Fix ADF→Lakeflow classification + single-bundle packaging for multi-pipeline factories - #39
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September 1, 2026 12:17
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Rewrite internal package-proxy URLs (pypi-proxy.dev.databricks.com) to pypi.org / files.pythonhosted.org so public CI resolves deps. Same pinned versions and hashes; matches main. Co-authored-by: Isaac <no-reply@databricks.com>
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Closing — this was a port of another author's work and is being withdrawn from the public repo. |
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Fixes three flowx bugs surfaced while migrating 6 real Telefónica ADF/Synapse exports end-to-end through discover → convert → package → validate. With these changes, all 6 exports classify at 100% coverage
(0 unsupported) and package into a single deployable bundle each that passes
databricks bundle validatewith 0 errors.This branch already carries the fixes from PR the multi-pipeline report fix and PR the bridge-notebook header fix.
Changes:
AzureFunctionActivity, butAGENTIC_TYPESonly listedAzureFunction, so every Azure Function activity fell through tounsupported(no translation path) instead of agentic. Add"AzureFunctionActivity"toAGENTIC_TYPES.databricks.ymlbundle per pipeline. New_combine_into_single_bundle()folds every pipeline into one workflow tree (reusing the existinginner_workflowsmachinerywrite_bundlealready uses to emit a parent plus its children as separate job resources). Each pipeline keeps its own job name, parameters, and schedule.default; flowx omitted the field for ADF params that had none, sobundle deployfailed with "The argument 'default' is required.". Emitdefault: ""for params with no source default.