Machine learning is increasingly becoming a part of people’s business platforms. In order to make full use of machine learning in our business platforms, we will need a tool with similar characteristics to our database tools. It needs to be distributed and scale-able, and integrate near seamlessly with our data store. Luckily Spark is a great tool for this purpose. In this post and future ones, we will learn about how to set up an environment for performing machine learning using Apache Spark and Cassandra, and also learning more about machine learning in general.

Modern business platforms require modern business solutions. Technologies such as Spark, Akka, Cassandra, Kafka, and Alpakka are several tools that we have identified as platforms game changers in the world today.

Real-time data processing is the current state-of-the-art in business platform data engineering practices. Long gone are the days of batch processing and monolithic ETL engines that are turned on at midnight. Today’s demands come from the number of mobile users and things on the internet. Mobile phones and tablets have steadily increased in the realm of customer experience as companies create mobile only or mobile-first interfaces to interact with their commercial systems or business processes. Similarly, there are other “things” in the “Internet of Things” (IoT) such as rental bikes and scooters, key-less home locks, as smart home thermostats.

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This resource for monitoring Datastax, Cassandra, Spark, & Solr performance is just the first iteration of a longer initiative to create the best knowledge base on these real-time data platform technologies such as DataStax Enterprise (Cassandra, Spark, and Solr) as well as for Kafka, Docker, and Kubernetes. Our firm, Anant, has been working with Solr/Lucene for several years, and then over the years picked up Spark and Cassandra, and then made the logical move to become experts at and partners with Datastax.

Datastax OpsCenter is good but we’re wise enough to say, however, that it is just the beginning of the toolset needed to really understand what is happening under the hood in the component technologies that comprise of the Datastax Enterprise Platform. When monitoring to scale complex systems such as business platforms you need to review all signals, not just those that come from the database.

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