What this book covers

Chapter 1, Introduction to Big Data and MySQL 8, provides an overview of Big Data and MySQL 8, their importance, and life cycle of big data. It covers the basic idea of Big Data and its trends in the current market. Along with that, it also explains the benefits of using MySQL, takes us through the steps to install MySQL 8, and acquaints us with newly introduced features in MySQL 8.

Chapter 2, Data Query Techniques in MySQL 8, covers the basics of querying data on MySQL 8 and how to join or aggregate data set in it.

Chapter 3, Indexing your data for High-Performing Queries, explains about indexing in MySQL 8, introduces the different types of indexing available in MySQL, and shows how to do indexing for faster performance on large quantities of data.

Chapter 4, Using Memcached with MySQL 8, provides an overview of Memcached with MySQL and informs us of the various advantages of using it. It covers the Memcached installation steps, replication configuration, and various Memcached APIs in different programming languages.

Chapter 5, Partitioning High Volume Data, explains how high-volume data can be partitioned in MySQL 8 using different partitioning methods. It covers the various types of partitioning that we can implement in MySQL 8 and their use with Big Data.

Chapter 6, Replication for building highly available solutions, explains implementing group replication in MySQL 8. Chapter talks about how large data can be scaled and replicating of data can be faster using different techniques of replication.

Chapter 7, MySQL 8 Best Practices, covers the best practices of using MySQL 8 for Big Data. It has all the different kinds of dos and don'ts for using MySQL 8.

Chapter 8, NoSQL API for Integrating with Big Data Solutions, explains integration of NoSQL API for acquiring data. It also explains NoSQL and its various APIs in different programming languages for connecting NoSQL with MySQL.

Chapter 9, Case Study: Part I - Apache Sqoop for Exchanging Data between MySQL and Hadoop, explains how bulk data can be efficiently transferred between Hadoop and MySQL using Apache Sqoop.

Chapter 10, Case Study: Part II - Realtime event processing using MySQL applier, explains real-time integration of MySQL with Hadoop, and reading binary log events as soon as they are committed and writing them into a file in HDFS.