So by now, we have imported all data into HDFS and the data has been processed using MapReduce. Now we need to transfer the data back to MySQL. Apache Sqoop provides the export feature to export data from HDFS storage to relational database like MySQL.
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MySQL 8 for Big Data
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MySQL 8 for Big Data
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Overview of this book
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 (11 chapters)
Preface
Free Chapter
Introduction to Big Data and MySQL 8
Data Query Techniques in MySQL 8
Indexing your data for High-Performing Queries
Using Memcached with MySQL 8
Partitioning High Volume Data
Replication for building highly available solutions
MySQL 8 Best Practices
NoSQL API for Integrating with Big Data Solutions
Case study: Part I - Apache Sqoop for exchanging data between MySQL and Hadoop
Case study: Part II - Real time event processing using MySQL applier
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