Press Release: Scala Programming for Big Data Analytics Book Launch

As you may know, I was writing a book on Scala Programming and I am just about to launch it. As a part of its launch campaign, a press release is also being distributed to major news websites. I am also publishing the press release on my blog as well that explains what this book is about.

Irfan Elahi teaches Scala Programming in his new book “Scala Programming for Big Data Analytics”

Big Data and Machine Learning expert, Irfan Elahi, launches a new book titled “Scala Programming for Big Data Analytics,” providing readers with an opportunity to learn Scala Programming Language with easescala programming for big data analytics best book to learn scala

Irfan Elahi is a leading Big Data evangelist that has made a name for himself using Big Data and Machine Learning to support business growth with multifaceted and strong ties in several industries. As part of his goals of spreading knowledge and expertise about his domain and ensuring that every business and individual enjoys immense benefits out of this, Irfan recently launched what has been described by many as the “Best book to learn Scala” – Scala Programming for Big Data Analytics, a comprehensive guide on getting started with Scala for Apache Spark development.

Big Data technologies have become increasingly popular in recent times with businesses across different industries aiming to process large volumes of data to derive insights with the goal of orchestrating innovation and staying ahead of the competition. Consequently, there is an increase in the demand for individuals with the right skill-set in these areas, which has also made such people one of the highest paid workers at the moment. Unfortunately, there are not too many of such professionals and this is where Irfan is looking to make a difference by bridging the gap and developing more Big Data Analytics professionals across the globe.

What is the best book to learn Scala for Big Data Analytics?

Due to the relatively steep learning curve of Scala, most books and other such resources on the subject are somewhat peripheral, covering the basic concepts without addressing the subject in-depth. However, “Scala Programming for Big Data Analytics” was written to teach interested persons just Scala only relevant for Big Data, that is, Apache Spark development without bogging them with irrelevant details and complex concepts of Scala programming language features.

Irfan uses his versatile experience and expertise using the Scala programming language to offer a practical guide that teaches the concepts of the language from the basics. Some of the topics covered include an introduction to Scala, variables (mutable/immutable), data types, functions, collections, flow control, libraries usage, and some emphasis on object-oriented programming and functional programming concepts.

Scala Programming for Big Data Analytics by Irfan Elahi is designed for everyone and anyone interested in learning Scala Programming regardless of their programming language experience. Its cost-effectiveness that allows users to do hands-on practice on their system (Windows/Mac/Linux) without any software cost is another amazing feature of the book.

Here is more information about Irfan Elahi and Scala Programming for Big Data Analytics.

About Irfan Elahi

Irfan Elahi is a Senior Consultant in Deloitte Australia, the world’s largest consultancy firm, specializing in Big Data and Machine Learning. He is called the Big Data evangelist and has worked on several projects in Australia in end-to-end lifecycle to design, prototype, develop and deploy production-grade Big Data solutions in Amazon Web Services (AWS) and Azure.

Irfan is also a public speaker and has presented on multiple occasions like Big Data conferences, universities and meet-ups all around the world. He also has launched Udemy courses on Apache Spark for Big Data Analytics and R Programming for Data Science with more than 18,000 students from 145+ countries enrolled in them.

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Why you should learn Scala:

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.

What’s covered in Scala Programming for Big Data Analytics book:

Though you can get details of its content on the book’s landing page, but I am also sharing the table of content here for your convenience:

  • Context Setting
  • Chapter 0 – Scala Language
    • Getting to know Scala
    • Why Learn Scala
    • Scala and Java
    • Interoperability with Java Libraries
    • Scala Verbosity and Java
    • Statically Typed Language
    • Apache Spark and Scala
    • Performance Benefits
    • Learning Apache Spark
  • Chapter 1 – Installing Scala
    • Checking Scala Installation Status in Your System
    • Verifying Java Development Kit (JDK) Installation Status
    • Installing Scala in Windows
    • Verifying Scala Installation Status
    • Exercises
  • Chapter 2 – Using Scala Shell
    • 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
    • Exercises
  • Chapter 3 – Variables
    • 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
    • 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
    • Caveats – Using {} after if/else
    • Nested If-Else Statements
    • If Else as Ternary Operator
    • Pattern Matching
    • Exercise – Condition Statements
  • Chapter 6 – Code Block
    • Caveats – Code Blocks
    • Code Blocks and If/Else Statements
    • Exercise
  • Chapter 7 – Functions
    • 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
    • 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
    • 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
    • Classes and Objects in Scala
    • Mutating Attribute Values and Caveats
    • Singleton Objects
    • Classes and Packages
    • Importing Packages
    • Exercise
  • Chapter 11 – Exception Handling
    • Fundamentals of Exception Handling in Scala
    • Implications in Type Inference and Exception Handling
    • Exercise – Exception Handling
  • Conclusion and Beyond

 

Get the book now and develop skill-set in this amazing language!