Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Scala and Spark for Big Data Analytics
  • Toc
  • feedback
Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

By : Karim, Sridhar Alla
2.8 (12)
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

Aggregations

Aggregation is the method of collecting data based on a condition and performing analytics on the data. Aggregation is very important to make sense of data of all sizes, as just having raw records of data is not that useful for most use cases.

For example, if you look at the following table and then the aggregated view, it is obvious that just raw records do not help you understand the data.

Imagine a table containing one temperature measurement per day for every city in the world for five years.

Shown in the following is a table containing records of average temperature per day per city:

City

Date Temperature
Boston 12/23/2016 32
New York 12/24/2016 36
Boston 12/24/2016 30
Philadelphia 12/25/2016 34
Boston 12/25/2016 28

If we want to compute the average temperature per city for all the days we have measurements for in the above table, we can see...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete