What is the difference between Fabric SQL Database and Azure SQL Database?

Both are fully-managed Azure SQL Database engines. The core differences: Fabric SQL Database is provisioned inside a Fabric workspace and consumes CU from Fabric capacity; Azure SQL Database is provisioned in an Azure subscription with dedicated DTU or vCore compute. Fabric SQL DB automatically mirrors data to OneLake as Delta Parquet for zero-ETL analytics; Azure SQL DB does not. Fabric SQL DB provisions in 45-60 seconds; Azure SQL DB takes 3-5 minutes. Azure SQL DB has SQL Server Agent, Service Broker, and native Replication; Fabric SQL DB does not (yet).

When should I choose Fabric SQL Database over Azure SQL Database?

Choose Fabric SQL Database when: your app is Fabric-first, you want zero-ETL data replication to OneLake for BI consumption, you value 45-60 second provisioning, you already have Fabric F-SKU capacity with idle headroom (near-zero incremental cost), or your team is more Fabric-fluent than Azure-fluent. Skip Fabric SQL DB if you need SQL Server Agent, Service Broker, native Replication topologies, or FedRAMP High authorization.

Does Fabric SQL Database support SQL Server Agent?

Not in GA. SQL Server Agent equivalents are on the Fabric SQL Database roadmap but no firm date has been announced. If your workload depends on Agent jobs today (backup schedules, ETL orchestration, custom job history), stay on Azure SQL Database or move Agent workloads to Fabric Data Factory pipelines before migrating.

How much does Fabric SQL Database cost vs Azure SQL Database?

A representative 200GB OLTP workload with 60% idle time costs approximately $845/month on Fabric SQL Database (share of F32 CU + auto-pause during idle + OneLake storage) versus $2,456/month on Azure SQL Database S6. Fabric SQL DB benefits enormously from auto-pause and from riding on existing Fabric capacity headroom. Constantly-active workloads narrow the cost gap.

Can I migrate from Azure SQL Database to Fabric SQL Database?

Yes. Export schema via SqlPackage DACPAC, import into Fabric SQL Database, copy data via Fabric Data Factory or Import Data Wizard, update connection strings to the workspace-scoped Fabric endpoint. Typically 2-6 weeks per database. Feature-gap workloads (SQL Server Agent, Service Broker, Replication) are the main migration blockers.

Does Fabric SQL Database automatically replicate data for analytics?

Yes. Every table you create in Fabric SQL Database is automatically mirrored as Delta Parquet to a hidden _mirrored folder in the workspace's OneLake, with 5-15 second replication latency. The mirrored tables are queryable by Fabric Warehouse, Lakehouse SQL endpoint, Power BI Direct Lake+ semantic models, and Fabric notebooks — with no ETL pipelines. Azure SQL Database does not offer automatic OneLake replication; you would need to configure mirroring or use Fabric Data Factory pipelines to move data.

Is Fabric SQL Database FedRAMP authorized?

Fabric SQL Database inherits Fabric's compliance posture: SOC 2 Type II, HIPAA BAA, ISO 27001, and FedRAMP Moderate authorization. FedRAMP High for Fabric is on the roadmap but not yet GA. If your target authorization is FedRAMP High, choose Azure SQL Database (which is FedRAMP High authorized) until Fabric SQL DB matures further.

How is a Power BI engagement priced for enterprise workloads?

Pricing is modeled against three variables: the complexity of the semantic layer, the volume and velocity of source data, and the governance footprint required after go-live. A scoped implementation typically runs as a fixed-fee discovery sprint followed by a time-and-materials build, because dataset refresh patterns and row-level security rules almost always evolve once real user personas review early drafts. Licensing is separated from consulting fees and mapped against Power BI Pro, Premium Per User, and Fabric capacity SKUs so finance teams can plan capacity uplift independently of delivery cost. A typical mid-market rollout lands between 120 and 480 consulting hours across data modeling, DAX optimization, report design, and deployment pipelines. Fabric workloads add capacity sizing sessions to avoid over-provisioning F-SKUs on day one.

How long does a production-ready Power BI rollout take?

A single subject-area workspace with a conformed star schema, deployment pipeline, and row-level security ships in roughly six to eight weeks once source access is granted. Multi-domain rollouts that span finance, operations, and customer analytics typically run three to five months because the semantic model has to reconcile calendar, product, and organizational hierarchies that rarely align across source systems. Fabric lakehouse projects add four to six weeks for medallion design, Direct Lake tuning, and OneLake shortcut setup. Timelines compress when an authoritative data dictionary already exists and lengthen when stakeholders are discovering definitions (for example, what counts as "active customer") for the first time. Governance, training, and center-of-excellence enablement are run in parallel rather than sequentially.

What delivery methodology do you use for Power BI and Fabric projects?

Delivery runs on a five-stage pattern: Discover, Model, Visualize, Operationalize, and Enable. Discover captures questions stakeholders actually want answered, not just source tables. Model builds a Kimball-style star schema (or a medallion lakehouse in Fabric) with conformed dimensions and documented grains. Visualize produces reports against a shared theme file, applies accessibility tokens, and uses bookmarks and field parameters instead of bespoke page duplication. Operationalize wires up deployment pipelines, dataset refresh monitoring, and Azure DevOps or GitHub source control via TMDL. Enable delivers hands-on training for citizen developers and a lightweight center-of-excellence charter. Each stage ends with a demo and a written acceptance checklist, so scope creep is visible before it becomes rework.

How do you secure sensitive data inside Power BI reports?

Security is layered rather than relying on a single control. Row-level security and object-level security are enforced inside the semantic model using DAX filter expressions driven by Entra ID group membership, so filters survive refresh and cannot be bypassed by report-level tricks. Sensitivity labels from Microsoft Purview are applied at dataset and report scope and follow exports into Excel and PDF. Workspace roles follow a least-privilege pattern (Admin, Member, Contributor, Viewer) and are granted to Entra ID groups, never individuals. Private endpoints and VNet data gateways keep on-premises sources off the public internet, and tenant-level settings restrict publish-to-web, external sharing, and guest access. Every production dataset is audited quarterly against a written RLS test plan.

Can existing Excel and Tableau reports be migrated into Power BI?

Yes, but a migration is an opportunity to redesign rather than a literal port. Excel financial models usually translate cleanly into a semantic model: named ranges become dimensions, pivot tables become matrix visuals, and SUMIFS chains become well-formed DAX measures with variables. Tableau workbooks require more interpretation because Tableau extracts and Power BI datasets have different refresh and relationship semantics; calculated fields are rewritten as DAX, LOD expressions become CALCULATE patterns, and dashboard actions are rebuilt using bookmarks and drillthrough. A discovery audit catalogs every report, ranks them by business value, and retires the long tail of duplicates before migration starts. This typically reduces the report estate by 30–50 percent.

How do you optimize DAX performance on large semantic models?

DAX performance starts with the model, not the measure. Star schemas with integer surrogate keys, single-direction relationships, and calculated columns materialized in Power Query outperform ambiguous snowflakes every time. Measures are written using variables (VAR/RETURN) to avoid repeated context transitions, and expensive patterns like FILTER over entire tables are replaced with KEEPFILTERS and Boolean filter arguments. Aggregation tables and composite models offload detail-level queries from imported caches to Direct Lake or DirectQuery sources. Every measure that appears in a production report is profiled with DAX Studio and VertiPaq Analyzer; queries above a 2-second threshold on representative hardware are rewritten before release. Fabric capacity metrics are reviewed weekly to catch runaway refreshes and interactive CPU spikes.

Do you support Microsoft Fabric, OneLake, and Direct Lake workloads?

Fabric is now the default landing zone for new analytics workloads unless a client has a specific reason to stay on legacy Power BI Premium capacity. Engagements cover medallion architecture design in lakehouses, data engineering pipelines built in Fabric notebooks or Dataflows Gen2, shortcut-based integration with Azure Data Lake Storage and Amazon S3 through OneLake, and Direct Lake semantic models that skip import refresh entirely. Capacity sizing is based on measured workload patterns rather than vendor rules of thumb, and F-SKU scaling is automated through Azure Logic Apps or the Fabric REST API so off-hours costs stay predictable. Copilot in Fabric is configured with tenant-level data boundaries and audit logging before it is enabled for end users.

What training and enablement do end users receive after go-live?

Training is tiered by persona. Report consumers get a 45-minute guided tour covering filters, bookmarks, subscriptions, and mobile access. Analysts get a three-session workshop on self-service semantic model extensions, composite models, and publishing to workspaces that follow the governance pattern. Data modelers receive a two-day immersion on star schema design, DAX fundamentals, and deployment pipeline usage. Every session is recorded, indexed, and paired with a sandbox workspace seeded with representative sample data. A written center-of-excellence charter defines who owns certified datasets, who can promote a report to production, and how to request enhancements. Office hours run for 30 days post-launch so questions do not pile up into a formal change request.

How are Power BI deployments managed across dev, test, and production?

Every production workspace is paired with matching development and test workspaces wired through Power BI deployment pipelines. Datasets are source-controlled as TMDL files in Azure DevOps or GitHub so pull requests can be reviewed by a second modeler before merge. Environment-specific parameters (connection strings, sensitivity label rules, capacity assignments) are swapped at deployment time using parameter rules rather than manual edits. Fabric deployment pipelines handle lakehouses, notebooks, and data pipelines with the same promotion pattern. Refresh schedules, gateway assignments, and alerting are applied through the Power BI REST API so they survive redeployment. A written change-management checklist covers dataset certification, dependency impact analysis, and rollback procedure for every promotion to production.

Which industries and data sources do you support most often?

Heaviest experience sits in healthcare, financial services, energy, manufacturing, and public sector, because each of those verticals pushes a different dimension of Power BI: HIPAA-bound PHI handling, transaction-grain reconciliation, time-series sensor data, cost-center-driven manufacturing variance, and FedRAMP-aligned government reporting. Source systems frequently include Microsoft Dynamics 365, SAP ECC and S/4HANA, Salesforce, Workday, Epic and Cerner (Oracle Health), Infor, and a long tail of legacy ODBC databases connected through on-premises data gateways. Fabric engagements add Azure Data Lake Storage, Snowflake, Databricks, BigQuery, and Amazon S3 through OneLake shortcuts. Regardless of source, the modeling discipline is identical: conformed dimensions, documented grains, and a semantic layer that hides the join complexity from report authors.

Fabric SQL Database vs Azure SQL Database: 2026

The Fabric SQL Database GA at Microsoft Build 2026 raised a decision every Azure architect now has to make: which SQL Database service should host your new OLTP workload? Both are Azure SQL Database engines. Both offer serverless auto-pause. Both give millisecond OLTP latency. The differences are in provisioning, billing, integration surface, and long-tail feature parity. This guide gives you the decision tree with concrete criteria.

The 30-Second Decision

Choose Fabric SQL Database if: your app lives inside a Fabric-first analytics environment, you want zero-ETL replication to OneLake for analytics, or you value under-60-second provisioning inside the Fabric workspace.
Choose Azure SQL Database if: your app is Azure-native (App Service, Azure Functions), you need SQL Server Agent / Service Broker / Replication topologies, or your team is more Azure-first than Fabric-first.
Choose both: many enterprises run Fabric SQL DB for analytics-adjacent OLTP workloads and Azure SQL DB for line-of-business apps. The two can peer through connection strings and OneLake shortcuts.

Feature Parity Matrix

Feature	Fabric SQL Database	Azure SQL Database
SQL Server engine version	Latest	Latest
T-SQL surface	Full (parity with Azure SQL DB)	Full
Transactions	ACID	ACID
Stored procedures	Yes	Yes
Triggers	Yes	Yes
SQL Server Agent	No (roadmap)	Yes
Service Broker	No	Yes
Native Replication topologies	No	Yes
Cross-database queries	Within workspace	Cross-DB via linked server
Point-in-time restore	Yes (7 days included)	Yes (7-35 days)
Geo-restore	Yes (same region)	Yes (paired region)
Auto-pause	Yes	Yes (serverless tier only)
Cold-start latency	8-12 seconds	30-60 seconds
Provisioning time	45-60 seconds	3-5 minutes
Automatic OneLake replication	Yes (Delta Parquet in _mirrored)	No (requires ADF or mirroring config)
Entra ID auth	Yes (default)	Yes
SQL authentication	Optional	Yes
Managed identity for outbound	Yes	Yes
Row-level security	Yes	Yes
Always Encrypted	Yes	Yes
Dynamic data masking	Yes	Yes
Private endpoint	Yes (Fabric managed)	Yes
Failover groups (multi-region HA)	Yes (roadmap for cross-region)	Yes

The feature gaps that matter most: SQL Server Agent, Service Broker, and native Replication topologies are not in Fabric SQL DB GA. Roadmap has SQL Server Agent equivalents but no firm date. If any of these features are on your critical path today, stay on Azure SQL DB.

Cost Comparison at Equivalent Sizing

Comparing an equivalent OLTP workload of approximately 200 GB, 500 QPS at peak, ~40 QPS at average, on both platforms:

Metric	Fabric SQL DB (F32 shared)	Azure SQL DB (S6, 400 DTU)
Base monthly cost	~$2,100 (share of F32 CU)	$2,410 (S6 DTU)
Compute during idle	$0 (auto-pause)	$2,410 (constant DTU)
Realistic monthly cost (60% idle)	~$840	$2,410
Storage 200GB	$4.60 (OneLake)	$46 (Premium storage)
Backup	Included	Included
Total realistic month	~$845	$2,456

Note on Fabric costing: Fabric SQL DB doesn't have a dedicated SKU — it consumes CU from your existing Fabric capacity. The $2,100 above is the *equivalent share of an F32*. If you already have an F64 for Power BI reporting, adding a Fabric SQL DB uses the existing headroom and the incremental cost is near zero. This is the biggest cost advantage.

Note on Azure SQL DB costing: DTU pricing is fixed for the tier — the S6 costs the same whether idle or peaked. Serverless tier can auto-pause, but the compute cost during active periods is still full-tier. Total monthly cost ends up much closer to Fabric SQL DB when the app is highly utilized.

Integration Surface

Fabric SQL Database integrates natively with: - Fabric Warehouse and Lakehouse (via OneLake replication) - Power BI Direct Lake+ semantic models - Fabric Data Factory pipelines - Fabric notebooks (Spark SQL, PySpark) - Microsoft Purview (via Fabric admin) - Fabric Data Agent API (for AI question-answering)

Azure SQL Database integrates natively with: - Azure App Service, Azure Functions, AKS - Azure Logic Apps and Power Automate - Azure Data Factory pipelines - Azure Synapse (via linked services) - Azure Machine Learning - Every third-party BI tool via ODBC/JDBC drivers

Both integrate with: - Any client speaking TDS (SSMS, Azure Data Studio, .NET SqlClient, JDBC, ODBC, Python pyodbc) - Entra ID authentication - Third-party ORM libraries (Entity Framework, Hibernate, SQLAlchemy)

The integration surface question really is: is your app Fabric-first or Azure-first? Very few teams need both integration surfaces equally.

Regulated Industries: Which Clears Audit Faster?

For healthcare (HIPAA), financial services (SOC 2, SOX), and government (FedRAMP) contexts, the maturity gap matters:

Azure SQL Database has been GA since 2010, has FedRAMP High authorization, SOC 2 Type II, HIPAA BAA, ISO 27001, and PCI DSS. It clears any enterprise procurement.
Fabric SQL Database shares Fabric's compliance posture — Fabric has SOC 2 Type II, HIPAA BAA, ISO 27001, and FedRAMP Moderate. FedRAMP High for Fabric is on the roadmap but not yet GA.

If your target is FedRAMP High or your procurement team is conservative about newer services, choose Azure SQL Database until Fabric SQL DB matures further. If your target is SOC 2 or HIPAA, either is fine.

Migration Paths in Both Directions

Azure SQL Database → Fabric SQL Database: 1. Export schema via SqlPackage as DACPAC. 2. Import DACPAC into Fabric SQL Database. 3. Copy data via Data Factory, Fabric Data Factory pipeline, or Import Data Wizard. 4. Update connection strings to `{workspace-id}.database.fabric.microsoft.com`. 5. 30-60 day parallel run, then decommission Azure SQL DB.

Typically 2-6 weeks per database depending on complexity. Feature gaps (Agent jobs, Service Broker) are the main blockers — if you use them, stay on Azure SQL DB.

Fabric SQL Database → Azure SQL Database: Same steps in reverse. Fabric SQL DB's DACPAC exports are Azure SQL DB compatible.

Both directions: schema migrations are typically 1:1. Application code changes are limited to connection strings.

Decision Tree

Answer these five questions in order. First "yes" wins.

Does your workload require SQL Server Agent, Service Broker, or native Replication? → Yes: Azure SQL Database.
Does your workload require FedRAMP High authorization? → Yes: Azure SQL Database.
Is your app hosted in Azure (App Service, Functions, AKS) with no Fabric integration in scope? → Yes: Azure SQL Database.
Do you need the operational data to be queryable by Fabric Warehouse or Power BI Direct Lake+ with zero ETL? → Yes: Fabric SQL Database.
Do you already have Fabric F-SKU capacity with idle headroom? → Yes: Fabric SQL Database (nearly free incremental cost).

If none of the above apply, either works. Default to Fabric SQL Database for new greenfield analytics-adjacent OLTP workloads.

Production Readiness Checklist (Both)

[ ] Backup and point-in-time restore tested against a real backup.
[ ] Entra ID authentication configured for both users and service principals.
[ ] Row-level security policies deployed and tested.
[ ] Auto-pause interval tuned to workload (typically 4-8 hours for prod).
[ ] Monitoring configured — Log Analytics or Fabric warehouse for query performance metrics.
[ ] Disaster recovery plan documented and tested (geo-restore for Fabric SQL DB, failover group for Azure SQL DB).
[ ] Connection strings use managed identity or Entra ID service principal, not SQL authentication.
[ ] Private endpoint enabled if the workload is not internet-facing.

Related Guides

Ready to pick the right SQL Database service for your workload? Book a 30-minute strategy call and we will run your specific requirements through the decision tree.