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
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
MySQL 8 for Big Data

You're reading from   MySQL 8 for Big Data Effective data processing with MySQL 8, Hadoop, NoSQL APIs, and other Big Data tools

Arrow left icon
Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781788397186
Length 296 pages
Edition 1st Edition
Languages
Tools
Concepts
Arrow right icon
Authors (4):
Arrow left icon
Chintan Mehta Chintan Mehta
Author Profile Icon Chintan Mehta
Chintan Mehta
Shabbir Challawala Shabbir Challawala
Author Profile Icon Shabbir Challawala
Shabbir Challawala
Jaydip Lakhatariya Jaydip Lakhatariya
Author Profile Icon Jaydip Lakhatariya
Jaydip Lakhatariya
Kandarp Patel Kandarp Patel
Author Profile Icon Kandarp Patel
Kandarp Patel
Arrow right icon
View More author details
Toc

Table of Contents (11) Chapters Close

Preface 1. Introduction to Big Data and MySQL 8 FREE CHAPTER 2. Data Query Techniques in MySQL 8 3. Indexing your data for High-Performing Queries 4. Using Memcached with MySQL 8 5. Partitioning High Volume Data 6. Replication for building highly available solutions 7. MySQL 8 Best Practices 8. NoSQL API for Integrating with Big Data Solutions 9. Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop 10. Case study: Part II - Real time event processing using MySQL applier

Organizing and analyzing data in Hadoop

As we learned in the Chapter 9, Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop, Hadoop can be used for processing unstructured data generated through relational databases like MySQL. In this topic, we will find out how we can use Hadoop for analyzing the unstructured data generated in MySQL 8. Based on our case study of e-commerce store, we will try to find out the bestselling product among the customers based on the order history of customers in e-commerce store. We will transfer the order data generated in MySQL 8 into Apache Hive using MySQL applier. Than we will use Hive Query Language (Hive-QL) for analyzing required data.

Hive-QL uses map-reduce algorithm which makes it much faster to analyze millions of data within seconds. Data generated in Hive can be transferred back to the MySQL 8 as a flat table...

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