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Scala Programming Projects

Scala Programming Projects

By : Valot, Nicolas Jorand
3.8 (5)
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Scala Programming Projects

Scala Programming Projects

3.8 (5)
By: Valot, Nicolas Jorand

Overview of this book

Scala Programming Projects is a comprehensive project-based introduction for those who are new to Scala. Complete with step-by-step instructions and easy-to-follow tutorials that demonstrate best practices when building applications, this Scala book will have you building real-world projects in no time. Starting with the fundamentals of software development, you’ll begin with simple projects, such as developing a financial independence calculator, and then advance to more complex projects, such as a building a shopping application and a Bitcoin transaction analyzer. You’ll explore a variety of Scala features, including its OOP and FP capabilities, and learn how to write concise, reactive, and concurrent applications in a type-safe manner. You’ll also understand how to use libraries such as Akka and Play. Furthermore, you’ll be able to integrate your Scala apps with Kafka, Spark, and Zeppelin, along with deploying applications on a cloud platform. By the end of the book, you’ll have a firm foundation in Java programming that’ll enable you to solve a variety of real-world problems, and you’ll have built impressive projects to add to your professional portfolio.
Table of Contents (13 chapters)
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Understanding Apache Spark


Spark is an open source framework built to perform analytics on large datasets. Unlike other tools such as R, Python, and MathLab that are using in-memory processing, Spark gives you the possibility to scale out. And thanks to its expressiveness and interactivity, it also improves developer productivity.

There are entire books dedicated to Spark. It has a vast number of components and lots of areas to explore. In this book, we aim to get you started with the fundamentals. You should then be more comfortable exploring the documentation if you want to.

The purpose of Spark is to perform analytics on a collection. This collection could be in-memory and you could run your analytics using multiple threads, but if your collection is becoming too large, you are going to reach the memory limit of your system.

Spark solved this issue by creating an object to hold all of this data. Instead of keeping everything in the local computer's memory, Spark chunks the data into multiple...

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