Apache Spark vs Jenkins

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

When the build needs something hosted runners cannot give you: physical hardware, an air-gapped network, a node-locked commercial tool licence, or an unusual platform. If none of those apply, hosted CI is usually less work to own.

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

Not in the open source distribution, which runs a single active controller. High availability and active-active controllers are features of CloudBees' commercial products. Open source deployments mitigate it with fast restores and, sometimes, multiple independent controllers.

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

This is the main operational risk. Many plugins have a single volunteer maintainer, and Jenkins publishes security advisories for unmaintained plugins where no fix exists. Auditing which plugins you depend on and who maintains them should be a periodic task, not a one-off.

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

For declarative pipelines you can go a long way without it. Anything involving shared libraries, conditional logic or custom steps is Groovy, and it runs in a sandboxed, transformed environment where standard Groovy idioms sometimes behave unexpectedly.

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

The software is free under the MIT licence. The cost is infrastructure and staff time to run controllers, agents and upgrades, plus a CloudBees subscription if you want high availability, support or centralised management of many controllers.