Building A Prediction Engine

Richard Williamson explains how to build a prediction engine using technologies such as Spark, Kudu, Impala, and Kafka:

We’ll aim to predict the volume of events for the next 10 minutes using a streaming regression model, and compare those results to a traditional batch prediction method. This prediction could then be used to dynamically scale compute resources, or for other business optimization. I will start out by describing how you would do the prediction through traditional batch processing methods using both Apache Impala (incubating) and Apache Spark, and then finish by showing how to more dynamically predict usage by using Spark Streaming.

Of course, the starting point for any prediction is a freshly updated data feed for the historic volume for which I want to forecast future volume. In this case, I discovered that Meetup.com has a very nice data feed that can be used for demonstration purposes. You can read more about the API here, but all you need to know at this point is that it provides a steady stream of RSVP volume that we can use to predict future RSVP volume.

This is pretty dense, but it is a great look at one potential architecture leveraging Spark and several tools in the Hadoop ecosystem.

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