Reactive Big Data

Overview

Apache Spark and the
Typesafe Reactive Platform

At Typesafe, we’re committed to helping developers build massively scalable applications on the JVM. Because our users are increasingly building Reactive applications that leverage Big Data, we decided to team with Databricks to help developers understand how to better utilize Spark.

What is Spark?

Apache Spark is a distributed computing system written in Scala and leveraging Akka that was developed initially as a UC Berkeley research project for distributed data programming. It has grown in capabilities and recently became a top-level Apache project. Spark is replacing the venerable Hadoop MapReduce for several reasons including better performance, support for natural data processing idioms, and streaming operations.

Typesafe and Databricks: working together

Spark has quickly shifted from a research project to a production-ready library, thanks in large part to the dedication of the fine folks at Databricks.

Because Spark is implemented in Scala and leverages Akka, it presents a logically consistent extension to the Typesafe Reactive Platform. Your Spark-based data analytics can deploy and scale with your existing environment, without the need to deploy a separate cluster dedicated to data analysis. However, when your projects grow to the point where you need a dedicated data cluster, Spark will grow with you and still interoperate with the rest of your Akka- and Play-based applications.

Consequently, it made a lot of sense for us to partner more closely with Databricks and invest in resources and programs to show developers how they can build highly scalable and resilient applications with minimal effort using the Typesafe Reactive Platform and Spark.

Getting started with Typesafe and Spark

If you are ready to get started building Spark applications with the Typesafe Reactive Platform, here are a couple of resources we recommend:

Have questions? We’re here to help.