The data, which is readily available using different data streams, needs to be parsed and converted to a meaningful format, which essentially creates value for the business. Hbase integration with Mahout provides the stream that allows us to do clustering in machine learning, which is a way of programmatically orchestrating similar sets of data in a more organized and meaningful way.

Let's say that you have a fruit tasting session where you have different types of fruits, and you want to group people who liked apples, bananas, watermelons, and so on. A small set of data will look staggered, so we have to create some sort of algorithm to do it programmatically. Here is how the data will look before clustering and after clustering. In this section, we can access the underlying data using direct invocation from the Java client.

This can be done in analysis for the purpose of exposing the data in various forms to the data scientist, data analysis...