Most Spark developers learn to write data with df.write long before they ever encounter df.writeTo. It is simple, familiar, and everywhere: choose a format, pick a mode, add a few options, and save the result to a table or path. For years, that mental model worked well enough. Spark was often writing files first and tables second.
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Schema inference is convenient. In production or benchmarking, it is often a silent performance killer.
I’m guilty. I’ve peddled the #NotebookEverything tagline more than a few times.
Coming from a notebook-first Spark background, I wanted to write the introduction to Spark Job Definitions (SJDs) that I wish I had when I first encountered them. If you are first interest in why you might want to use a Spark Job Definition over a Notebook, see my blog here.
I’m excited to formally announce LakeBench, now in version v0.3, the first Python-based multi-modal benchmarking library that supports multiple data processing engines on multiple benchmarks. You can find it on GitHub and PyPi.