Sightseeing, venues, and friends: Predictive analytics with Spark ML and Cassandra

Which venues have similar visiting patterns? How can we detect when a user is on vacation? Can we predict which venues will be favorited by users by examining their friends' preferences? Natalino Busa explains how these predictive analytics tasks can be accomplished by using Spark SQL, Spark ML, and just a few lines of Scala code.

Which venues have similar visiting patterns? How can we detect when a user is on vacation? Can we predict which venues will be favorited by users by examining their friends’ preferences? Natalino Busa explains how these predictive analytics tasks can be accomplished by using Spark SQL, Spark ML, and just a few lines of Scala code. Natalino presents a collection of machine-learning techniques to extract insights from location-based social networks such as Facebook, demonstrating how to combine a dataset of venues’ check-ins with the user social graph using Spark and how to use Cassandra as a storage layer for both events and models before sketching how to operationalize such predictive models and embed them as microservices.