Warehouse Snapshots in Microsoft Fabric (Generally Available)
Managing data consistency during ETL has always been a challenge for our customers. Dashboards break, KPIs fluctuate, and compliance audits become painful when reporting hits ‘half-loaded’ data. With Warehouse Snapshots, Microsoft Fabric solves this by giving you a stable, read-only view of your warehouse at a specific point in time and now, this capability is Generally Available! Think of this as a true time travel database, an industry-first capability that sets us apart.
Why Warehouse Snapshots Matter
- Guarantee reporting consistency during ETL or data changes.
- Enable audit and compliance workflows.
- Support reproducible analytics and ML training.
- Roll forward snapshots atomically without breaking BI connections.
Key Scenarios Enabled using Warehouse Snapshots
- Stable Reporting During ETL: Keep dashboards consistent while pipelines run, free from disruptions caused by data modifications.
- Historical Analysis: Users can schedule snapshots hourly, daily, or weekly to align with their business needs. This is especially valuable when the source dataset does not maintain historical changes. You can compare and analyze differences between two specific points in time. For instance, you can compare the product inventory quantity from 20 days ago with that from 5 days ago.
- Financial Close: Lock KPIs for month/quarter-end without blocking operations.
- Data Audit: Auditing data changes is crucial for data compliance and understanding how data has evolved over time. Warehouse Snapshots empowers you to track changes, access different versions of updates, and perform data analysis at any desired point in time
- Day 0 (Close Date): Finance triggers a snapshot named
MonthEnd_Sept2025. - Day 1–30: Operations continue adding new sales and expenses.
- Audit: Finance queries
SELECT * FROM MonthEnd_Sept2025.Salesto validate KPIs without worrying about post-close changes. The data retention is for 30 days from the current date.
- Day 0 (Close Date): Finance triggers a snapshot named
- Repairing Accidental Data Changes & Incident Recovery: Warehouse Snapshots feature is invaluable for rectifying individual records to their last known good state, making it efficient to perform repairs without resorting to backups and restores. Once you access the desired data as it existed in the last known good state, you can either update the source warehouse with that data or ingest the records into a new object.
- Data Science & ML: Train models on reproducible datasets tied to snapshots. Snapshots capture the warehouse state at a specific point in time. Data scientists can train models on this frozen dataset, ensuring reproducibility during that window. While the live warehouse continues to ingest new data, snapshots allow ML teams to work on stable datasets without blocking operational pipelines or risking data changes. Experiments can be reproduced within the 30-day retention period. For longer-term reproducibility, teams can export snapshot data to external storage.
See Warehouse Snapshots in Action
What’s New in GA vs Preview
| Area | Preview | General Availability |
| Portal Improvements – Update Snapshot | ONLY possible using TSQL and from context menu for the warehouse snapshot item in the workspace | Capability available through portal – ‘Manage Warehouse Snapshot’ tab |
| SSMS Object Explorer | Warehouse Snapshots do not appear in SSMS Object Explorer although it is visible in the database selection dropdown | SSMS 22 Preview 3 provides direct visibility and query access to snapshots |
| CREATE | Warehouse Snapshots can only be created against new warehouses created after March 2025 | Warehouse Snapshots can be created against any existing warehouses |
Call to Action
Start using Warehouse Snapshots today to deliver consistent, compliant, and disruption-free analytics. Your insights matter to us. Please visit the feedback form to share your experience with Warehouse Snapshots.
