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Databricks pipelines that hold up in production

Getting data into Databricks once is easy. Getting it in every night, from systems like Oracle and SAP, with history, deletes and late corrections handled correctly, is where most platforms struggle. We build ingestion and data products that your team can extend source by source.

Book an intro callWhat you get

When you need this

  • The platform runs, but each new source is a project of its own
  • You need change data capture from operational databases, not nightly full loads
  • Business logic lives in SAP or legacy SQL and has to be rebuilt on Databricks
  • Releases to production are manual, and nobody likes doing them

How we work

We agree on a small set of patterns first: how data lands in bronze, how history is kept in silver, and what makes a gold table a data product someone can rely on. Then we apply them to your highest-value sources, with Lakeflow Declarative Pipelines or plain Delta MERGE, depending on what fits. Everything ships through CI/CD with Declarative Automation Bundles and Azure DevOps, from dev to prod.

What we see go wrong

  • Watermarks that miss rows.

    Timestamp-based incremental loads lose changes from long-running transactions. CDC from the source log, or a carefully chosen watermark, avoids silent gaps.

  • Bronze kept forever, or not at all.

    Both are expensive: one in storage, the other when you need to reprocess. Decide retention per source.

  • SCD logic written ten times.

    Slowly changing dimensions belong in one tested pattern, not in every notebook.

  • Data products without owners or versions.

    A gold table that changes shape without warning breaks reports downstream. Releases, versions and a changelog fix that.

What you get

  • Ingestion patterns for your source types, documented and reusable
  • Production pipelines for your priority sources, with monitoring and alerting
  • A release process for data products, from dev through test to prod
  • CI/CD pipelines in Azure DevOps your team runs on its own

Other stages

  • 01 · Start

    Up and running in days, not months

    A proven deployment playbook and guidance on the setup decisions that are hard to undo later: workspaces, Unity Catalog, networking, identities.

    More about Start

  • 03 · Run

    Lean operations, continuous value

    Adopt new Databricks features with intent, fix data quality and pipeline issues fast, and keep the platform current without a big ops team.

    More about Run

Let's talk about your lakehouse.

Thirty minutes, no slides. Bring the question that keeps coming back.

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