Apache Spark & Scala Tutorial

What is Apache Spark?

Apache Spark is an open-source cluster computing framework that was initially developed at UC Berkeley in the AMPLab.

As compared to the disk-based, two-stage MapReduce of Hadoop, Spark provides up to 100 times faster performance for a few applications with in-memory primitives.

This makes it suitable for machine learning algorithms, as it allows programs to load data into the memory of a cluster and query the data constantly.

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.

In the next section of the Apache Spark and Scala tutorial, let’s speak about what Apache Spark is.

What is Apache Scala?

Scala is a modern and multi-paradigm programming language. It has been designed for expressing general programming patterns in an elegant, precise, and type-safe way. One of the prime features is that it integrates the features of both object-oriented and functional languages smoothly.

It is a pure object-oriented language, as every value in it is an object. The objects’ behavior and types are explained through traits and classes.

It is also a functional language, as every function in it is a value. By providing a lightweight syntax for defining anonymous functions, it provides support for higher-order functions.  

In addition, the language also allows functions to be nested and provides support for carrying. It also has features like case classes and pattern matching model algebraic types support.

Scala is statically typed, being empowered with an expressive type system. The system enforces the use of abstractions in a coherent and safe way. To be particular, this system supports various features like annotations, classes, views, polymorphic methods, compound types, explicitly typed self-references and upper and lower type bounds.

When it comes to developing domain-specific applications, it generally needs domain-specific language extensions. Scala, being extensible, provides an exceptional combination of language mechanisms. Due to this, it becomes easy to add new language constructs as libraries

In the next section of the Apache Spark and Scala tutorial, we’ll discuss the benefits of Apache Spark and Scala yo professionals and organizations.

Benefits of Apache Spark and Scala to Professionals and Organizations

Following are the benefits of Apache Spark and Scala

  • Provides highly reliable fast in memory computation.

  • Efficient in interactive queries and iterative algorithm.

  • Fault tolerance capabilities because of immutable primary abstraction named RDD.

  • Inbuilt machine learning libraries.

  • Provides processing platform for streaming data using spark streaming.

  • Highly efficient in real time analytics using spark streaming and spark sql.

  • Graphx libraries on top of spark core for graphical observations.

  • Compatibility with any api JAVA, SCALA, PYTHON, R makes programming easy.

In the next section of the Apache Spark and Scala tutorial, we’ll discuss the prerequisites of apache spark and scala.

Apache Spark and Scala Tutorial Prerequisites

The basic prerequisite of the Apache Spark and Scala Tutorial is a fundamental knowledge of any programming language is a prerequisite for the tutorial. Participants are expected to have basic understanding of any database, SQL, and query language for databases. Working knowledge of Linux or Unix based systems, while not mandatory, is an added advantage for this tutorial.

Let us explore the target audience of Apache Spark and Scala Tutorial in the next section.

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Target Audience of Apache Spark and Scala Tutorial

The tutorial is aimed at professionals aspiring for a career in growing and demanding fields of real-time big data analytics. Analytics professionals, research professionals, IT developers, testers, data analysts, data scientists, BI and reporting professionals, and project managers are the key beneficiaries of this tutorial. Other aspirants and students, who wish to gain a thorough understanding of Apache Spark can also benefit from this tutorial.

Let us explore the Apache Spark and Scala Tutorial Overview in the next section.

Apache Spark and Scala Tutorial Overview

The Apache Spark and Scala training tutorial offered by Simplilearn provides details on the fundamentals of real-time analytics and need of distributed computing platform.

This tutorial will :

  • Explain Scala and its features.

  • Enhance your knowledge of the architecture of Apache Spark.

  • Explain the process of installation and running applications using Apache Spark.

  • Enhance your knowledge of performing SQL, streaming, and batch processing.

  • Explain Machine Learning and Graph analytics on the Hadoop data.

In the next section, we will discuss the objectives of the Apache Spark and Scala tutorial.


After completing this tutorial, you will be able to:

  • Explain the process to install Spark

  • Describe the features of Scala

  • Discuss how to use RDD for creating applications in Spark

  • Explain how to run SQL queries using SparkSQL

  • Discuss the features of Spark Streaming

  • Explain the features of Spark ML Programming

  • Describe the features of GraphX Programming

Let us explore the lessons covered in Apache Spark and Scala Tutorial in the next section.

Lessons Covered in this Apache Spark and Scala Tutorial

There are seven lessons covered in this tutorial. Take a look at the lesson names that are listed below

Lesson No

Chapter Name

What You’ll Learn

Lesson 1

Introduction to Spark Tutorial

In this chapter, you’ll be able to:

  • Describe the limitations of MapReduce in Hadoop

  • Compare batch vs. real-time analytics

  • Describe the application of stream processing and in-memory processing.

  • Explain the features and benefits of Spark.

  • Explain how to install Spark as a standalone user,

  • Compare Spark vs. Hadoop Eco-system.

Lesson 2

Introduction to Programming in Scala Tutorial

In this chapter, you’ll be able to:

  • Explain the features of Scala.

  • List the basic data types and literals used in Scala.

  • List the operators and methods used in Scala.

  • Discuss a few concepts of Scala.

Lesson 3

Using RDD for Creating Applications in Spark Tutorial

In this chapter, you’ll be able to:

  • Explain the features of RDDs

  • Explain how to create RDDs

  • Describe RDD operations and methods

  • Discuss how to run a Spark project with SBT

  • Explain RDD functions, and

  • Describe how to write different codes in Scala

Lesson 4

Running SQL Queries using Spark SQL Tutorial

In this chapter, you’ll be able to:

  • Explain the importance and features of SparkSQL

  • Describe the methods to convert RDDs to DataFrames

  • Explain a few concepts of SparkSQL, and

  • Describe the concept of hive integration

Lesson 5

Spark Streaming Tutorial

In this chapter, you’ll be able to:

  • Explain a few concepts of Spark streaming

  • Describe basic and advanced sources

  • Explain how stateful operations work

  • Explain window and join operations

Lesson 6

Spark ML Programming Tutorial

In this chapter, you’ll be able to:

  • Explain the use cases and techniques of Machine Learning.

  • Describe the key concepts of Spark Machine Learning.

  • Explain the concept of a Machine Learning Dataset.

  • Discuss Machine Learning algorithm, model selection via cross-validation.

Lesson 7

Spark GraphX Programming Tutorial

In this chapter, you’ll be able to:

  • Explain the fundamental concepts of Spark GraphX programming

  • Discuss the limitations of the Graph Parallel system

  • Describe the operations with a graph, and

  • Discuss the Graph system optimizations


With this, we come to an end about what this Apache Spark and Scala tutorial include. In the next chapter, we will discuss an Introduction to Spark Tutorial.

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