The examples are all in Rust, so it’s very hard to make a non-toy demo. Usually if one uses the RDD API, one has some sort of library code that’s already in Java or Python and it’s impractical to port that code to make the job run. Or more likely, somebody will write an initial version using that code and then port / optimize the job later.

Native dependencies usually mean you’ll need docker. Spark pre-dates docker, and just relatively recently added the Kubernetes runner, which makes dockerized jobs easy. But historically it hasn’t been easy to run a job in a containerized environment with the native dependencies you need. You can ship native deps with your job, but that’s not easy, especially if you need a rebuild with each job.

The main advantage of Spark is flexibility and interoperability. You save time by not having to write something optimized on day 1 (for something you might throw away). And you get SQL support, something Beam / Hadoop don’t have (certainly not for Python). There are lots of benchmarks where Spark SQL is not a winner, but the point is Spark will help you save development time.