & collaboration of different components used in TLC

  • DevOps Basics
  • Versioning
  • Create and use a repository
  • Start and manage a new branch
  • Make changes to a file and push them to GitHub as commits
  • Open and merge a pull request
  • Create Story boards
  • Desktop integration
  • Maven integration with Git
  • Create project in Maven
  • Add scala nature
  • Maven operations
  • Adding and updating POM
  • Managing dependencies with the maven
  • Building and installing maven repository
  • Maven fat & lean jar build with submit

Amazon Web Service components of EC2, 53 storage, access control, Subnets, Athena, Elastic Mapreduce components with Hadoop framework integration

  • Introduction to AWS & Why Cloud Managing keys for password less connection
  • All about EC2 instance creation till the management
  • Amazon Virtual Private Cloud creation Managing the roles with identity Access management
  • Amazon object simple storage service (S3) creation with static file uploads and exposure.
  • Athena - SQL on top of S3 creation and managing
  • Managing AWS EMR cluster with the formation.
  • Spark & Hive Integration for data pipeline with S3, Redshift/Dynamo DB, EC2 instance
  • Kafka integration

identify the Platform as a service with the creation and management of Hadoop and Spark cluster in the Google cloud platform

  • Registering and managing cloud account
  • Key generation
  • Cloud compute engine configuration and creation
  • Enabling Ambari
  • Multi Node cluster setup
  • Hardware consideration Software Consideration
  • Commands (fsck, job, dfsadmin)
  • Schedulers in Resource Manager
  • Rack Awareness Policy
  • Balancing
  • NameNode Failure and Recovery
  • Commissioning and Decommissioning a Nodes
  • Managing other GCP services
  • Cluster health management

Lets do a smart effort of learning how to prepare resume, interview, projects, answering cluster size, daily activities, roles, challenges faced, data size, growth rate, type of data worked etc.,

  • Resume Building & flavoring
  • Daily Roles & Responsibilitres
  • Cluster formation guidelines
  • Interview Questions
  • Project description & Flow Execution of end to end 5134.0 practices
  • Framework integration with log monitor
  • Data size & growth rate
  • Architectures of Lambda, Kappa, Master slave. Peer to peer with types of data handled
  • Datalake building guide
  • Projects discussion
  • Package & Development
  • Setting up of Single node pseudo Distributed mode Cluster, Hortonworks Sandbox & Cloud based multinode Hortonworks cluster setup and Admin.
  • Customer - Transaction data movement using Sqoop.
  • Customer - Transaction Data analytics using Hive.
  • Profession segmentation, Weblog analysis & Student career analysis using Hive
  • Unstructured course data and Students processing using MapReduce.
  • Medical and Patient data handling using HBase, Web Statistics low latency data processing using Phoenix.
  • Web Server and HDFS data integration with Kafka using NIFI.
  • Ebay Auction data analytics and SF Police Department data processing using Spark Core.
  • Retail Banking data processing using Spark core.
  • Server Log Analysis using spark core,Sensus data analysis using Spark SQL.
  • Realtime Network, HDFS and Kafka data processing using Spark Streaming.
  • Create rich Visualization 8. Dashboard using Kibana with Ebay & Trans data
  • Managing twitter open data, RESTAP1 data using NIFI-> KAFKA->SPARK
  • Project 1: Sentimental Analytics - Web event analytics using Linux, HDFS, Hive, Hbase & Oozie.
  • Project 2: Server log analysis for view ship pattern, threat management and error handling - Sqoop, Hive, HCatalog, HBase, Phoenix.
  • Project 3: Datalake for Usage Pattern Analytics & Frustration scoring of customer - Data Warehouse Migration/consolidation using Sqoop, HDFS, Masking UDF Hive, Oozie, HBase,
  • Phoenix.
  • Project 4: Realtime Streaming analyrics using Vehicle fleet data using I0T, RPA, Kafka, Spark, NIFI, Kafka, Hive, HBASE/ES, Phoenix.
  • Project 5: DataLake exploration using Spark SQL, Hive, HBASE/ES;
  • Project 6: Fast Data Processing for Customer segmentation using Kafka, Spark, NIFI, AWS S3, Hive, HBASE/ES.
  • 2 Hackathons
  • 1 Exams
  • 1 Production packaging and deployment
  • 1 Cloud formation
  • 1 Live Project execution
  • 1 Job Support video
  • 1 Chat & text mining

About Our Hadoop Instructor

Sai has been working with data for more than 15 years.

Sai specializes in Hadoop projects. He has worked with business intelligence, analytics, Machine learning, Predictive modeling and data warehousing. He has also done production work with Apache Spark on the Databricks cloud and Google Cloud Dataproc and Cloud Datastore.

In the last 10 years, Sai has trained and placed 5000+ students and supported many of his students to switch from non-technical to technical Job

Sai currently focuses on teaching and delivering Individual Placement and Support for all his students. During his training journey, He has taken 300+ batches through different modes (Online, classroom, corporate).

Sai Worked with major IT companies such as British Telecom, Microsoft, Bank of America, as well as several smaller private companies in delivering high-quality training.

Sai has a passion for teaching and has spent years speaking at conferences and delivering Hadoop and cloud technologies online learning content.

Flexible Timings / Weekend classes Available.

Talk to the Trainer @ +91-8939975577

Students Placed

I was a slow learner and was frustrated in life if I could ever get any Job. Then I chose Greens technologies for learning Hadoop as my friend conveyed that they are amazing and can change lives. After joining them I started picking up on each and every topic . I climbed the ladder of success and cleared my training program and Hadoop Certification . And not only that. Today I have been placed as a Big Data analyst in one of the most reputed organizations which I had once dreamt of. Hats off to the trainer and the whole team for being patient enough in solving my queries and guiding me throughout..Always grateful.

Mohammed Ali

Finest Institute for Hadoop training in Chennai. The whole training team gave a detailed explanation of the course. They provided us with training materials and videos which are very helpful. I couldn’t have imagined to clear Hadoop certification without their support. Thank you Greens Technologies”. Special Thanks To the trainer-Mr. Sai Ravi and Greens Technologies Team for helping me not only to complete my certification but also to get job in one of the most reputed MNCs


When I was in a dilemma to choose which course would give me a bright future, Greens Technologies’ counseling team came into rescue. They guided me to take Hadoop training program and helped me to understand how it has become a trending course in the market .I am happy that I listened to them at a crucial juncture of my life and now I am a successful Hadoop Analyst in an MNC. Not to forget I am a certified Hadoop professional earning a fat amount and leading a happy life..Thanks to Dinesh Sir and Sai Ravi Sir..Ever Indebted


First of all thanks to Greens Technologies for providing a seat for the batch in such a short notice. I have completed the Apache program and got the certificate promptly. The trainer was really helpful in clearing all my doubts and also helped me with few other queries. Thanks for all the support .I really had a wonderful learning experience. Will refer Greens Technologies to all my friends as well,as the promise of Job assurance has been kept by them”. Yes, Happy to share that I am a part of Big Data Analyst team of a leading MNC.

Pavan Reddy

Hadoop training from Greens Technologies helped me get my first job in Accenture. The process of attending mock interviews along with technical training helped us to boost our confidence levels. Also the staff here is co-operative & they help immediately .As a result of which I was able to clear my certification program too. Thanks to Greens Technologies from the bottom of my heart


The placement officer and the team of Greens Technologies is wonderful. They regularly send me job opening notifications and schedule interviews and Hence I got placed in Infosys.. Thanks to my trainer for giving full support. I am happy doing course with Greens Technologies”. The best thing about them is they not only focus on training program but also emphasize on successful completion of certification.


I had enquired many institutes for Hadoop Training and Certification Program .Cost was bit high, but Greens Technologies offered it for better package. And regarding the course agenda, they are very punctual and sincere. Thanks to the team for helping to complete the certification and also they got me a placement in a reputed organization

What are the pre-requisites for learning Hadoop training?

 As such, there is no prerequisite for undertaking this training. 

However, it is highly desirable if you possess the following skills sets: 

  • Mathematical and Analytical expertise
  • Good critical thinking and problem-solving skills
  • Technical knowledge of Python, R and SAS tools
  • Communication skills

How much time it will take to learn Hadoop course?

It is 2 to 3months of Study, if you take regular classes it will take 45 days or if you go with Weekend classes it will take 4 to 5 weekends.

What is the course fee for Hadoop course?

The course fee for Hadoop course at Green’s Technologies is minimal and highly affordable. We also provide the liberty to pay it in two installments. For the course fee structure, you can contact us at(+91 8939975577). We offer free demo classes and once you are comfortable, you can pay the fees.

What is the admission procedure in Greens Technologies?

To start with, fill the enquiry form in our website or call the counselors at +91 8939975577.

What will be the size of a Hadoop batch at Greens Technologies?

At Greens Technologies, we limit the batch sizes to not more than 5 to 6 students for any course. Providing quality training to each and every individual is our motto.

How would it get adjusted if I miss a session?

Regular attendance is highly recommended by us in order to maintain the regularity .However, due to emergency circumstances, if you miss a session then we would arrange for a substitute class.

What are the different modes of Hadoop training that Greens Technologies provides?

We provide both classroom and online training. Also we provide fast track mode programs.

Will the sessions be only theory oriented?

Not at all. We, at Greens technologies are more focused on providing sufficient practical training and not only theory. We ensure that a student should be able to handle any type of real time scenarios.

Will I be at par with industry standards after course completion?

Ofcourse yes, you will become a Hadoop expert as per the current industry standards. You will be confident in attending the interviews since we provide career-oriented training that covers mock interviews, technical reviews etc.

Is Placement assistance provided at Greens Technologies?

The answer is Definitely yes. We have a dedicated team that ensures that conducts mock interviews, regular technical reviews and assessments .Also soft skills session is provided to boost the confidence levels of each and every students.

How many students have been trained by Greens Technologies up till now?

We have been sustaining in the market from past 10years and have trained several students and placed them in top notch MNCs.We have multiple branches in Chennai ,which provide training to thousands of students.

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Try two FREE CLASS to see for yourself the quality of training.
Total Duration: 200 hours

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Available 24x7 for your queries
Course Features
Course Duration 200 hours
Learning Mode Online / Class room
Assignments 60 Hours
Project work 40 Hrs Exercises
Self-paced Videos 30 Hrs
Support 24/7
Certification Cloudera
Skills Covered
  • Hadoop Certification Training
  • Hadoop Project based Training
  • Apache Spark Certification Training
  • Hadoop Administration
  • NoSQL Databases for Big Data
  • CCA175 - Cloudera Spark and Hadoop Developer Certification
  • Spark, Scala and Storm combo
  • Apache Kafka
  • Apache Storm Introduction
  • Apache Hadoop and MapReduce Essentials
  • Apache Spark Advanced Topics
  • Realtime data processing
  • Parallel processing
  • Functional programming
  • Spark RDD optimization techniques
  • Interview Preparation - Questions and Answers
  • Placements