Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Scala and Spark for Big Data Analytics
  • Table Of Contents Toc
Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

By : Karim, Sridhar Alla
2.8 (12)
close
close
Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

2.8 (12)
By: Karim, Sridhar Alla

Overview of this book

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
Table of Contents (19 chapters)
close
close

Functional programming and data mutability

Pure functional programming is one of the best practices in functional programming and you should stick to it. Writing pure functions will make your programming life easier and you will be able to write code that's easy to maintain and extend. Also, if you want to parallelize your code then it will be easier to do so if you write pure functions.

If you're an FP purist, one drawback of using functional programming in Scala is that Scala supports both OOP and FP (see Figure 1), and therefore it's possible to mix the two coding styles in the same code base. In this chapter, we have seen several examples showing that writing pure functions is easy. However, combining them into a complete application is difficult. You might agree that advanced topics such as monads make FP intimidating.

I talked to many people and they think...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Scala and Spark for Big Data Analytics
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon