Apache Spark,Scala and Storm Training

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Apache Spark,Scala and Storm Training

About SimplyAnalytics

We are the best Training institute for learning Apache Spark,Scala and Storm Training in Chennai . We have expert trainers and excellent materials to transform your skills to fit into the job market.

Storm

Module 1 : Storm Architecture

  • 1.Baysean Law
  • 2.Hadoop Distributed Computing
  • 3.Features of Big Data
  • 4.Legacy Architecture of Real Time System
  • 5.Difference b/w Storm and Hadoop
  • 6.Logical Dynamic and Components in Storm
  • 7.Topology in Storm
  • 8.Storm Execution Components
  • 9.Stream Grouping
  • 10.Tuple
  • 11.Spout
  • 12.Bolt-normalization bolt

Module 2 : Apache storm installation

  • Apache storm installation

Module 3 : Grouping

  • 1.Grouping and its different type
  • 2.Reliable and unreliable messaging
  • 3.How to get Data – Direct connection and Enqueued message
  • 4.Life cycle of bolt

Module 4 : What is Trident

  • 1.Trident Spouts
  • 2.Types of trident spouts
  • 3.Trident spout components
  • 4.Trident spout Interface
  • 5.Trident filter, function & Aggregator

Module 5 : Boot Stripping

  • 1.What is Twitter Boot Stripping
  • 2.Deep Dive in Boot Stripping
  • 3.Fundamental concept of storm
  • 4.Storm Development Environment

Projects

Real Life Storm Project

The project bolt blue print

Apache Spark

Module 1 : Why Spark? Explain Spark and Hadoop Distributed File System

  • 1.What is Spark
  • 2.Comparison with Hadoop
  • 3.Components of Spark

Module 2 : Spark Components, Common Spark Algorithms-Iterative Algorithms, Graph Analysis,

Machine Learning

  • 1.Apache Spark- Introduction, Consistency, Availability, Partition
  • 2.Unified Stack Spark
  • 3.Spark Components
  • 4.Comparison with Hadoop – Scalding example, mahout, storm, graph

Module 3 : Running Spark on a Cluster, Writing Spark Applications using Python, Java, Scala

  • 1.Explain python example
  • 2.Show installing a spark
  • 3.Explain driver program
  • 4.Explaining spark context with example
  • 5.Define weakly typed variable
  • 6.Combine scala and java seamlessly.
  • 7.Explain concurrency and distribution.
  • 8.Explain what is trait.
  • 9.Explain higher order function with example.
  • 10.Define OFI scheduler.
  • 11.Advantages of Spark
  • 12.Example of Lamda using spark
  • 13.Explain Mapreduce with example

Module 4 : RDD and its operation

  • 1.Difference between RISC and CISC
  • 2.Define Apache Mesos
  • 3.Cartesian product between two RDD
  • 4.Define count
  • 5.Define Filter
  • 6.Define Fold
  • 7.Define API Operations
  • 8.Define Factors

Module 5 : Spark, Hadoop, and the Enterprise Data Centre, Common Spark Algorithms

  • 1.How hadoop cluster is different from spark
  • 2.Define writing data
  • 3.Explain sequence file and its usefulness
  • 4.Define protocol buffers
  • 5.Define text file, CSV, Object Files and File System
  • 6.Define sparse metrics
  • 7.Explain RDD and Compression
  • 8.Explain data stores and its usefulness

Module 6 : Spark Streaming

  • 1.Define Elastic Search
  • 2.Explain Streaming and its usefulness
  • 3.Apache bookeeper
  • 4.Define Dstream
  • 5.Define mapreduce word count
  • 6.Explain Paraquet
  • 7.Scala ORM
  • 8.Define Mlib
  • 9.Explain multi graphix and its usefulness
  • 10.Define property graph

Module 7 : Spark Persistence in Spark

  • 1.Persistence
  • 2.Motivation
  • 3.Example
  • 4.Transformation
  • 5.Scala and Python
  • 6.Examples – K-means
  • 7.Latent Dirichlet Allocation (LDA)

Module 8 : Broadcast and accumulator

  • 1.Motivation
  • 2.Broadcast Variables
  • 3.Example: Join
  • 4.Alternative if one table is small
  • 5.Better version with broadcast
  • 6.How to create a Broadcast
  • 7.Accumulators motivation
  • 8.Example: Join
  • 9.Accumulator Rules
  • 10.Custom accumulators
  • 11.Another common use
  • 12.Creating an accumulator using spark context object

Module 9-Spark SQL and RDD

  • 1.Introduction
  • 2.Spark SQL main capabilities
  • 3.Spark SQL usage diagram
  • 4.Spark SQL
  • 5.Important topics in Spark SQL- Data frames
  • 6.Twitter language analysis

Mini Projects

Project 1. List the items

Project 2. Sorting of Records

Project 3. Show a histogram of date vs users created. Optionally, use a rich visualization like

Project 4. Prepare a map of tags vs # of questions in each tag and display it.

Major Projects

Project 1 Movie Recommendation

Project 2 Twitter API Integration for tweet Analysis

Project 3 Data Exploration Using Spark SQL – Wikipedia dataset

Scala

Module 1 : Introduction of Scala

  • 1Scala Overview

Module 2 : Pattern Matching

  • 1.Advantages of Scala
  • 2.REPL (Read Evaluate print loop)
  • 3.Language Features
  • 4.Type Interface
  • 5.Higher order function
  • 6.Option
  • 7.Pattern Matching
  • 8.Collection
  • 9.Currying
  • 10.Traits
  • 11.Application Space

Module 3 : Executing the Scala code

  • 1.Uses of scala interpreter
  • 2.Example of static object timer in scala
  • 3.Testing of String equality in scala
  • 4.Implicit classes in scala with examples.
  • 5.Recursion in scala
  • 6.Currying in scala with examples.
  • 7.Classes in scala

Module 4 : Classes concept in Scala

  • 1.Constructor
  • 2.Constructor overloading
  • 3.Properties
  • 4.Abstract classes
  • 5.Type hierarchy in Scala
  • 6.Object equality
  • 7.Val and var methods

Module 5 : Case classes and pattern matching

  • 1.Sealed traits
  • 2.Case classes
  • 3.Constant pattern in case classes
  • 4.Wild card pattern
  • 5.Variable pattern
  • 6.Constructor pattern
  • 7.Tuple pattern

Module 6 : Concepts of traits with example

  • 1.Java equivalents
  • 2.Advantages of traits
  • 3.Avoiding boilerplate code
  • 4.Linearization of traits
  • 5.Modelling a real world example

Module 7 : Scala java Interoperability

  • 1.How traits are implemented in scala and java
  • 2.How extending multiple traits is handled

Module 8 : Scala collections

  • 1.Classification of scala collections
  • 2.Iterable
  • 3.Iterator and iterable
  • 4.List sequence example in scala

Module 9 : Mutable collections vs. Immutable collections

  • 1.Array in scala
  • 2.List in scala
  • 3.Difference between list and list buffer
  • 4.Array buffer
  • 5.Queue in scala
  • 6.Dequeue in scala
  • 7.Mutable queue in scala
  • 8.Stacks in scala
  • 9.Sets and maps in scala
  • 10.Tuples

Module 10 : Use Case bobsrockets package

  • 1.Different import types
  • 2.Selective imports
  • 3.Testing-Assertions
  • 4.Scala test case- scala test fun. Suite
  • 5.Junit test in scala
  • 6.Interface for Junit via Junit 3 suite in scala test
  • 7.SBT
  • 8.Directory structure for packaging scala application

Why choose SimplyAnalytics for Apache Spark,Scala and Storm Training in Chennai?

  • 1. 100% Practical and placement oriented training.
  • 2. We are registered training organization.
  • 3. Expert trainers from IT industries.
  • 4. Placements Assistance.
  • 5. Flexible timings.
  • 6. Weekdays and weekend batches.
  • 7. Affordable fees.
  • 8. Air conditioned classroom.
  • 9. Wi-Fi enabled training institute.
  • 10. Best Lab specialities.

Are you located in any of these areas – Adambakkam, Camp Road, Chromepet, Ekkattuthangal, Guindy, kovilambakkam, Madipakkam, Medavakkam, Nanganallur, Navalur, Nungambakkam, OMR, Pallikaranai, Perungudi, Rajakilpakkam, Saidapet, Sholinganallur,Siruseri, St.Thomas Mount, T. Nagar, Tambaram, Tambaram East, Thiruvanmiyur, Thoraipakkam, Velachery, and West Mambalam.

Our Medavakkam office is just few kilometre away from your location. If you need the best Apache Spark,Scala and Storm Training in Chennai travelling of extra kilometres is worth it .

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Course Features

  • Lectures 1
  • Quizzes 1
  • Duration 50 hours
  • Skill level All level
  • Language English
  • Students 0
  • Assessments Self
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