Evolution of MySQL for Big Data

Most enterprises have used MySQL as a relational database for many decades. There is a large amount of data stored, which is used either for transactions or analysis on the data that is collected and generated, and this is where Big Data analytic tools need to be implemented. This is now possible with MySQL integration with Hadoop. Using Hadoop, data can be stored in a distributed storage engine and you can also implement the Hadoop cluster for the distributed analytical engine for Big Data analytics. Hadoop is most preferred for its massive parallel processing and powerful computation. With the combination of MySQL and Hadoop, it is now possible to have real-time analytics where Hadoop can store the data and work in parallel with MySQL to show the end results in real time; this helps address many use cases like GIS information, which has been explained in the Introducing MySQL 8 section of this chapter. We have seen the Big Data life cycle previously where data can be transformed to generate analytic results. Let's see how MySQL fits in to the life cycle.

The following diagram illustrates how MySQL 8 is mapped to each of the four stages of the Big Data life cycle: