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Hadoop Training Institutes In Bangalore Marathahalli & BTM Layout

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Virtual box/VM Ware :

  • Basics & Installations

Linux :

  • Basics

Hadoop:

  • What is Hadoop?
  • Why Hadoop and flow of Hadoop
  • Scaling
  • Distributed Framework
  • Hadoop v/s RDBMS
  • Brief history of Hadoop

Hadoop installation in pseudo mode:

Hadoop installation in cluster mode

  • Adding and removing nodes (without down time)
  • Decommissioning nodes
  • Block size
  • Hadoop Processes ( NN, SNN, JT, DN, TT)
  • Common errors when running Hadoop cluster, solutions

HDFS- Hadoop distributed File System :

    • HDFS Design and Architecture
    • HDFS Concepts
    • Interacting HDFS using command line
    • Dataflow
    • Introduction about Blocks
    • Data Replication
    • Admin Commands
    • Hadoop archives

Hadoop Processes :

      • Name node and its functionality
      • Secondary name node and its functionality
      • Job tracker and its functionality
      • Task tracker and its functionality
      • Data node and its functionality
      • Resource manager and its functionality Hadoop
      • Node manager and its functionality

Map Reduce :

      • Developing Map Reduce Application
      • Phases in Map Reduce Framework
      • Map Reduce Input and Output Formats
      • Advanced Concepts
      • Combiner
      • HAR
      • Partitioner, sorting, shuffling
      • Different phases of MapReduce programs
      • Data localization
      • Different unstructured data processing examples
      • Image processing by using MapReduce

Joining datasets in MapReduce jobs :

      • Map-side join
      • Reduce-Side join

Hadoop Programming Languages :

PIG

      • Introduction (Basics)
      • Installation and Configuration
      • Different datatypes
      • Interacting HDFS using PIG
      • Map Reduce Programs through PIG 6. PIG Commands
      • Execution mechanisms (grunt, script…)
      • Loading, Filtering, Grouping, joins….
      • Sample programs in PIG with Real time

Hive :

      • Basics (Introduction)
      • Installation and Configurations
      • Datatypes and operators
      • HQL Commands
      • Interacting HDFS using Hive
      • MapReduce programs through Hive
      • Joins, groups, filter……
      • Sample Programs in hive with real-time
      • Join vs Map Join

Impala :

      • Basics
      • Commands

Sqoop :

      • Introduction to sqoop
      • Installations & Configurations
      • Sqoop commands
      • Connect to relational database using sqoop and downloading lakhs of records to Hadoop (in single minute)

Flume :

      • Basics (Introduction)
      • Installation and Configurations

NOSQL Databases Concepts :

Hbase

      • Basics & Installations
      • commands
      • Interacting Hbase with HDF

MongoDb :

      • Basics & Installations I
      • All queries for processing data

OOZIE Introduction

Zookeeper introduction

Apache Spark :

      • Introduction
      • Installations and configurations
      • RDD , SC….
      • Scala Introduction
      • Interacting spark with HDFS
      • Programs in Spark through Scala

Specialties :

ETL tool (Data Warehousing BI Tools) :

PDI

      • Introduction
      • Creating RDBMS database
      • Establishing Connection between PDI to RDMS database
      • Creating data in Hadoop
      • Establishing Connection between PDI to Hadoop data
      • Moving data from Hadoop to RDBMS and vice versa
      • Summarization

Highlights

      • Working with Apache & cloudera Hadoop
      • Practical’s on Hadoop cluster
      • Real life use cases
      • Will cover old version of Hadoop and latest version of Hadoop

Student Splash(advantages):

    • 3 classes for free.
    • Your complete resume will be prepared by us
    • Faq’s will be given which makes you to crack any interview
    • World class infrastructure class rooms.
    • Huge digital display for all the class rooms.
    • Latest study material and books(soft copy)
    • 100% placement assistance.
    • All 365 days our institute will be opened for any time clarifications
Student Reviews
DataBytes – Best Big Data & Hadoop Training Institute in Bangalore with Placement Assistance
Expert Hadoop Training Institutes In Bangalore Marathahalli
Best institute to do Hadoop course, I’m very happy with DataBytes. Trainer explains everything very well. Thanks DataBytes
Written by: Nabanita Karmakar
DataBytes BTM Bangalore Reviews
5 / 5 stars

Contact for Hadoop Training Institutes In Bangalore Marathahalli & BTM

Spark

  • Spark Overview LECTURE
  • Spark Opportunity and Solution
  • Capabilities and Ecosystem
  • Spark Components vs Hadoop HANDS-ON
  • Databricks Lab Environment
  • Working with Notebooks
  • Spark Clusters and Files MODULE 2 RDD Fundamentals LECTURE
  • Purpose and Structure of RDDs
  • Transformations, Actions, and DAG
  • RDD programming API HANDS-ON
  • Creating RDDs from Data Files
  • Reshaping Data to Add Structure
  • Interactive Queries Using RDDs MODULE 3 Spark SQL / Dataframes LECTURE
  • Spark SQL and DataFrame Uses
  • DataFrame / SQL APIs
  • Catalyst Query Optimization HANDS-ON
  • Creating DataFrames
  • Query with DataFrame API and SQL
  • Caching and Re-using DataFrames
  • Generating Graphics and Reports MODULE 4 Spark Job Execution LECTURE
  • Jobs, Stages, and Tasks
  • Partitions and Shuffles
  • Data Locality
  • Job Performance HANDS-ON
  • Visualizing DAG Execution
  • Observing Task Scheduling
  • Understanding Performance
  • Measuring Memory Usage MODULE 5 Clustering Architecture LECTURE Cluster Managers for Spark: Spark Standalone, YARN, and Mesos
  • Understanding Spark on YARN HANDS-ON
  • Tracking Jobs through the Cluster UI
  • Understanding Deploy Modes
  • Specifying Executors, Cores, Memory MODULE 6 Spark Streaming LECTURE
  • Streaming Sources and Tasks
  • DStream APIs and Stateful Streams
  • Reliability and Fault Recovery HANDS-ON
  • Creating DStreams from Sources
  • Operating on DStream Data
  • Viewing Streaming Jobs in the Web UI MODULE 7 Machine Learning LECTURE
  • Basic Principles of Machine Learning
  • Spark ML API Patterns
  • Built-in Featurizing and Algorithm APIs LECTURE
  • Featurizing and Learning with RDDs
  • ML Using Pipelines and DataFrames

Scala

Introduction:

  • Introduction to Scala
  • Creating a Scala Doc
  • Creating a Scala Project
  • The Scala REPL
  • Scala Documentation

Basics:

  • Hello World
  • Primitive Types
  • Type inference
  • Vars vs Vals
  • Lazy Vals
  • Methods
  • Pass By Name
  • Infix Notation
  • No parens/Brackets
  • Default Arguments
  • Named Arguments

Classes:

  • Classes
  • Immutable and Mutable Fields
  • Methods
  • Default and Named Arguments

Objects:

  • Introduction
  • Inheritance
  • Main/Additional Constructors
  • Private Constructors
  • Uniform Access
  • Case Classes
  • Objects
  • Traits

COLLECTIONS:

  • Collections overview
  • Sequences and Sets
  • Options
  • Tuples and Maps
  • Higher Order Functions
  • Lists
  • Collection Manipulation
  • Simple Methods
  • Methods With Functions
  • Use Cases With Common Methods
  • Tuples

Options:

  • Option Implementation
  • Like Lists
  • Practice Application

Types:

  • Type parameterization
  • Covariance
  • Contravariance
  • Type Upper Bounds
  • ‘Nothing’ Type

Anonymous Classes:

  • Introduction
  • Structural Typing
  • Anonymous Classes With Structural Typing

Special Methods:

  • Apply
  • Update

Currying:

  • Introduction
  • Applications

Implicits:

  • Implicit Values/Parameters
  • Implicit Conversions
  • With Anonymous Classes
  • Implicit Classes
  • The ‘Pipe’ Operator

For Loops:

  • Introduction
  • Coding Style
  • With Options
  • And flatMap
  • Guards
  • Definitions

Var Args:

  • Introduction
  • Ascribing the _* type

Partial Functions:

  • Introduction
  • Match
  • Match Values/Constants
  • Match Types
  • Extractors
  • If Conditions
  • Or
  • With Collections
  • The Unapply

Recursion:

  • Examples
  • Optimization

More on Traits:

  • Stackable Traits
  • Examples

Case Objects and Classes:

  • Companion Objects
  • Case Classes and Case Objects
  • Apply and Unapply
  • Synthetic Methods
  • Immutability and Thread Safety

Idiomatic Scala:

  • For expressions
  • Pattern Matching
  • Handling Options
  • Handling Failures
  • Handling Futures

Advanced Types:

  • F-Bounded Polymorphism
  • Self Type Annotation
  • Introduction to Type