Scala Programming for Big Data Analytics
Scala Programming for Big Data Analytics
About the Book
It's an open secret that we are living in the world of Big Data. Organisations are currently experiencing disruptive paradigm shift wherein they are increasingly adopting Big Data technologies to process large volumes of data to derive insights with the goal to orchestrate innovation and to stay competitive. As a result of that, demand of candidates with strong skill-set in these areas is experiencing exponential growth and people with skills in Big Data are among the highest paid ones as well.
In Big Data landscape, Hadoop is the de-facto framework that powers big data platforms with its suite of services and Apache Spark is the leading distributed and in-memory computing engine in Hadoop ecosystem. Apache Spark is being used for a diverse variety of Big Data use-cases like machine learning, ETL, graph analytics to name a few and is experiencing phenomenal growth and adoption in businesses all around the world. And Scala is the lingua-franca of Apache Spark i.e. Not only Apache Spark (and many other frameworks like Apache Kafka) is developed in Scala but it is also the recommended language for Apache Spark development as it provides the best performance and access to all the latest features in Apache Spark API releases. Thus, to develop skill-set in Apache Spark and build your career in this promising domain, there is a critical prerequisite i.e. you need to learn Scala!
Learning Scala has manifold benefits on its own as Scala is one of the hottest JVM based programming languages out there and candidates skilled in Scala are among the highest paid ones.
The challenge with Scala is that there is a steep learning curve. Scala combines advanced constructs from functional programming and object oriented principles and candidates willing to learn Scala become overwhelmed with the complexity and depth of language. On the other hand, specifically for getting started with Apache Spark development, one generally needs to master a subset of key concepts in Scala language. This itself is another issue because there is not a single book/resource out there that covers Scala programming language with the focus of Big Data development. Though there is no shortage of books/tutorials on Scala but they cover concepts with unnecessary depth and breadth which may not be relevant for Big Data development.
And this is exactly the problem that my book "Scala Programming for Big Data Analytics" addresses which has been written with one crisp goal: teach you just enough Scala only relevant for Big Data i.e. Apache Spark development with no fluff! Instead of bogging you down with needless details of irrelevant and complex concepts of Scala programming language features, the book covers only the most important concepts with laser-focus and necessary depth along with highlighting the best practices based on my versatile experience of using this language.
The book is crafted to be fully hands-on. If you'll follow this book, you'll find the impression that I am holding your hand and teaching you the concepts starting from the very basics. Each section of the book is complimented with series of hands-on code examples. The book will start by introducing Scala and will then will naturally progress to the topics including variables (mutable/immutable), data types, functions, collections, flow control, libraries usage and exception handling along with a gentle emphasis on object oriented programming and functional programming concepts wherever necessary coupled with best practices based on my versatile experience of using this language.
This book doesn't cover Apache Spark. Rather, it covers the key Scala programming language concepts necessary to develop mastery in Apache Spark. After this book, you will be able to learn Apache Spark with no hassle or even use Scala alone as its a general purpose language.
You don't need to have any prior programming language experience to use this book and you'll be able to do hands-on practice on your system (Windows/Mac/Linux) without any software cost.
Now you are one step away from learning one of the most in-demand languages i.e. Scala to excel your career in the promising and lucrative domain of Big Data. Get this book now and let's learn Scala!
About the Author
Table of Contents
- Context Setting
-
Chapter 0 - Scala Language
- Introduction
- Getting to know Scala
- Why Learn Scala
- Scala and Java
- Interoperability with Java Libraries
- Verbosity - Scala and Java
- Scala - A Statically Typed Language
- Apache Spark and Scala
- Scala Performance Benefits
- Learning Apache Spark
-
Chapter 1 - Installing Scala
- Introduction
- Checking Scala Installation Status in Your System
- Verifying Java Development Kit (JDK) Installation Status
- Installing Scala in Windows
- Verifying Scala Installation Status
- Exercise
-
Chapter 2 - Using Scala Shell
- Introduction
- Getting help in Scala shell
- Hello World in Scala REPL
- Understanding Hello World in Scala REPL Step by Step
- Real Life Example: Usefulness of Scala REPL’s Data Type Highlighting Feature
- Paste Mode in Scala REPL
- Retrieving History in Scala REPL
- Auto-completion Feature of Scala REPL
- Exiting from Scala REPL
- Exercise
-
Chapter 3 - Variables
- Introduction
- Immutability of Objects in Scala
- Defining Variables (Mutable and Immutable) in Scala
- Why Immutability Is So Emphasized in Scala?
- Mutability and Type-safety Caveats
- Specifying Types for Variables and Type Inference
- Exercise
-
Chapter 4 - Data Types
- Introduction
- Exercise - Data Types
- Boolean Type
- Exercises - Boolean Type
- String Type
- Exercise - String Types
- Special Types in Scala
- Type Casting in Scala
- Exercise - Special Types
-
Chapter 5 - Conditional Statements
- Introduction
- Caveats - Using {} after if/else
- Nested If-Else Statements
- If Else as Ternary Operator
- Pattern Matching
- Exercise
-
Chapter 6 - Code Blocks
- Introduction
- Caveats - Code Blocks
- Code Blocks and If/Else Statements
- Exercise
-
Chapter 7 - Functions
- Introduction
- Why use Functions at all?
- Intuitive Understanding of Functions
- Invoking a Function
- Caveats - Function Definition
- Functions With Multiple Parameters
- Positional Parameters
- Default Value of Parameters in Functions
- Function with No Arguments aka 0 Parity
- Single Line functions
- When To Actually Use Return Statements
- Passing Function As Arguments
- Anonymous Functions
-
Chapter 8 - Scala collections
- Introduction
- Real Life and Intuitive Examples of Collections
- Lists
- Indexing List Elements
- What Can You Store in Lists?
- Widely Used Lists Operations
- Iterating Over List
- Using Map Function for Iterating Over Lists
- Getting to Know Functional Programming Concepts
- Using foreach on Lists
- Using filter on Lists
- Reduce Operation on Lists
- List Equality Check
- Alternative Ways To Create Lists
- Exercise - Lists
- Sets
- Map Collections
- Indexing a Map
- Alternative Ways to Create Map Collections
- Manipulating Maps
- Iterating through Maps in Functional Style
- Tuples
- Indexing Tuples
- Iterating Over Tuples
- Alternative Ways to Create Tuples
- Mutable Collections
- Implications Related to Mutable Collections
- Mutable Maps
- Nested Collections
-
Chapter 9 - Loops
- Introduction
- Types of Loops in Scala
- Guards in For Loop
- While Loop
- Comparison of For and While Loop: Which One Suits Well in What Scenarios?
-
Chapter 10 - Using Classes and Packages
- Introduction
- Classes and Objects in Scala
- Mutating Attribute Values and Caveats
- Singleton Objects
- Classes and Packages
- Importing Packages
- Exercise
-
Chapter 11 - Exception Handling
- Introduction
- Fundamentals of Exception Handling in Scala
- Implications in Type Inference and Exception Handling
- Exercise - Exception Handling
-
Chapter 12 - Hello World in Apache Spark
- Development Environment for Apache Spark Development
- Instantiating Spark Session and Context Object Using OOP Concepts
- Using Spark Context Object’s Functions to Create Spark-Native Data Structure (RDD)
- Using RDD’s Transformations Employing Functional Programming and Scala Collection Concepts
- Employing Scala Functions Concepts in Spark RDD’s Transformations
- Conclusion and Beyond
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