The bundled JupyterLab now ships the Pipeline Exporter: turn a notebook into a running pipeline without ever leaving Jupyter. Export it as either an Ilum Spark job or an Airflow pipeline. You can also download it as a plain Python script. Ilum generates everything for you, schedules it, triggers the run and links you straight back into Ilum to watch it execute.
Export to an Ilum Spark job: turn your notebook into a one-shot job, a long-running service or a scheduled (cron) job that runs on Ilum's own Spark.
Export to Airflow: generate an Airflow pipeline straight from your notebook, with no DAG code to hand-write.
One-click run: trigger the generated pipeline and jump straight back into Ilum's History and Services views through clickable links.
Per-step retries: tag individual cells (retries:<n>) to control how many times each step retries: more for flaky I/O steps, none for parameter cells.
In the box: bundled into the standard Jupyter image. A fresh install has it ready, nothing extra to set up.
Why it matters: promoting an exploratory notebook to a scheduled pipeline normally means rewriting it by hand as pipeline code, which is exactly where logic drifts and bugs slip in. The Pipeline Exporter collapses that into a click and keeps the notebook as the single source of truth. Where to find it: open a notebook in the bundled JupyterLab and use the Pipeline Exporter panel. Pipeline Exporter embedded in JupyterLab Links:Pipeline Exporter Available in version: 6.7.2
