MySQL 8 for Big Data

Uncover the power of MySQL 8 for Big Data
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MySQL 8 for Big Data

Shabbir Challawala et al.

Uncover the power of MySQL 8 for Big Data
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Book Details

ISBN 139781788397186
Paperback266 pages

Book Description

With organizations handling large amounts of data on a regular basis, MySQL has become a popular solution to handle this structured Big Data. In this book, you will see how DBAs can use MySQL 8 to handle billions of records, and load and retrieve data with performance comparable or superior to commercial DB solutions with higher costs.

Many organizations today depend on MySQL for their websites and a Big Data solution for their data archiving, storage, and analysis needs. However, integrating them can be challenging. This book will show you how to implement a successful Big Data strategy with Apache Hadoop and MySQL 8. It will cover real-time use case scenario to explain integration and achieve Big Data solutions using technologies such as Apache Hadoop, Apache Sqoop, and MySQL Applier. Also, the book includes case studies on Apache Sqoop and real-time event processing.

By the end of this book, you will know how to efficiently use MySQL 8 to manage data for your Big Data applications.

Table of Contents

Chapter 1: Introduction to Big Data and MySQL 8
The importance of Big Data
The life cycle of Big Data
Structured databases
Basics of MySQL
New features in MySQL 8
Benefits of using MySQL
Installing MySQL 8
Evolution of MySQL for Big Data
Summary
Chapter 2: Data Query Techniques in MySQL 8
Overview of SQL
Database storage engines and types
Select statement in MySQL 8
Insert, replace, and update statements in MySQL 8
Transactions in MySQL 8
Aggregating data in MySQL 8
JSON
Summary
Chapter 3: Indexing your data for High-Performing Queries
MySQL indexing
MySQL 8 index types
Indexing JSON data
Summary
Chapter 4: Using Memcached with MySQL 8
Overview of Memcached
Setting up Memcached
Using of Memcached
Analyzing data stored in Memcached
Memcached replication configuration
Memcached APIs for different technologies
Summary
Chapter 5: Partitioning High Volume Data
Partitioning in MySQL 8
Horizontal partitioning in MySQL 8
Vertical partitioning
Pruning partitions in MySQL
Querying on partitioned data
Summary
Chapter 6: Replication for building highly available solutions
High availability
Replication with MySQL
Group replication
Summary
Chapter 7: MySQL 8 Best Practices
MySQL benchmarks and configurations
Best practices for MySQL queries
Best practices for the Memcached configuration
Best practices for replication
Summary
Chapter 8: NoSQL API for Integrating with Big Data Solutions
NoSQL overview
NoSQL versus SQL
Implementing NoSQL APIs
Summary
Chapter 9: Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop
Case study for log analysis
Apache Sqoop overview
Integrating Apache Sqoop with MySQL and Hadoop
Importing unstructured data to Hadoop HDFS from MySQL
Loading structured data to MySQL using Apache Sqoop
Summary
Chapter 10: Case study: Part II - Real time event processing using MySQL applier
Case study overview
MySQL Applier overview
Real-time integration with MySQL Applier
Organizing and analyzing data in Hadoop
Summary

What You Will Learn

  • Explore the features of MySQL 8 and how they can be leveraged to handle Big Data
  • Unlock the new features of MySQL 8 for managing structured and unstructured Big Data
  • Integrate MySQL 8 and Hadoop for efficient data processing
  • Perform aggregation using MySQL 8 for optimum data utilization
  • Explore different kinds of join and union in MySQL 8 to process Big Data efficiently
  • Accelerate Big Data processing with Memcached
  • Integrate MySQL with the NoSQL API
  • Implement replication to build highly available solutions for Big Data

Authors

Table of Contents

Chapter 1: Introduction to Big Data and MySQL 8
The importance of Big Data
The life cycle of Big Data
Structured databases
Basics of MySQL
New features in MySQL 8
Benefits of using MySQL
Installing MySQL 8
Evolution of MySQL for Big Data
Summary
Chapter 2: Data Query Techniques in MySQL 8
Overview of SQL
Database storage engines and types
Select statement in MySQL 8
Insert, replace, and update statements in MySQL 8
Transactions in MySQL 8
Aggregating data in MySQL 8
JSON
Summary
Chapter 3: Indexing your data for High-Performing Queries
MySQL indexing
MySQL 8 index types
Indexing JSON data
Summary
Chapter 4: Using Memcached with MySQL 8
Overview of Memcached
Setting up Memcached
Using of Memcached
Analyzing data stored in Memcached
Memcached replication configuration
Memcached APIs for different technologies
Summary
Chapter 5: Partitioning High Volume Data
Partitioning in MySQL 8
Horizontal partitioning in MySQL 8
Vertical partitioning
Pruning partitions in MySQL
Querying on partitioned data
Summary
Chapter 6: Replication for building highly available solutions
High availability
Replication with MySQL
Group replication
Summary
Chapter 7: MySQL 8 Best Practices
MySQL benchmarks and configurations
Best practices for MySQL queries
Best practices for the Memcached configuration
Best practices for replication
Summary
Chapter 8: NoSQL API for Integrating with Big Data Solutions
NoSQL overview
NoSQL versus SQL
Implementing NoSQL APIs
Summary
Chapter 9: Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop
Case study for log analysis
Apache Sqoop overview
Integrating Apache Sqoop with MySQL and Hadoop
Importing unstructured data to Hadoop HDFS from MySQL
Loading structured data to MySQL using Apache Sqoop
Summary
Chapter 10: Case study: Part II - Real time event processing using MySQL applier
Case study overview
MySQL Applier overview
Real-time integration with MySQL Applier
Organizing and analyzing data in Hadoop
Summary

Book Details

ISBN 139781788397186
Paperback266 pages
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