So, back when we mentioned Spark SQL, remember I said DataFrames are kind of the way of the future with Spark and it's going to be tying together different components of Spark? Well, that applies to MLlib as well. There's a new DataFrame-based API in Spark 2.0 for MLlib, which is the preferred API going forward. The one that we just mentioned is still there if you want to keep using RDDs, but if you want to use DataFrames instead, you can do that too, and that opens up some interesting possibilities. Using DataFrames means you can import structured data from a database or JSON file or even a streaming source, and actually execute machine learning algorithms on that as it comes in. It's a way to actually do machine learning on a cluster using structured data from a database.

We'll look at an example of doing that with linear regression, and just to refresh you, if you're not familiar with linear regression, all that is fitting a line to a bunch of data. So imagine...