Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

Arrow left icon
Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Understanding the evolution of schema awareness


Spark can process data from various data sources, such as HDFS, Cassandra, and relational databases. Big data frameworks (unlike relational database systems) do not enforce a schema while writing data into it. HDFS is a perfect example of where any arbitrary file is welcome during the write phase. The same is true with Amazon S3. Reading data is a different story, however. You need to give some structure to even completely unstructured data to make sense out of it. With this structured data, SQL comes in very handy, when it comes to making sense out of some data.

Getting ready

Spark SQL is a component of the Spark ecosystem, introduced in Spark 1.0 for the first time. It incorporates a project named Shark, which was an attempt to make Hive run on Spark.

Hive is essentially a relational abstraction; it converts SQL queries into MapReduce jobs. See the following figure:

Shark replaced the MapReduce part with Spark while retaining most of the code...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image