Our Databricks partnership came from delivery: production migration work proven on real enterprise workloads, not a logo exchange. When we recommend the platform, it is because we have run it under load. Since 2006 we have shipped production systems for enterprises in banking, healthcare, retail, energy and manufacturing, and that is why the platforms we hand back keep running.
Legacy ETL Migration
AI-assisted conversion of legacy ETL estates into Databricks pipelines, with generated tests and infrastructure as code proving every conversion.
Lakehouse Architecture and Modeling
Medallion-style foundations that give engineering, analytics and AI one governed platform instead of three competing copies of the data.
Data Engineering and Pipelines
Ingestion, transformation and orchestration built to run unattended, for batch and streaming workloads.
Production AI and ML
Models that serve answers in production, with the monitoring, retraining and data quality AI depends on.
Our Process
Databricks migrations follow a consistent structure at Smart Data. The phases are not rigid. They adjust to where your estate is starting from. But the sequence is deliberate, and the first phase exists so the later ones do not surprise anyone.
1–2 weeks • Low commitment • High clarity
We inventory the current estate: transformation logic, orchestration jobs, deployment configs, source systems, and the reporting that depends on all of it. Then we sort it: what automated conversion can carry, what needs senior hands, and what should be retired rather than moved.
The assessment ends with a phased plan and a written platform recommendation. Databricks is not always the answer, and if Fabric or a simpler path fits your estate better, that is what the plan will say.
Phase 2: Convert, Test and Parallel Run
4–8 weeks • Working solution • Measurable outcome
We convert the estate into Databricks pipelines on PySpark, with generated unit tests and infrastructure as code shipped alongside every converted pipeline. Senior engineers take what the tooling cannot. In a recent Fortune 100 proof of concept, the tooling covered 78 percent of the legacy transformation types in the estate in roughly three months.
Old and new pipelines run in parallel until output parity is validated. Reporting stays up throughout, and retirement of the legacy estate comes last rather than first.
Phase 3: Operate and Extend
Ongoing • Expand what works • Embed into operations
After cutover, most organizations have more domains to bring onto the lakehouse, streaming workloads to add, and AI use cases waiting on governed data. We help sequence that work so each phase lands something useful.
Your team runs the platform if that is the plan, and we build toward it from the first week: source control, deployment pipelines, and documentation your engineers can maintain. If you would rather we ran it, managed services is a real offering with named response commitments.
Our Services
Our Value
Why Organizations Choose Smart Data for Databricks
Verifiable Output, Not Vendor Promises
Every converted pipeline ships with generated tests and infrastructure as code. Your platform team checks the work rather than taking our word for it.
Twenty Years of Production Delivery
Two decades of enterprise software is why these platforms run in production, with tests, and not as a demo.
Multi-Platform Honesty
We build on Databricks, Fabric and Snowflake and show our scoring. The recommendation follows your estate, not our specialization.
Senior-Led, and We Stay If You Want
Senior engineers from discovery through delivery, and managed services with named response commitments afterwards.