Synapse, SSIS, ADF and T-SQL estates moved to the Lakehouse as code your team owns: Delta tables under Unity Catalog, Lakeflow pipelines and jobs, Databricks SQL under the same reports. Power BI stays exactly where it is.
Hardcoded SSIS packages, ADF and Synapse pipelines, T-SQL nobody dares to touch, a dedicated pool with a retirement date, and every Power BI report downstream of all of it.
Governed Delta tables under Unity Catalog, Lakeflow pipelines and jobs, Databricks SQL under the same reports, and all of it in Git, owned by your team.
Tables as Delta on your own cloud storage: ACID transactions, time travel, schema enforcement, and an open format any engine can read. Bronze, silver and gold are governed tables in the catalog, not folders in a lake, and the data never belongs to a service.
One catalog for tables, views, models and files: permissions, lineage and audit in one place, mapped from the roles the estate already has. Delta Sharing shares tables with partners and other platforms without copying them.
SQL warehouses, jobs and notebooks on serverless compute, started in seconds and billed by use. Nothing is left running because it is hard to start, and nothing is sized on a guess.
Managed, incremental ingestion from SQL Server and the business applications into governed tables, and files from ADLS Gen2 picked up as they land, once, with schema evolution handled. No pipeline written per source, nothing polled.
SQL Server, Synapse or Snowflake queried through Unity Catalog without copying them first, for what stays where it is during the move and after it. Linked servers and interim extracts have no reason to exist.
Pipelines declared rather than scripted: streaming tables, materialized views and data quality expectations that fail loudly. ADF pipelines, Synapse pipelines and SSIS packages land here as one pipeline where there was one per source, in Git.
Schedules, dependencies, retries and alerts for every pipeline, notebook and query, in one place. The triggers of ADF and the SQL Agent jobs of the estate become jobs your team can see and change.
The SQL endpoint the reports read from: serverless, governed by the catalog, accelerated by Photon. The semantic models point at a SQL warehouse instead of the pool; measures, reports and users stay as they are, and AI/BI Genie asks the same tables in plain language.
Every pipeline, job and catalog object as code in Git, deployed to development, test and production the same way as a Databricks Asset Bundle and reviewed like any other change. The repository is the platform.
Databricks’ own migration toolkit underneath: it inventories the estate, converts the T-SQL and reconciles source against target. Our accelerator runs on top of it, recovering the intent of every package and procedure before an engineer decides its target.
ADF pipelines, Synapse pipelines and SSIS packages are rebuilt as declarative pipelines with expectations and scheduled as jobs, one where there was one per source, all of it in Git.
Schemas are mapped, the history backfilled and reconciled, distribution redesigned for Delta; the pool is frozen and retired once the reports read the Lakehouse.
Converted with Lakebridge, every dialect difference source-linked and reviewed by a person; what needs code becomes a notebook run as a job.
Databases through managed connectors, files as they land in ADLS Gen2, incrementally and governed; what must stay is queried in place through Lakehouse Federation.
The semantic models are repointed to Databricks SQL. Measures, reports and their users are untouched; only the source under them changes.
No files or data at this stage