Serverless Scala on Databricks: Running Apache Spark™ JARs Without Managing Clusters

Scala remains a critical language for some of the largest and most performance-sensitive Apache Spark™ workloads. But running Scala pipelines shouldn’t require managing clusters, dependencies and runtime configuration by hand.This lightning talk introduces Serverless JARs on Databricks, now in Public Preview and heading to GA. We’ll show how teams run existing Scala Spark applications on serverless compute, what changes operationally and what stays the same.This lightning talk will cover:

• When serverless execution makes sense for Scala Spark workloads
• Best practices for packaging and running Scala JARs
• Common migration pitfalls and how to avoid them

The goal is to help Scala teams move to serverless execution without rewriting code or giving up performance and control.