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Mission

Our mission is to provide excellent knowledge transfer with experienced professional and place our student in right career path.

Placements

100 % placements assurance, (need to fulfil our requirements) with the help of our corporate network and placements consultants.

Placements Assistance - We will arrange interviews, fine-tune resume so that your placement goal is achieved.

We have our own placements division – VISUAL PROSPECT

Trainer's Profile

• SAP CERTIFIED Trainers

• More than 5 years' of experience in related field in MNC

• willingness to share live scenarios and industrial behaviour

• Passion towards knowledge transfer

Global Online Training

• Breaking the global boarder, our training reaches your home computer

• Live session with trainers

• Desktop sharing with team viewer is adopted to have an effective training session

NEWS

Big Data Training in Chennai

Whom Hadoop is suitable for?

Hadoop is suitable for all IT professionals who look forward to become Data Scientist / Data Analyst in future and become industry experts on the same. This course can be pursued by Java as well as non- Java background professionals (including Mainframe, DWH etc.)

Whom do we train?

We train professionals across all experience 0 -15 years and we have separate modules like Developer module, Project manager module etc.. We customize the syllabus covered according to the role requirements in the industry.

Job Opportunity for Hadoop training in chennai

Hadoop is the buzzword in the market right now and there is tremendous amount of job opportunity waiting to be grabbed. In the current state market is short of good Big data professionals. Hence BIG Data means BIG Opportunities with Big bucks. Come grab them with both hands!!!

Certifications and Job opportunity Support

We help the trainees with guidance for Cloudera Developer Certification and also provide guidance to get placed in Hadoop jobs in the industry.

Big Data Hadoop provides wonderful opportunities for the aspiring IT professional both fresher and experienced. This course is suitable for both Java and non- Java professionals like Data-warehousing professionals, Mainframe professionals etc.

All topics will be covered with in-depth concepts and corresponding practical programs.

Hadoop Training Syllabus

INTRODUCTION

  • Big Data
  • 3Vs
  • Role of Hadoop in Big data
  • Hadoop and its ecosystem
  • Overview of other Big Data Systems
  • Requirements in Hadoop
  • UseCases of Hadoop

HDFS

  • Design
  • Architecture
  • Data Flow
  • CLI Commands
  • Java API
  • Data Flow Archives
  • Data Integrity
  • WebHDFS
  • Compression

MAPREDUCE

  • Theory
  • Data Flow (Map – Shuffle – Reduce)
  • Programming [Mapper, Reducer, Combiner, Partitioner]
  • Writables
  • InputFormat
  • Outputformat
  • Streaming API

ADVANCED MAPREDUCE PROGRAMMING

  • Counters
  • CustomInputFormat
  • Distributed Cache
  • Side Data Distribution
  • Joins
  • Sorting
  • ToolRunner
  • Debugging
  • Performance Fine tuning

ADMINISTRATION – Information required at Developer level

  • Hardware Considerations – Tips and Tricks
  • Schedulers
  • Balancers
  • NameNode Failure and Recovery

HBase

  • NoSQL vs SQL
  • CAP Theorem
  • Architecture
  • Configuration
  • Role of Zookeeper
  • Java Based APIs
  • MapReduce Integration
  • Performance Tuning

HIVE

  • Architecture
  • Tables
  • DDL – DML – UDF – UDAF
  • Partitioning
  • Bucketing
  • Hive-Hbase Integration
  • Hive Web Interface
  • Hive Server

OTHER HADOOP ECOSYSTEMS

  • Pig (Pig Latin , Programming)
  • Sqoop (Need – Architecture ,Examples)
  • Introduction to Components (Flume, Oozie,ambari)

Introduction to Big Data

Defining Big Data

  • The four dimensions of Big Data: volume, velocity, variety, veracity
  • Introducing the Storage, MapReduce and Query Stack

Delivering business benefit from Big Data

  • Establishing the business importance of Big Data
  • Addressing the challenge of extracting useful data
  • Integrating Big Data with traditional data

Storing Big Data

Analysing your data characteristics

  • Selecting data sources for analysis
  • Eliminating redundant data
  • Establishing the role of NoSQL

Overview of Big Data stores

  • Data models: key value, graph, document, column-family
  • Hadoop Distributed File System
  • HBase
  • Hive
  • Cassandra
  • Hypertable
  • Amazon S3
  • BigTable
  • DynamoDB
  • MongoDB
  • Redis
  • Riak
  • Neo4J

Selecting Big Data stores

  • Choosing the correct data stores based on your data characteristics
  • Moving code to data
  • Implementing polyglot data store solutions
  • Aligning business goals to the appropriate data store

Processing Big Data

Integrating disparate data stores

  • Mapping data to the programming framework
  • Connecting and extracting data from storage
  • Transforming data for processing
  • Subdividing data in preparation for Hadoop MapReduce

Employing Hadoop MapReduce

  • Creating the components of Hadoop MapReduce jobs
  • Distributing data processing across server farms
  • Executing Hadoop MapReduce jobs
  • Monitoring the progress of job flows

The building blocks of Hadoop MapReduce

  • Distinguishing Hadoop daemons
  • Investigating the Hadoop Distributed File System
  • Selecting appropriate execution modes: local, pseudo-distributed and fully distributed

Handling streaming data

  • Comparing real-time processing models
  • Leveraging Storm to extract live events
  • Lightning-fast processing with Spark and Shark

Tools and Techniques to Analyse Big Data

Abstracting Hadoop MapReduce jobs with Pig

  • Communicating with Hadoop in Pig Latin
  • Executing commands using the Grunt Shell
  • Streamlining high-level processing

Performing ad hoc Big Data querying with Hive

  • Persisting data in the Hive MegaStore
  • Performing queries with HiveQL
  • Investigating Hive file formats

Creating business value from extracted data

  • Mining data with Mahout
  • Visualising processed results with reporting tools
  • Querying in real time with Impala

Developing a Big Data Strategy

Defining a Big Data strategy for your organisation

  • Establishing your Big Data needs
  • Meeting business goals with timely data
  • Evaluating commercial Big Data tools
  • Managing organisational expectations

Enabling analytic innovation

  • Focusing on business importance
  • Framing the problem
  • Selecting the correct tools
  • Achieving timely results

Implementing a Big Data Solution

  • Selecting suitable vendors and hosting options
  • Balancing costs against business value
  • Keeping ahead of the curve

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  • Training + Job Program

    Academy’s T + J program prepares college students for a successful entry into the professional IT world by making them job-ready; we teach and make students to work in the live software projects in order to meet IT industrial requirements. We change student’s identity from fresher to IT professional.
    We assure job to our students, who undergo T + J program.

    • Technical Skill and
    • Communication skill.

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