Apache Spark and Scala Training

Live Online & Classroom Enterprise Certification Training

Master Apache Spark, a fast, in-memory distributed collections framework written in the programming language Scala. This Spark & Scala course will enable candidates to gain an in depth knowledge of Scala's programming model. It also gives them exposure to near-to-real-time data analytics through hands-on examples in Spark and Scala.

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What is Apache Spark Scala Training about?

Spark and Scala training module will equip candidates with the necessary skills to create applications in Spark with the implementation of Scala programming. Additionally, this training will also provide a clear comparison between Spark and Hadoop and cover techniques to increase candidates' application performance and enable high-speed processing.

With the use of advanced cloud-labs, this training will help candidates to gain seamless hands-on experience by enabling them to work on various use cases.

What are the objectives of Apache Spark Scala Training ?

At the end of Apache Spark and Scala training, you will be able to:

Describe Scala and its implementation
Explain Control Structures, Loops, Collection, etc.
Apply the concepts of Traits and OOPS in Scala
Explain Functional programming in Scala
Interpret Big Data challenges

Who is Apache Spark Scala Training for?

Data Scientists
Analytics Professionals
Developers & Testers
Teams getting started on Apache Spark and Scala projects

What are the prerequisites for Apache Spark Scala Training?

Prior Programming experience in Java or other languages required
Basic familiarity with Linux or Unix preferred
Intermediate-level of Hadoop understanding is good to have

Available Training Modes

Live Online Training

16 Hours

Classroom Training

3 Days

Self-Paced Training

9 Hours

Course Outline Expand All

Expand All

Introduction to Spark

Spark Overview

Map Reduce vs. Spark

Advantages of Spark over Map Reduce

Spark Components and full-stack

Working with Spark

Demo-Spark Installation

Spark Comparison with Hadoop

Introduction to Scala

Introduction to Scala

Scala Programming Constructs

Demo- Using Literals and Arithmetic operator

Demo-Using logical operators

Scala Type Interface

Demo- Using Type Interface

Scala Object-oriented Aspects

Demo- Use type interfaces, function and class

Demo- Operation on traits

Demo- Operation on list

Scala Functional Programming Aspects

Demo- Operation on pattern matching

Creating Applications in Spark using RDD

Introduction to RDDs

Working on Spark Project

Demo- Building scala project with the SBT tool

Demo-Run Scala application using jar file

Demo- Scala application to read Hadoop data

Working with RDDs

Demo- Scala application that performs GroupBy operation

Running SQL queries using SparkSQL

Spark SQL Overview

Working with Spark Session

Demo- Wordcount using Dataset API

Working with DataFrames

Demo-Spark SQL using DataFrame operations

Interoperability using different Approaches

Demo-Spark SQL using reflection-based approach

Demo- Run Spark SQL programmatically

Working with Datasets

Demo- Ways of creating datasets

Demo-Datasets Operations and Joining Datasets

Operating on various Data Sources

Demo- infer JSON dataset schema and load as a Dataset

Demo- Run Hive queries using Spark SQL

Catalog API

Spark Streaming

Introduction to Spark Streaming

Demo- Execute word count operation in streaming

Introduction to DStreams

Spark Streaming Sources

Transformation and Operations on DStreams

Demo-Perform Dataframe and SQL operations

Demo-Perform join operations

Performance Tuning

Demo-Capture and process netcat data

Demo-Capture and process flume data

Demo- Capture twitter data

Spark Structured Streaming

Introduction to Spark Structured Streaming

Demo- Batch vs. Streaming

Structured Streaming Architecture, model and its Components

Demo- Wordcount steps in Structured streaming

Structured Streaming APIs

Demo- Operations on dataframes/datasets

Demo- Data parsing with schema inference

Demo- Column construction in Structured Streaming

Demo- Using "groupBy" and "aggregation"

Demo- Capturing and processing of real data

Spark ML Programming

Machine Learning Applications and its Types

Machine Learning using Spark Mllib& Spark ML

ML pipeline

Spark Mllib Supported Types and Algorithms

Demo-Perform clustering using k-means

Demo-Perform classification using Linear Regression

Demo- Run linear regression

Demo- Perform Recommendation using Collaborative filtering

Demo- Run reccomendation system

Spark GraphX and GraphFrames

Graph and Graph Parallel System

GraphX and Property Graph

Demo- Create a Graph using GraphX

Graph Operator

Demo- Perform graph operations using graphX

Demo-Perform subgrpah operations

Graph Analytics

Introduction to GraphFrames

Demo- Implement presidential election results using GraphFrames

Demo- Create GraphFrame

Demo- Perform operations on GraphFrames

Demo-Working with GraphFrames

Demo- Select subgrpah on motif finding

GraphFrame algorithms

Who is the instructor for this training?

This Apache Spark and Scala training is led by a subject-matter expert with extensive experience in the domain. The trainer also has years of experience training and mentoring professionals in Spark and Scala.

Reviews

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Sony

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Stryker

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Heena Rajan

Mindtree