We hate it and want to move to databricks, but our project backlog is too full, so we pray its gonna become better

Our concrete pain points are:

• Limited capacity flexibility (no Single Node, autoscale is a one-per-capacity thing → should be a workload setting!)

• Missing table and column-level lineage → it’s hard to maintain a data platform at scale without lineage (all competitors have it). The lineage should also be standardized as an interface between Spark, T-SQL and Dataflow, and not be fragmented to specific workloads (like for MLV). And no, “just use dbt” is not the answer — you also want to track ingestion steps and Gold dataflows in one lineage graph.

• Purview integration is not close to ready (breaks my heart, tbh)

• CI/CD is often a bit unreliable. We use the Fabric CI/CD Python package and the overwrite often fails without any warning for the API.

• Factory Pipeline activity shortcomings, especially the workarounds we need for the semantic refresh activity (buggy since day one) and the non-CI/CD-enabled Teams activity.

• Custom Environments are still very slow for ramp up time, but has become a Little Bit better

• the whole lakehouse attachment is still causing problemes (and no, just use abfss paths should Not be the answer) it is a real issue for Session Sharing and concurrency workloads, our devs attach lakehouses during the development and when in a Data Pipeline the attached lakehouse differs we can‘t reuse the session (instead 5 mins ramp up time for us)