Power BI Embedded: Supported Data Sources
Power BI Embedded supports a comprehensive range of data sources that enable ISVs and enterprises to build rich, interactive analytics experiences within custom applications. Understanding which data sources are supported in Import, DirectQuery, and live connection modes is critical for designing embedded analytics architectures that meet performance, security, and data freshness requirements.
Microsoft Data Sources
Microsoft data sources receive first-class support in Power BI Embedded with the deepest integration, best performance, and most complete feature compatibility across all connection modes.
- Azure SQL Database – Import and DirectQuery. The most common cloud data source for Power BI Embedded with full query folding and gateway-free connectivity.
- Azure Synapse Analytics – Import and DirectQuery. Ideal for large-scale data warehouse scenarios with serverless or dedicated SQL pools.
- Azure Analysis Services – Live connection. Enables centralized semantic models shared across multiple embedded reports.
- SQL Server – Import and DirectQuery. Requires an on-premises data gateway for cloud-to-on-premises connectivity.
- SQL Server Analysis Services (SSAS) – Live connection via on-premises data gateway. Supports both Tabular and Multidimensional models.
- Azure Data Lake Storage Gen2 – Import mode. Connect to Parquet, CSV, and Delta Lake files stored in ADLS Gen2.
- Microsoft Dataverse – Import and DirectQuery. Native integration with Power Platform and Dynamics 365 data.
- Azure Databricks – Import and DirectQuery via the Databricks SQL connector.
- SharePoint Online Lists – Import mode. Useful for embedding reports that visualize SharePoint-managed data.
- Excel workbooks – Import mode. Connect to Excel files in OneDrive for Business or SharePoint document libraries.
Third-Party Database Sources
Power BI Embedded supports connections to major third-party databases, enabling organizations to embed analytics over their existing data infrastructure without migration.
- Oracle Database – Import and DirectQuery. Requires Oracle client libraries and on-premises data gateway for on-premises instances.
- PostgreSQL – Import and DirectQuery. Popular for open-source data stacks and cloud-native architectures (Azure, AWS RDS, Google Cloud SQL).
- MySQL – Import and DirectQuery. Widely used in web applications and SaaS platforms.
- Amazon Redshift – Import and DirectQuery. Enables multi-cloud analytics by connecting Power BI Embedded to AWS data warehouses.
- Google BigQuery – Import and DirectQuery. Supports cross-cloud scenarios where data resides in Google Cloud.
- Snowflake – Import and DirectQuery. Cloud-agnostic data warehouse popular in modern data stack architectures.
- SAP HANA – Import and DirectQuery. Enables analytics over SAP enterprise data without extracting to a separate data warehouse.
- Teradata – Import and DirectQuery. Supports legacy enterprise data warehouse environments.
Cloud Services and API Sources
Power BI Embedded can connect to SaaS applications and REST APIs, enabling embedded analytics over operational data from CRM, ERP, marketing, and financial platforms.
- Salesforce – Import mode via the native Salesforce connector. Pulls objects, reports, and custom data.
- Dynamics 365 – Import and DirectQuery via Dataverse connector. Full access to CRM, ERP, and HR modules.
- Google Analytics – Import mode. Visualize website and marketing analytics alongside business data.
- REST APIs / OData – Import mode via Web connector or OData feed connector. Enables integration with any API-accessible data source.
- Azure Blob Storage – Import mode. Connect to CSV, JSON, and Parquet files in blob containers.
- Power BI dataflows – Import mode. Use Power BI dataflows as a reusable data preparation layer between raw sources and embedded reports.
Connection Modes: Import vs DirectQuery vs Live Connection
The connection mode you choose directly impacts data freshness, query performance, and feature availability in your embedded analytics solution. Each mode has trade-offs that must be evaluated against your specific requirements.
- Import – Data is loaded into the Power BI model at refresh time. Fastest query performance, supports all DAX features, but data is only as current as the last refresh. Maximum dataset size of 1 GB (Pro) or 400 GB (Premium).
- DirectQuery – Queries are sent to the data source at report interaction time. Always-current data, but slower query performance and limited DAX support. Requires data source to handle concurrent query load.
- Live connection – Connects to a pre-built model in Analysis Services or Power BI datasets. No local model; all calculation logic resides in the remote model. Ideal for shared semantic models.
- Composite models – Combine Import and DirectQuery tables in a single model. Use Import for dimensions and DirectQuery for large fact tables to balance performance with data freshness.
Gateway Requirements for On-Premises Data
When Power BI Embedded connects to on-premises data sources, an on-premises data gateway is required to establish a secure bridge between the cloud service and your internal network.
- Standard gateway – Required for SQL Server, Oracle, SAP, and other on-premises databases. Supports multiple data sources and scheduled refresh.
- Gateway cluster – Deploy multiple gateway nodes for high availability and load balancing in production environments.
- Personal gateway – Single-user gateway for development and testing only. Not recommended for production embedded scenarios.
- VNet gateways – For Azure-hosted data sources in private VNets, use Power BI VNet data gateway to avoid on-premises gateway deployment.
Why Choose EPC Group for Power BI Embedded
EPC Group has delivered Power BI Embedded solutions for ISVs and enterprises for 29 years as a Microsoft Gold Partner. Our founder, Errin O'Connor, authored 4 bestselling Microsoft Press books, and our architects specialize in designing multi-data-source embedded analytics architectures that handle enterprise security, multi-tenancy, and high-concurrency requirements. We have deep expertise in connecting Power BI Embedded to complex data ecosystems spanning Azure, AWS, on-premises databases, and SaaS APIs.
Build Embedded Analytics with the Right Data Sources
Let EPC Group's embedded analytics experts architect a Power BI Embedded solution that connects seamlessly to your data sources while meeting security and performance requirements.
Frequently Asked Questions
Can I use DirectQuery with Power BI Embedded?
Yes. Power BI Embedded fully supports DirectQuery for data sources that offer it (Azure SQL, SQL Server, Snowflake, etc.). DirectQuery in embedded scenarios sends queries to the data source each time a user interacts with a visual. Ensure your data source can handle the concurrent query load from embedded report viewers, especially during peak usage periods. Power BI Premium capacity auto-scales to manage report rendering, but the backend data source must be provisioned accordingly.
What is the maximum dataset size in Power BI Embedded?
For Import mode, the maximum dataset size depends on your capacity SKU. A-series capacities (EM1/A1 through EM3/A3) support datasets up to 3 GB. P1/A4 capacity supports up to 25 GB. P2/A5 supports up to 50 GB. P3/A6 supports up to 100 GB. Premium Gen2 and Fabric capacities can support datasets up to 400 GB with large dataset format enabled. DirectQuery has no dataset size limit since data stays in the source.
Do I need an on-premises data gateway for Azure SQL Database?
No. Azure SQL Database connects directly to Power BI Embedded without a gateway since both services are cloud-native. A gateway is only required for on-premises data sources (SQL Server, Oracle, file shares) or Azure services running inside a private VNet with no public endpoint. For VNet-isolated Azure resources, use the Power BI VNet data gateway instead of an on-premises gateway.
Can I combine multiple data sources in a single embedded report?
Yes. Power BI composite models allow combining Import and DirectQuery tables from different data sources in a single report. For example, import dimension data from Azure SQL and use DirectQuery for large fact tables in Snowflake. All combined sources render in a single embedded report with cross-source relationships. Note that cross-source relationships have some DAX limitations compared to same-source relationships.
How does row-level security work with different data sources in Embedded?
RLS in Power BI Embedded is applied through the embed token API regardless of the underlying data source. When generating an embed token, you specify the user identity and roles, and Power BI applies DAX filter expressions defined in the model. For Import mode, RLS filters the cached data. For DirectQuery, RLS filters are converted to SQL WHERE clauses and pushed to the data source. SSO DirectQuery can also pass the end-user's identity to the data source for native database-level security.
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Why Organizations Choose EPC Group
EPC Group is a Houston-based Microsoft consulting firm with 29 years of enterprise implementation experience and over 10,000 successful deployments across Power BI, Microsoft Fabric, SharePoint, Azure, Microsoft 365, and Copilot. We serve organizations across all industries including Fortune 500, federal agencies, healthcare, financial services, government, manufacturing, energy, education, retail, technology, and global enterprises.
What sets EPC Group apart is our governance-first approach. Every engagement begins with a security and compliance assessment. Our team of senior architects brings hands-on delivery experience across HIPAA, SOC 2, FedRAMP, and CMMC environments. We own outcomes, not hours.
- Fixed-fee accelerators with predictable pricing and defined deliverables
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- 11,000+ enterprise engagements refined into repeatable, risk-controlled patterns
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Power BI Strategy: 2026 Considerations for Power BI Embedded Supported Data Sources
Power BI Copilot grounds itself on the semantic model, NOT the underlying source data. That means Copilot answers are only as accurate as the DAX measure definitions, the field metadata (display folders, descriptions, hierarchies), and the synonyms taxonomy. In practice, the difference between a Copilot deployment that drives 32% time-savings and one users abandon within 90 days is whether the semantic model was Copilot-prepared.
Power BI capacity sizing in 2026 starts with the F-SKU economics: F2 ($263/mo) covers small workloads with up to 4 GB of memory and roughly 30 reports, F4 ($526/mo) handles a typical mid-market deployment with semantic-model refresh windows under 10 minutes, and F64 ($5,257/mo) is the sweet spot for enterprises consuming Power BI alongside Microsoft Fabric data engineering, lakehouse storage, and real-time intelligence. Capacity right-sizing should be revisited every 90 days because Microsoft adjusts F-SKU memory allocations, paginated report performance, and Direct Lake mode availability with each major service update.
Decision factors EPC Group evaluates
- Capacity sizing decision (F2/F4/F64+) tied to peak concurrent users and refresh window
- Copilot grounding quality assessment of semantic-model metadata
- Direct Lake mode adoption for Fabric-resident semantic models
- License optimization audit (Pro vs Premium Per User vs F-SKU)
- Row-level security via service principal authentication
For a tailored read on this topic in your specific tenant, contact EPC Group at contact@epcgroup.net or +1 (888) 381-9725. Engagement options at /pricing.