Apache Spark and Scala

Course Features

Course Details





Apache Spark and Scala

Apache Spark and Scala the most trending and highest paid Programming Jobs. Enrol Today!


Learn Apache Spark and scala from myTectra the market leader !

Apache Spark is an open-source cluster computing framework that was initially developed at UC Berkeley in the AMPLab. ... A Spark project contains various components such as Spark Core and Resilient Distributed Datasets or RDDs, Spark SQL, Spark Streaming, Machine Learning Library or Mllib, and GraphX.

Training Features

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes. Weekend Class : 10 sessions of 3 hours each. Weekday Class : 15 sessions of 2 hours each.

Lifetime Access

You get lifetime access to Learning Management System (LMS) where presentations, quizzes, installation guide & class recordings are there.

Real-life Case Studies

Live project based on any of the selected use cases, involving real time project of the various Apache spark and scala concepts.

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Assignments

Live project based on any of the selected use cases, involving of the various Apache spark and scala concepts.

Certification

Towards the end of the course, you will be given access to online Test. myTectra certifies you as an Apache spark and scala Expert based on the scoring of 60% or above.

Course Outline



Chapter 1:Spark And Hadoop Distributed File System

  • What is Spark
  • Comparison with Hadoop
  • Components of Spark

CHAPTER 2:Spark Components, Common Spark Algorithms-Iterative Algorithms, Graph Analysis, Machine Learning

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

CHAPTER 3:Running Spark On A Cluster, Writing Spark Applications Using Python, Java, Scala

  • Explain python example
  • Show installing a spark
  • Explain driver program
  • Explaining spark context with example
  • Define weakly typed variable
  • Combine scala and java seamlessly
  • Explain concurrency and distribution
  • Explain what is trait
  • Explain higher order function with example
  • Define OFI scheduler
  • Advantages of Spark
  • Example of Lamda using spark
  • Explain Mapreduce with example

CHAPTER 4:RDD And Its Operation

  • Difference between RISC and CISC
  • Define Apache Mesos
  • Cartesian product between two RDD
  • Define count
  • Define Filter
  • Define Fold
  • Define API Operations
  • Define Factors

CHAPTER 5:Spark, Hadoop, And The Enterprise Data Centre, Common Spark Algorithms

  • How hadoop cluster is different from spark
  • Define writing data
  • Explain sequence file and its usefulness
  • Define protocol buffers
  • Define text file, CSV, Object Files and File System
  • Define sparse metrics
  • Explain RDD and Compression
  • Explain data stores and its usefulness

CHAPTER 6:Spark Streaming

  • Define Elastic Search
  • Explain Streaming and its usefulness
  • Apache bookeeper
  • Define Dstream
  • Define mapreduce word count
  • Explain Paraquet
  • Scala ORM
  • Define Mlib
  • Explain multi graphix and its usefulness
  • Define property graph

CHAPTER 7:Spark Persistence In Spark

  • Persistence
  • Motivation
  • Example
  • Transformation
  • Scala and Python
  • Examples – K-means
  • Latent Dirichlet Allocation (LDA)

CHAPTER 8:Broadcast And Accumulator

  • Motivation
  • Broadcast Variables
  • Example: Join
  • Alternative if one table is small
  • Better version with broadcast
  • How to create a Broadcast
  • Accumulators motivation
  • Accumulator Rules
  • Custom accumulators
  • Another common use
  • Creating an accumulator using spark context object

CHAPTER 9:Spark SQL And RDD

  • Spark SQL main capabilities
  • Spark SQL usage diagram
  • Spark SQL
  • Important topics in Spark SQL- Data frames
  • Twitter language analysis

CHAPTER 10:Introduction Of Scala

  • Scala Overview

CHAPTER 11:Pattern Matching

  • Advantages of Scala
  • REPL (Read Evaluate print loop)
  • Language Features
  • Type Interface
  • Higher order function
  • Option
  • Pattern Matching
  • Collection
  • Currying
  • Traits
  • Application Space

CHAPTER 12:Executing The Scala Code

  • Uses of scala interpreter
  • Example of static object timer in scala
  • Testing of String equality in scala
  • Implicit classes in scala with examples
  • Recursion in scala
  • Currying in scala with examples
  • Classes in scala

Chapter 13:Classes Concept In Scala

  • Constructor
  • Constructor overloading
  • Prop erties
  • Abstract classes
  • Type hierarchy in Scala
  • Object equality
  • Val and var methods

Chapter 14:Case Classes And Pattern Matching

  • Sealed traits
  • Case classes
  • Constant pattern in case classes
  • Wild card pattern
  • Variable pattern
  • Constructor pattern
  • Tuple pattern

Chapter 15:Concepts Of Traits With Example

  • Java equivalents
  • Advantages of traits
  • Avoiding boilerplate code
  • Linearization of traits
  • Modelling a real world example

Chapter 16:Scala Java Interoperability

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

Chapter 17:Scala Collections

  • Classification of scala collections
  • Iterable
  • Iterator and iterable
  • List sequence example in scala

Chapter 18:Mutable Collections Vs. Immutable Collections

  • Array in scala
  • List in scala
  • Difference between list and list buffer
  • Array buffer
  • Queue in scala
  • Dequeue in scala
  • Mutable queue in scala
  • Stacks in scala
  • Sets and maps in scala
  • Tuples

Chapter 19:Use Case Bobsrockets Package

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


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