Migration toMigration toFabric.Fabric.

One logical lake for the whole tenant. A table is stored once, in the open Delta Parquet format, and every workload reads that same copy: no extracts between Warehouse, Lakehouse and Power BI, and no second bill for the same data.

• One copy
• Delta Parquet
• Every workload

Delta tables and files under one item, with Spark notebooks on top for the heavy transformations. Every table gets a SQL analytics endpoint the moment it lands, so T-SQL readers never wait for anyone.

• Tables and files
• Spark notebooks
• SQL analytics endpoint

A full T-SQL warehouse on OneLake with transactions, stored procedures and views. Dedicated pool schemas and the procedures written against them land here largely as they are, the dialect differences reviewed rather than the logic rewritten.

• T-SQL
• Transactions
• Procedures kept

A snapshot of a database, then every change, replicated into OneLake in near real time as Delta tables, through the on-premises data gateway when the source is on-premises. SQL Server is mirrored, not extracted on a schedule.

• SQL Server
• Azure SQL
• Near real time

A reference to data that stays where it is, read by OneLake as if it were there: ADLS Gen2 folders, S3 buckets, Unity Catalog tables. One copy of the files, with OneLake’s security on top and no second pipeline.

• ADLS Gen2
• Unity Catalog
• Zero copy

Data Factory in Fabric: activities, loops, triggers and the copy activity, built in the same workspace as the data and scheduled there. ADF pipelines, Synapse pipelines and SSIS packages land here as pipelines your team can open and change.

• Data Factory in Fabric
• Orchestration
• Copy activity

Power Query transformations that write straight into a Lakehouse or a Warehouse, for the loads that were shaped in Power Query or in SSIS data flows. Low code where low code was the original.

• Power Query
• Low code
• Writes to OneLake

The semantic models read the Delta tables directly: no import refresh window and no DirectQuery latency, a metadata-only framing instead. The models keep their measures and the reports keep their users.

• Direct Lake
• Semantic models
• Reports unchanged

Permissions defined on the data once and enforced by every engine that reads it, row and column level included. Sensitivity labels that travel with the data, lineage across items, and the OneLake catalog to find and endorse what exists.

• Row and column level
• Sensitivity labels
• Lineage

Every item source-controlled in Git and promoted from development to test to production through deployment pipelines, reviewed like any other code. One capacity runs every workload and is measured in the Capacity Metrics app.

• Git
• Dev · Test · Prod
• F SKUs

ADF pipelines and Synapse pipelines map activity for activity; SSIS packages are rebuilt as pipelines and Dataflow Gen2 with their schedules kept. All of it lives in the workspace, in Git.

Schemas, tables and stored procedures move to the Warehouse with their T-SQL kept; the history is backfilled and reconciled before the pool is frozen and retired.

The operational databases replicate into OneLake through the on-premises gateway, a snapshot and then every change, readable in Fabric from the first day.

Files that live in ADLS Gen2 and tables that live in Unity Catalog stay where they are and are reached from OneLake as shortcuts, governed once.

The semantic models switch storage mode from import to Direct Lake. Measures, reports and users are untouched; the nightly refresh window is gone.