The last time that I created a playground for experimenting with machine learning using Apache Spark and an InterSystems data platform, see Machine Learning with Spark and Caché, I installed and configured everything directly on my laptop: Caché, Python, Apache Spark, Java, some Hadoop libraries, to name a few. It required some effort, but eventually it worked.

Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.

What would be the easiest or best way to filter the options presented by a filter control?

For example, suppose that I create a query using HoleFoods that uses a named filter to filter the product categories according to some criteria. When I display the query on the dashboard, I would like to provide a filter control that will allow users to further constrain the product categories. However, a simple filter control will display all the product categories available in the cube many of which are irrelevant to the current query.

Thanks for your help!