Practical Predictive Analytics

Introduction to Spark Using R

Data! Data! Data! I can't make bricks without clay!

- Sir Arthur Conan Doyle

So far, we have learned how to perform analytics on what can be referred to as "small data". However, as the amount of data increases, so does the size and the problem of how to analyze the vast amounts of data that is produced arises. When that occurs, we begin to approach "big data" and new approaches to solving problems develop and sometimes, new tools are needed as well.

To some extent, nothing changes. You still want high quality data. You still want to be able to examine the relationships and cast the problem within a predictive analytic framework.

What does change are the steps needed to achieve that end, bearing in mind that ...