Apache Hadoop vs Jenkins

No, but it is legacy. Large on-premises HDFS estates still run and are still supported, and Spark and Flink still run on YARN. What has ended is Hadoop as a default choice for new platforms, which now start on object storage.

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.

Not a maintained one. CDH and HDP reached end of support and Cloudera's CDP is a paid subscription. The remaining free route is building and patching Apache releases yourself, which is a real engineering commitment.

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.

No. Spark runs standalone, on Kubernetes and on managed cloud services, and reads object storage directly. Many Spark deployments include Hadoop client libraries for the filesystem connectors without running a Hadoop cluster at all.

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.

Object storage, typically S3 or a compatible system, combined with an open table format such as Apache Iceberg or Delta Lake. Apache Ozone exists as an object store within the Hadoop ecosystem for organisations staying on-premises.

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.

It can be at multi-petabyte scale with steady, predictable utilisation, particularly where egress charges would be large. Include the staffing cost honestly, because the specialist operators a Hadoop cluster requires are scarce and therefore expensive.

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.