Large-Scale Data Analytics with Python and Spark A Hands-on Guide to Implementing Machine Learning Solutions

In this chapter we cover clustering and regression, looking at two traditional machine learning methods: k-means and linear regression. We briefly discuss how to implement these methods in a non-distributed manner first, to then carefully analyze the bottlenecks of these methods when manipulating big data. This enables us to design global-based solutions based on the DataFrame API of Spark. The key focus is on the principles for designing solutions effectively. Nevertheless, some of the challenges in this chapter are to investigate tools from Spark to speed up the processing even further. k-means is an example of an iterative algorithm, and how to exploit caching in Spark, and we analyze its implementation with both RDD and DataFrame APIs. For linear regression, we first implement the closed form, which involves numerous matrix multiplications and outer products, to simplify the processing in big data. Then, we look at gradient descent. These examples give us the opportunity to expand on the principles of designing a global solution, and also allow us to show how knowing the underlying platform, Spark in this case, well is essential to really maximize the performance.