%%configure not starting session with given environment id and environment name

When you want to run a PySpark notebook from a Fabric pipeline, you need to think of the environment as something that belongs to the notebook, not the pipeline. The pipeline doesn’t control Spark at all — it just triggers the notebook, which then starts Spark using whatever environment it’s already attached to.

For anything you care about running reliably, you should attach a published Fabric Environment to the notebook. That’s where you define the Spark runtime, Python libraries, and compute sizing so every run behaves the same whether it’s interactive or triggered by a pipeline.

Once the notebook is bound to an environment, pipeline runs become predictable. Spark always starts with the same configuration, libraries are guaranteed to be available, and you avoid the usual “it worked yesterday but not today” surprises.

This is also why you shouldn’t rely on notebook‑level tweaks for pipelines. Pipelines can reuse Spark sessions or initialise Spark before your notebook code runs, which means inline configuration can be skipped without warning.

That’s where %%configure fits in. You use it when you’re working interactively and just want to experiment — for example, testing memory settings, executor counts, or Spark SQL tuning while debugging performance.

What you don’t want to do is treat %%configure as a production mechanism. It’s session‑scoped, easy to miss, and not guaranteed to apply in pipeline runs, which makes it risky and hard to govern.

In short, if the configuration needs to be consistent, repeatable, and pipeline‑safe, you attach an environment to the notebook. If you’re just exploring or tuning things in an ad‑hoc session, that’s when %%configure makes sense.