Monitoring Real-Time Uber Data Using Spark Machine Learning, Streaming, and the Kafka API (Part 2)

This post is the second part in a series where we will build a real-time example for analysis and monitoring of Uber car GPS trip data. If you have not already read the first part of this series, you should read that first. The first post discussed creating a machine learning model using Apache Spark's K-means algorithm to cluster Uber data based on location. This second post will discuss using the saved K-means model with streaming data to do real-time analysis of where and when Uber cars are clustered. The example data set is Uber trip data, which you can read more about in part 1 of this series.