IT Training institute in Bangalore

Hadoop Training in Bangalore

hadoop training in bangalore
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Hadoop is one of the most popular and demanding and open source data analytics technology that can be effectively applied to manage Big Data problems and achieve data solutions. It allows distributed data processing across clusters of computers using simple programming models. MapReduce is the heart of Hadoop that possesses massive scalability.

I’m currently working as a Junior Java developer with 1.5 years of experience.global training bangalore approved reviews As I was already good at Java, my Team Lead suggested me to learn Bigdata-Hadoop; because we were going to get Hadoop projects. My trainer was a Big Data Analytics expert. His training and support throughout the entire course really helped me very well to shift my career path. Now I have completed this course successfully in Global Training Bangalore (TIB Academy) and took an internal switch in my company itself. I’m feeling glad and satisfied about my learning experience.

Tell me more about Hadoop Training in Bangalore! IT Training courses

  • Hadoop involves in data collection, data processing and data analytics using HDFS (Hadoop Distributed File System) and MapReduce algorithms.
  • Collection of massive unstructured data from multiple sources and conversion of raw data into user preferable format are the two primary operations performed by Hadoop.
  • TIB Academy is the best Bigdata Hadoop training institution in Bangalore where you will be exposed to differentiated learning environment as the course syllabus has been prepared by the highly experienced professionals. With this course, you can learn about Hadoop installation, MapReduce algorithms, MongoDB, HDFS, Flume, Zookeeper, SQOOP and lot more. Please check below for the detailed syllabus.

Are there any prerequisites for Hadoop? IT Training Prerequisites in bangalore

  • Basic knowledge on Java and Linux.
  • If you are already familiar with the above, this course will be quite easy for you to grasp the concepts quickly. Otherwise, our experts are here to help you with Java training, Linux training and Hadoop training from the basics.

Can you help me about Hadoop Job Opportunities? global training bangalore placement

  • Bigdata-Hadoop is suitable for experienced people, who have key skills on data analytics, Java programming and RDBMS concepts. In the current IT market, there are plenty of Hadoop opportunities for the experienced professionals who are aware of the above technologies.
  • If you possess strong Hadoop experience with Oracle and SQL, you can get job as Hadoop developer.
  • If you possess Hadoop as co-skill with Python, Web services and AWS – solution architect, you can get job as AWS Hadoop developer.
  • If you possess Hadoop as a co-skill with Splunk, Unix, you can get job as Big Data Engineer.
  • If you possess excellent administration skills in Hadoop, you can get job as Hadoop Admin.
  • Some of the companies that hire for Hadoop are JP Morgan, Altisource, Accenture, Akamai, Ocwen, Mphasis, Capgemini, Oracle, IBM, TCS.

Compared to other training institutes, Global Training Bangalore is one of the best Hadoop training institutes in Bangalore where you can acquire the best Hadoop training and placement guidance.

What is special about the Hadoop Training in Bangalore? tib-academy-bangalore

  • Global Training Bangalore is the only institute providing the best Hadoop training in Bangalore. They have top experienced industrial professionals as their trainers; they are working in top rated MNCs and Corporates with years of real time experience. So they will surely boost you to become the best Hadoop developer.
  • As their trainers are all currently working, the Hadoop training program will be usually scheduled during weekdays early mornings between 7AM to 10AM, weekdays late evenings between 7PM to 9:30PM and flexible timings in weekends. They provide Hadoop classroom training, Hadoop online training and Hadoop weekend training based upon the student’s time convenience. This training will make you to feel like obtaining the best Hadoop course and placement support in Bangalore with moderate Hadoop course fees.
  • The practical sessions throughout the course will help you to enhance your technical skills and confidence. Their connections to the job world will surely help you achieve your dream job. So start putting your sincere efforts into practice and grab the wonderful opportunities.

What are the Hadoop Training in Bangalore Class timings and course duration?  IT training in bangalore timing

Day Hadoop Classroom Training Timing Hadoop Online Training Timing
Mon – Fri

7AM to 10AM

7PM to 9.30PM

7AM to 10AM

7PM to 9.30PM

Sat,

Sun

Flexible Timing

Flexible Timing

Please contact us soon to book your preferable time slot.

Call Global Training Bangalore (TIB Academy)

+91 9513332301 / 02 / 03

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Posted by TIB Academy on Saturday, September 2, 2017

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Best Hadoop Training institute in Marathahalli

Hadoop training is provided in Global Training Bangalore (TIB Academy) with affordable training fee. They provide both Hadoop online Training and Hadoop classroom training. They give you hands on training in Hadoop. Trainers are experienced in teaching. They will help you in clearing examination on Hadoop certification. They will assist you in placements.

Hadoop Training in Bangalore Course Duration

Regular Classes (Morning, Day time & Evening)

Duration: 40 hrs.

Weekend Training Classes (Saturday, Sunday & Holidays)

Duration: 6 weeks

Fast Track Training Program (5+ hours daily)

Duration: within 20 days.

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Hadoop Training in Bangalore Syllabus

Session 1 : Introduction to Big Data

  • Importance of Data
  • ESG Report on Analytics
  • Big Data & It’s Hype
  • What is Big Data?
  • Structured vs Unstructured data
  • Definition of Big Data
  • Big Data Users & Scenarios
  • Challenges of Big Data
  • Why Distributed Processing?

Session 2: Hadoop

  • History Of Hadoop
  • Hadoop Ecosystem
  • Hadoop Animal Planet
  • When to use & when not to use Hadoop
  • What is Hadoop?
  • Key Distinctions of Hadoop
  • Hadoop Components/Architecture
  • Understanding Storage Components
  • Understanding Processing Components
  • Anatomy Of a File Write
    Anatomy of a File Read

Session 3 : Understanding Hadoop Cluster

  • Handout discussion
  • Walkthrough of CDH setup
  • Hadoop Cluster Modes
  • Hadoop Configuration files
  • Understanding Hadoop Cluster configuration
  • Data Ingestion to HDFS

Session 4 – MapReduce

  • Meet MapReduce
  • Word Count Algorithm – Traditional approach
  • Traditional approach on a Distributed system
  • Traditional approach – Drawbacks
  • MapReduce approach
  • Input & Output Forms of a MR program
  • Map, Shuffle & Sort, Reduce Phases
  • Workflow & Transformation of Data
  • Word Count Code walkthrough

Session 5 – MapReduce

  • Input Split & HDFS Block
  • Relation between Split & Block
  • MR Flow with Single Reduce Task
  • MR flow with multiple Reducers
  • Data locality Optimization
  • Speculative Execution

Session 6 – Advanced MapReduce

  • Combiner
  • Partitioner
  • Counters
  • Hadoop Data Types
  • Custom Data Types
  • Input Format & Hierarchy
  • Output Format & Hierarchy
  • Side Data distribution – Distributed cache

Session 7 – Advanced MapReduce

  • Joins
  • Map side Join using Distributed cache
  • Reduce side Join
  • MR Unit – An Unit testing framework

Session 8 – Mockup Interview Session
Session 9 – Pig

  • What is Pig?
  • Why Pig?
  • Pig vs Sql
  • Execution Types or Modes
  • Running Pig
  • Pig Data types
  • Pig Latin relational Operators
  • Multi Query execution
  • Pig Latin Diagnostic Operators

Session 10 – Pig

  • Pig Latin Macro & UDF statements
  • Pig Latin Commands
  • Pig Latin Expressions
  • Schemas
  • Pig Functions
  • Pig Latin File Loaders
  • Pig UDF & executing a Pig UDF

Session 11 – Hive

  • Introduction to Hive
  • Pig Vs Hive
  • Hive Limitations & Possibilities
  • Hive Architecture
  • Metastore
  • Hive Data Organization
  • Hive QL
  • Sql vs Hive QL
  • Hive Data types
  • Data Storage
  • Managed & External Tables

Session 12 – Hive

  • Partitions & Buckets
  • Storage Formats
  • Built-in Serdes
  • Importing Data
  • Alter & Drop Commands
  • Data Querying

Session 13 – Hive

  • Using MR Scripts
  • Hive Joins
  • Sub Queries
  • Views
  • UDFs

Session 13 – Resume Preparation

Session 14 – HBase & Introduction to MongoDB

  • Introduction to NoSql & HBase
  • Row & Column oriented storage
  • Characteristics of a huge DB
  • What is HBase?
  • HBase Data-Model
  • HBase vs RDBMS
  • HBase architecture
  • HBase in operation
  • Loading Data into HBase
  • HBase shell commands
  • HBase operations through Java
  • HBase operations through MR
  • Introduction to MongoDB
  • Basic Commands used in it

Session 15 – ZooKeeper & Oozie

  • Introduction to Zookeeper
  • Distributed Coordination
  • Zookeeper Data Model
  • Zookeeper Service
  • Zookeeper in HBase
  • Introduction to Oozie
  • Oozie workflow

Session 16 – Sqoop & Flume

  • Introduction to Sqoop
  • Sqoop design
  • Sqoop Commands
  • Sqoop Import & Export Commands
  • Sqoop Incremental load Commands
  • Introduction to Flume
  • Architecture & its Components
  • Flume Configuration & Interceptors

Session 17 – Hadoop 2.0 & YARN

  • Hadoop 1 Limitations
  • HDFS Federation
  • NameNode High Availability
  • Introduction to YARN
  • YARN Applications
  • YARN Architecture
  • Anatomy of an YARN application

Session 18 – Hands On Using Ubuntu

  • Installing Hadoop 2.2 on the Ubuntu
  • Installing Eclipse and Maven
  • Setting up the configuration files
  • Installation of Pig,Hive,Sqoop,Flume,oozie and zookeper
  • Installation of NoSql database – HBase
  • Hadoop Commands

Session:19 Introduction to Spark

  • What is Big Data?
  • What is Spark?
  • Why Spark?
  • Spark Ecosystem
  • A note about Scala
  • Why Scala?
  • MapReduce vs Spark
  • Hello Spark!

Session 20 – Project Discussion

  • Java to MapReduce Conversion
  • MapReduce Project

Session 21 – Project Discussion

  • Hive Project
  • Pig Project

Session 22 – Mockup Interview Session
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