Hadoop Spark Classes in BTM Layout Bangalore

IT Networks offers the best Hadoop Spark Training in BTM Layout with most experienced professionals. We conduct all our students from the Basic Level of Hadoop Spark Classes to Advanced level. All these Classes are undergone not only theoretically, but also are executed on the real-time basis. Our Trainers are Real-time Working professionals in the Hadoop Spark Field from many years with hands on real time Hadoop Spark Project Knowledge.

IT Networks is a highly experienced Hadoop Spark Training Institute in BTM Layout. This has helped students to get placed in top MNCs. We Offer Hadoop Spark Classes in BTM Layout for working professionals and students. we offer Regular training classes in Morning Batches and Evening Batches we also offer Weekend Training classes and Fast-track Training Classes for Hadoop Spark Course.

Introduction to Big Data, Hadoop & Spark Architecture

Introduction to Course

What is covered and not covered

Data Explosion, Data Sources, Data types

What is Big Data, Benefits & Big Data Problem

Limitations of Traditional Parallel Systems

Solution using Hadoop Framework

Characteristics and Types of Big Data Systems

What is Hadoop, History of Hadoop

Hadoop Architecture, Namenode, Job Tracker

HDFS and Map Reduce, Map Reduce example

Limitations of Hadoop 1.0 and MapReduce

Hadoop 2.0 and YARN Architecture

What is Apache Spark?

Apache Spark and Map Reduce differences

Spark Stack Architecture and Advantages

Spark History and Releases

Spark for Data science & Data processing tasks

Learning Scala – Functional Programming

Functions, Methods & Procedures

Function Literals / Anonymous Functions

Higher Order Functions – Function as a variable

Higher Order Functions – Passing function as parameter

Higher Order Functions – Returning a function

Higher Order Functions – Closures

Higher Order Functions – Partially Applied functions

Higher Order Functions – Call by Name, Call by value

Regular expressions and Pattern Matching

Case classes and Pattern Matching

Learning Scala – Basic & Object Oriented Programming

Scala Installation & Scala REPL Interpreter

First Scala Program, Scala Scripts

Scala Basics – Variables, Types, Control Structures, Loops

Scala Basics – Strings & String interpolations

Scala Basics – Functions without Parameters

Scala Basics – Functions with parameters

Scala Basics – Arrays, Lists, Ranges and Tuples

Classes, Objects and Apply method

Constructors and Parameters

Method Declaration, Call by Name

Singleton Objects, Packaging

Inheritance, Extending a class, Overriding

Traits, Case classes

Hands-on Scala Programming Labs

Creating Strings, String equality & splitting

Finding and replacing patterns in strings

Looping with Foreach, Embedded if statements

Using If construct as a Ternary Operator

Using Match expressions and assigning the result to a variable

Using Pattern matching in Match expressions

Using classes, Objects, Methods and Traits

Using Function Literals

Working with Higher Order Functions

Creating Collections

Using Map, Flatmap, Filter on Collections

Hands on Lab – Using Foreach and reduce on Collections

Spark Essentials

Getting started with Spark

Spark Python and Scala Shells

Spark Context

Spark Runtime Architecture – Workers and Cluster Managers

Spark Runtime Architecture – Driver Programs, Executors and Tasks

How a Spark Application works

Data sources for loading data into Spark

Understanding Hadoop Input and Output Formats

Understanding Data Serialization Formats – Avro and Sequence files

Understanding Columnar file formats – RCFile, ORC and Parquet

Advanced Spark Programming

Data Partitioning in Spark

Operations that benefit from partitioning

Operations that affect partitioning

Saving RDDs

Caching RDDs and Persistence

Word Count program using Spark

Spark Program Lifecycle

Spark Variables

Spark Broadcast Variables

Spark Accumulators and Fault Tolerance

Spark Core Programming – Understanding RDDs

Resilient Distributed Datasets (RDD)

Data sources for creating RDDs

Creating RDDs from text, csv and tsv files.

Creating RDDs from JSON files & Sequence files

Creating RDDs from Hadoop InputFormat

Creating RDDs from HDFS and Amazon S3 files

Creating RDDs from NOSQL Databases

RDD Operations – Transformations and Actions

Lazy evaluations

Loading and Saving RDDs

Passing functions to Spark, Spark Closures

Spark Key Value RDDs, Creating Pair RDDs

Pair RDD Transformations – Aggregations, Grouping, Joins & Sorting

Actions on Pair RDDs

Building and Running a Spark Scala program

Spark Scala API , Spark JAR files

Running a Spark program using spark-submit

Running a spark program on Standalone Cluster

Running a spark program on YARN

Launching Spark jobs from Java and Scala

Building a Spark application with Eclipse/Scala IDE and Maven, Maven Dependencies

Building a Spark application with Eclipse/Scala IDE and SBT

Building a Spark Fat JAR

Tuning and Debugging Spark for Performance

Configuring Spark with SparkConf

Components of a Spark program – Jobs, Tasks and Stages

Spark Web UI Deep Dive

Spark RDD Lineage

Spark Logs

Serialization and Memory Management to improve performance

Project Tungsten

Hardware Provisioning an Performance Management

Monitoring and Debugging a Spark Application

Spark SQL and Dataframes Programming I

Spark SQL and Hive Interoperability, Spark SQL Performance Advantages

ETL and Data warehousing with Spark SQL

Initializing Spark SQL using SQLContext

Dataframes Introduction, Caching Dataframes

Creating Dataframe from RDD using case class and toDF method to infer schema

Creating Dataframe from RDD using StructType and createDataFrame to specify schema

Creating Dataframes from Scala Collections

Creating Dataframes from text files, csv and tsv files

Creating Dataframes from JSON files, Parquet files & Hive Tables

Loading & Saving Dataframes

Hands-on Projects using Spark RDDs