A demo runs on a spreadsheet. A product runs on pipelines built to last.

With the foundation and one real domain, in parallel. The foundation is the part that's expensive to change later: the Unity Catalog metastore, a catalog-per-environment model, external locations and volumes, compute policies, identity, network and the CI/CD skeleton. The domain is one genuinely useful subject area taken from source to Gold with quality gates and a dashboard on the end. You get something in production in weeks rather than a foundation programme with value promised at the end - and the pattern that domain establishes is what every later wave copies.

No, and you shouldn't. We land your raw data first, rebuild the logic incrementally in declarative pipelines, and run old and new side by side until the numbers reconcile. Lakebridge's reconciler compares row counts, schemas and aggregates between source and target, so parallel run produces a signed-off difference report rather than an argument. You cut over domain by domain, on evidence - and only then does the old system get decommissioned.

Yes - that's usually the sane on-ramp. Lakehouse Federation lets Databricks query your existing databases and warehouses in place, governed by Unity Catalog, without copying the data first. You get one query surface across old and new from early on, then move workloads across when each is ready instead of holding everything until a single cutover date.

Both are first-class now. Unity Catalog manages Delta and Apache Iceberg tables, and reads foreign Iceberg tables managed elsewhere - so the format is a compatibility decision about what else needs to read your data, not a lock-in decision. Either way you get the same governance, and the same automated maintenance: predictive optimization handling OPTIMIZE, VACUUM and ANALYZE, and liquid clustering instead of hand-tuned partitioning. Our default is Unity Catalog managed tables, and we'll pick the format from your interoperability requirements rather than a preference.

There's a ladder, not a switch - and the honest answer is that most requirements stop on the second rung. Nightly batch, incremental micro-batch on a schedule or file-arrival trigger, continuous streaming through Structured Streaming or Zerobus Ingest for sub-five-second application events, and Real-Time Mode on declarative pipelines for millisecond end-to-end latency (in preview at the time of writing). The medallion architecture is the same at every rung; the trigger, the ingestion path and the cost change. We'll pick the cheapest tier that answers your question, and tell you what the next rung up would cost before you commit to it.

Unity Catalog gives you one permission model, column-level lineage and audit across every layer, plus row filters, column masks and attribute-based policies where the data needs them. Data stays in your cloud account and your storage. We work to an ISO/IEC 27001:2022 certified practice, and the governance layer is deep enough that it's a page of its own: the Governed Data Platform.

Mostly by designing for it, then measuring it. Serverless or right-sized job compute instead of long-lived all-purpose clusters; incremental materialized views instead of full rebuilds; predictive optimization and liquid clustering instead of hand-tuned maintenance jobs; and streaming reserved for the tables that genuinely need it. Then everything is tagged and attributed from the system tables with budget policies on top, so spend has an owner and a trend line before finance asks about it.

No. The same platform powers your BI, your reporting and your data science - AI is one consumer of it, not a precondition. Plenty of clients build the data foundation first and add agents later, or never. It stands on its own; it just happens to also be the only sane place to put agents when you get there.

Work with them, and hand over. We pair with your engineers through the build, leave the whole thing as reviewable code in your repo with runbooks, and taper as your team takes over. Some clients keep us on for on-call and a monthly cost-and-performance review; some don't need us after the second wave. Both are a good outcome.