Spark machine learning APIs

In this section, we will describe two key introduced by the Spark machine learning libraries (Spark MLlib and Spark ML) and the most widely used implemented algorithms that align with the supervised and unsupervised learning techniques we discussed in the previous sections.

Spark machine learning libraries

As already stated, in the pre-Spark era, big data typically used to build their ML models using statistical languages such as R, STATA, and SAS. However, this kind of workflow (that is, the execution flow of these ML algorithms) lacks efficiency, scalability, and throughput, as well as accuracy, with, of course, extended execution times.

Then, data engineers used to reimplement the same model in Java, for example, to deploy on Hadoop. Using Spark, the same ML model can be rebuilt, adopted, and deployed, making the whole workflow much more efficient, robust, and faster, allowing you to provide hands-on insight to increase the performance. Moreover, implementing...