Retiring the Fabric data agent custom tool in Microsoft Foundry: move to Fabric IQ
In March 2025, we announced that agents in Microsoft Foundry could use Fabric data agents to answer questions about data in Microsoft Fabric. In the earlier integration, you connected to a data agent by adding it as a custom tool. For details, refer to Consume a data agent in Microsoft Foundry. What’s changing We are deprecating the earlier custom-connection integration. Starting 11/6/2026, that integration will no longer be supported. If your Foundry agents use a Fabric data agent as a custom tool, you must migrate them before that date. The replacement integration (Preview) uses the Fabric IQ (OneLake Catalog) tool, which makes your data available to agents and applications. In the Foundry portal, you can use this tool to add a published Fabric data agent to a Foundry agent. For configuration steps, refer to Consume Fabric data agent from Microsoft Foundry via Fabric IQ. Capabilities with the new integration The replacement integration changes how you find and connect Fabric data agents: Select accessible published data agents from the catalog. In the Foundry portal, the OneLake Catalog shows the published data agents available to you by name, so the connection workflow does not require you to enter a workspace ID or an artifact ID. Connect more than one data agent. A Foundry agent can call multiple data agents, such as separate agents for sales data and customer support data. The earlier custom-connection integration only supported one Fabric data agent. Connect through a Model Context Protocol (MCP) endpoint. MCP is an open standard for how an agent calls a tool. Each selected Fabric data agent is presented to the Foundry agent as a tool through a MCP endpoint. Coming soon, when a data agent answers a question, its response will also include the query it used to generate the answer. What you need to do If any of your Foundry agents use a Fabric data agent as a custom tool, complete the following steps before 11/6/2026: In Microsoft Fabric, make sure to publish your Fabric data agent with a rich and detailed description. Open the agent in the Microsoft Foundry portal. In the Tools section, add the Fabric IQ (OneLake Catalog) tool, and then select your data agent from the catalog. Update the Foundry agent's instructions. Name each data agent and describe the types of questions it should handle. Remove the earlier custom tool and then save the Foundry agent. Test the Foundry agent with representative questions for each connected data agent. If you connect data agents in code, update your implementation to use the Fabric IQ tool. For samples in Python, C#, and JavaScript, refer to Connect agents to Microsoft Fabric with Fabric IQ. Guides to migrate from Fabric Data Agent to Fabric IQ tool Option 1: Migrate in Foundry Portal Navigate to Foundry Portal and open the Tools Catalog, select “Fabric IQ (OneLake Catalog)”. . In the OneLake catalog, filter by “Data agent” type. Select any data agent you want to use, and you are ready to go. Provide instructions to your Foundry agent about how to use Fabric data agent. . Option 2: Migrate in SDK/Rest API After you publish the Fabric data agent, open its Settings and go to the Model Context Protocol tab. This tab shows: Data agent MCP server name MCP server URL (copy this value; you can use in every client) Data agent MCP tool name MCP server tool description Once you get the data agent MCP server URL, proceed with the following steps: Step 1: Get an access token az account get-access-token --resource https://management.azure.com --query accessToken -o tsv Step 2: Create a Foundry connection to the Fabric data agent A connection stores the Fabric data agent endpoint and how to sign in to it. Your agent uses this connection to reach Fabric. Replace each value in angle brackets, then run the request: curl --request PUT \ --url 'https://management.azure.com/subscriptions/<azure-subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.CognitiveServices/accounts/<foundry-account-name>/projects/<foundry-project-name>/connections/<connection-name>?api-version=2025-04-01-preview' \ --header 'Authorization: Bearer <access-token-from-step-1>' \ --header 'Content-Type: application/json' \ --data '{ "type": "Microsoft.MachineLearningServices/workspaces/connections", "properties": { "authType": "UserEntraToken", "group": "AzureAI", "category": "RemoteTool", "target": "<fabric-data-agent-mcp-endpoint>", "audience": "https://api.fabric.microsoft.com/.default", "isSharedToAll": true, "sharedUserList": [], "UseCustomConnector": false, "Credentials": { "ClientId": "fake" }, "metadata": { "ApiType": "Azure" } } }' Main field capabilities: Field Value What it means authType UserEntraToken The agent calls Fabric as the signed in user, with that user's Microsoft Entra token. category RemoteTool The connection points to a remote tool, in this case an MCP endpoint. target Your MCP endpoint The address of your Fabric data agent. audience https://api.fabric.microsoft.com/.default The token is issued for the Fabric API. isSharedToAll true Every user in the Foundry project can use the connection. Credentials.ClientId fake Keep this value as shown. The response includes an id field. This is the full resource ID of the connection. Save it for step 4. It looks like this: /subscriptions/<azure-subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.CognitiveServices/accounts/<foundry-account-name>/projects/<foundry-project-name>/connections/<connection-name> Step 3: Install the Python packages pip install "azure-ai-projects>=2.2.0" azure-identity Step 4: Set the environment variables Variable Where to find it FOUNDRY_PROJECT_ENDPOINT The Overview page of your Foundry project. FOUNDRY_MODEL_NAME The deployment name of your model in your Foundry project. FABRIC_IQ_PROJECT_CONNECTION_ID The full connection resource ID from step 2. Use the full ID, not only the connection name. export FOUNDRY_PROJECT_ENDPOINT="<your-project-endpoint>" export FOUNDRY_MODEL_NAME="<your-model-deployment-name>" export FABRIC_IQ_PROJECT_CONNECTION_ID="<your-connection-resource-id>" Step 5: Create an agent that uses the Fabric IQ tool and ask it a question This script creates a prompt agent with the Fabric IQ tool, sends it one question, prints the answer, and then deletes the agent. import os from azure.identity import DefaultAzureCredential from azure.ai.projects import AIProjectClient from azure.ai.projects.models import PromptAgentDefinition, FabricIQPreviewTool endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"] with ( # Signs in with your Azure command line interface account (from az login). DefaultAzureCredential() as credential, AIProjectClient(endpoint=endpoint, credential=credential) as project_client, project_client.get_openai_client() as openai_client, ): # 1. Define the Fabric IQ tool. It uses the connection you created in step 2. fabric_iq_tool = FabricIQPreviewTool( project_connection_id=os.environ["FABRIC_IQ_PROJECT_CONNECTION_ID"], require_approval="never", # The agent calls the tool without asking you first. ) # 2. Create the agent with your model, its instructions, and the Fabric IQ tool. agent = project_client.agents.create_version( agent_name="MyAgent", definition=PromptAgentDefinition( model=os.environ["FOUNDRY_MODEL_NAME"], instructions="Use the available Fabric IQ tools to answer questions and perform tasks.", tools=[fabric_iq_tool], ), ) print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})") # 3. Ask the agent a question. Change this to a question about your own data. user_input = "Which customers placed orders above $10,000 last quarter?" response = openai_client.responses.create( input=user_input, extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}}, ) print(f"Agent response: {response.output_text}") # 4. Delete this agent version so unused versions don't build up in your project. project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version) print("Agent deleted") Finally, run the script. python fabric_iq_agent.py Learn more and share feedback We recommend that you move to Fabric IQ as soon as you can. We look forward to improving this integration, and we’d like to hear your feedback. Join the community discussion to share feedback and report issues. For more information, see these articles: Consume Fabric data agent from Microsoft Foundry via Fabric IQ Connect agents to Microsoft Fabric with Fabric IQ
Fabric Policies: Granular governance at scale (Preview)
As Microsoft Fabric grows across teams, workspaces, and capacities, governance becomes harder to manage with broad tenant-wide switches alone because governance models are not one-size-fits-all. Fabric Policies provide granular controls that can fit your governance model by defining what actions are allowed, to whom and where. The result is governance that scales with your Fabric estate. Teams keep the flexibility they need to work; capacity admins can protect the resources they manage, and central administrators gain consistent controls and visibility across the organization. Fabric Policies help organizations: Match controls to organizational boundaries - Apply organization-wide rules at tenant scope and resource-specific rules at capacity scope. Protect sensitive actions without slowing routine work - Keep approved self-service available while restricting higher-risk actions to the right users and context. Apply consistent guardrails at scale - Manage policies centrally through Policy Sets, Policies Center, auditing, and public APIs. What you can govern Fabric Policies currently provide controls for item creation, external data sharing (coming soon), and selected security-sensitive workspace settings. Control item creation by capacity Capacity admins can set a policy for any Fabric item type in workspaces assigned to their capacity (Power BI item creation is not yet enforced). An admin can allow the Data Engineering security group to create Dataflow Gen2 items in selected data platform workspaces and the AI Builders security group to create Data agent items in selected AI workspaces. Matching requests proceed; requests that do not match a complete Allow rule are blocked. This control supports a gradual Fabric rollout. Organizations can make specific item types available to approved teams and workspaces as their governance requirements are met, rather than enabling every item type everywhere at once. Item creation requires both controls to allow the request: the applicable Fabric item creation setting and the active Item creation policy. Figure: Advanced Item creation policy with separate Allow rules for Dataflow Gen2 and Data agent. Protect security-sensitive workspace settings Fabric admins can protect selected settings without removing broader workspace administration capabilities. An admin can allow the Central IT team to edit Outbound access protection, Inbound access protection, and Apply customer-managed keys (CMK) in sensitive workspaces. Other workspace admins can continue to edit the rest of the settings. The policy refines what existing workspace admins can change; it does not grant the Workspace Admin role. Govern external data sharing (Coming Soon) Fabric admins can support partner collaboration while controlling who can share, which workspaces the data comes from, its sensitivity label, and the recipient email domain. An admin can allow the Finance security group to share items labeled Confidential from Finance workspaces with recipients at CALDOVA.com. Sharing proceeds only when all configured conditions match; other requests are blocked. External sharing can be allowed by either the existing External data sharing tenant setting or a matching and active policy rule. How Policies work On Policy Set creation, an administrative scope is selected, such as the tenant or a capacity. This defines the available policy types and the administrator role required to activate the Policy Set for that scope. For each policy, administrators choose Allow all, Block all, or Advanced. Advanced policies use an allow-based model: All conditions within a rule must match. A match on any complete rule allows the action. If no rule matches, the action is blocked. Once the Policy Set is activated, Fabric evaluates its policies whenever a governed action occurs within that scope. Only one Policy Set can be active for a tenant or an individual capacity. Activating a replacement changes which definitions govern that scope. Existing access requirements remain in effect. Policies do not grant Fabric roles, workspace access, or item permissions. A user must first have the access required for the action; the policy then adds a governance decision for that action and context. Fabric Policies are available in preview for supported Fabric capacities and workspaces. Standard Fabric capacity and workspace requirements apply. Get started with Fabric Policies In Admin portal > Tenant settings, enable Policies (Preview). In a Fabric-capacity-backed workspace, create a Policy Set and choose Tenant or Capacity scope. Configure a policy with Allow all, Block all, or Advanced Allow rules, then review the users, actions, and workspaces covered. Activate the Policy Set as an administrator of the target scope. For capacity scope, select a capacity you administer. Review the related Fabric setting and policy together, then verify the active result in OneLake Catalog > Policies Center. For repeatable deployment across environments or capacities, use Fabric REST APIs to automate Policy Set and policy management at scale. API operations and activation remain subject to the required item permissions and administrator role. Policies best practices and management Design Advanced policies around every allowed scenario. List every user, item type, and workspace combination that must continue to work. Because Advanced policies are allow-based, a request is blocked when no complete rule matches. Align capacity policies with capacity ownership. Item creation policies govern workspaces assigned to the selected capacity. Each capacity can have its own active Policy Set, giving capacity admins control within their resource boundary. Review policy coverage whenever workspaces move between capacities. Use a dedicated management workspace. A Policy Set can be hosted in one supported Fabric-capacity-backed workspace and activated for a different capacity. A dedicated workspace can simplify access, ownership, and lifecycle management across multiple capacities. Separate editing from activation. People with Read, or the workspace Viewer role, can view a Policy Set. People with Read and Write, or a workspace Contributor, Member, or Admin role, can edit it. Activation still requires the administrator role for the target scope: Fabric admin for tenant scope or capacity admin for capacity scope. Validate before broad rollout. Start with representative users and workspaces and review each allowed scenario. Monitor and refine. Use Policies Center to review active policies and the Microsoft 365 Audit Log to follow Policy Set and rule lifecycle operations. Conclusion As Microsoft Fabric adoption scales across teams, workspaces, and capacities, governance must scale with it. Fabric Policies provide the flexibility to apply the right controls in the right contexts while maintaining a consistent governance model across the organization. By combining centralized oversight with granular policy enforcement, organizations can confidently expand their Fabric footprint without sacrificing security or control. Learn more about policies.Simplifying secure data access with Delegated OneLake Shortcuts (Preview)
Introduction Data rarely stays in one place. As organizations standardize Microsoft Fabric and OneLake, the same datasets need to be reused across teams, domains, workspaces, and increasingly across tenant boundaries. The challenge is no longer moving data; it is sharing it securely, consistently, and at scale without creating copies, breaking governance, or forcing every consumer to be individually provisioned at the source. OneLake Shortcuts already solve a large part of this problem. A shortcut presents data where people need it while the data stays in its original location, enabling a true zero-copy approach to distribution. By default, OneLake Shortcuts use pass-through authentication: when a user reads a shortcut, Fabric accesses the target data using that signed-in user’s identity, and the data owner controls access directly on the target. Pass-through is the right model for many collaborative scenarios, but customers have consistently told us it does not fit every access pattern. Two points came up frequently: Access management does not scale. When a curated dataset must be served to thousands of downstream users across multiple teams, the data owner becomes responsible for granting and maintaining every individual user’s permission on the source, an operational bottleneck that grows with every new consumer. Cross-tenant sharing is harder than it should be. Multi-tenant organizations told us that they need to access data residing in OneLake across their own tenant. These are not edge cases. They are everyday realities for enterprises building governed, reusable data products on Fabric. The preview of Delegated OneLake Shortcuts — including delegated sharing both within a tenant and across tenants — gives data owners a simpler, governed way to distribute data without compromising on security. Introducing delegated OneLake Shortcuts Delegated OneLake Shortcuts add a second authentication option to the existing shortcut experience you already know. Instead of accessing the target data as each signed-in user, a delegated shortcut accesses the target through a configured connection identity. That identity can be an organizational account, a service principal. This identity is attached to the shortcut, so all access to the shortcut reaches the target as the delegated identity. Delegated authentication is entirely optional and complements the default experience. If a user does not choose delegated authentication when creating a shortcut, the shortcut continues to use pass-through authentication exactly as before. The default flow is unchanged; delegation is simply there when you need it. How it works A delegated shortcut behaves like other external shortcuts in Fabric. When you create one, you sp, and that connection is used to browse and read the target data. This brings a familiar, governed connection model to OneLake-to-OneLake sharing. Identity delegation - Downstream users access the data through the delegated identity rather than their own, so the data owner no longer must provision each individual consumer on the source item. Secure access enforcement with OneLake security - OneLake security roles can be configured on both the data producer and data consumer delegated Shortcuts. At the time of this writing, table level security and column-level security are supported for delegated shortcuts, on both the target (where you are creating the shortcut) and the shortcut source (where data resides). Delegated permissions management - A shortcut can delegate as a fixed identity that represents a business unit. The central data owner controls what that identity can see, while the business unit manages OneLake security for its own end users, all while still honoring the controls applied to the delegated identity. Cross-tenant sharing Delegated shortcuts also work across Microsoft Fabric tenants. A cross-tenant delegated shortcut lets you create a OneLake shortcut to data that lives in another organization’s Fabric tenant. You provide a connection path to the external OneLake data and authenticate with an identity from that tenant; downstream users then access the external data through the configured delegated identity, without each user needing individual cross-tenant permissions. This makes delegated shortcuts a natural fit for multi-tenant enterprises, for example, sharing curated data between an organization’s test and production tenants, or between a parent company and a subsidiary using the same zero-copy, intersection-based security model that applies within a tenant. Difference between External Data Sharing and delegated Shortcuts Microsoft Fabric has External Data Sharing, a feature that enables Fabric users to share data from their tenant with users in another Fabric tenant. External data sharing can be used when the consumer has no identity in the producer's tenant, such as sharing across organizational boundaries with an outside partner or customer. This is ideal when you must share with partners or when ISVs must share data with their customers and don’t want to have the consumer identity in their tenant. Cross-tenant delegated shortcuts are used when the data consumer has an identity, such as an organizational account or service principal, in the producer’s tenant. For example, an organization can share data between its own test and production tenants, with access flowing through the configured delegated identity. Use cases Delegated OneLake Shortcuts are designed for the moments when the default pass-through behavior does not match the access pattern you want for a data product. Common scenarios include: Departmental data sharing at scale - Represent each department with a delegated identity, scope what that identity can see, and let department owners manage access for their own users instead of routing every request through the central data owner. Cross-tenant and subsidiary sharing - Share curated data between tenants — such as test-to-production or parent-to-subsidiary — with no data copies and the same delegated security model. Getting started Open the target Fabric item, such as a Lakehouse, and select Get data > New table shortcut. In New shortcut, select Microsoft OneLake, then choose the source you want to shortcut to. For cross-tenant data, select Enter connection details and provide the external OneLake path. For Connection method, select Delegated identity, then Connect. Choose an existing connection or create a new one by providing the OneLake path, a recognizable connection name, and an authentication kind (organizational account or service principal). Sign in to complete authentication. Browse the source, select the folders or tables to include, then review and create the shortcut. To switch an existing shortcut between pass-through and delegated authentication, delete and recreate it with the desired method. For detailed steps, refer to the OneLake Shortcuts documentation. Conclusion and next steps OneLake shortcuts are a foundational building block for zero-copy data distribution across Microsoft Fabric. Delegated OneLake Shortcuts extend that foundation to the scenarios enterprises care about most: serving curated data to large audiences, delegating access management to the teams closest to the users, and sharing securely across tenant boundaries. Together, pass-through and delegated shortcuts let organizations choose the right balance of control, scale, and simplicity for each data product. Pass-through keeps source-managed authorization per person for collaborative engineering. Delegated mode turns a shortcut into part of a governed publishing architecture: central teams retain ownership of the source, consuming teams avoid copying data, and downstream audiences access a managed experience rather than raw-path access — without ever giving up the governance benefits of unifying data in OneLake. Share your feedback, use cases, and questions in the Microsoft Fabric Community. Your input directly shapes the roadmap.FabCon and SQLCon Barcelona 2026: What’s new in Microsoft OneLake and its rapidly growing ecosystem
If you haven’t already, check out Arun Ulag’s hero blog “FabCon and SQLCon 2026 in Barcelona: Building the data foundation for Microsoft Copilot and Agents” for a complete look at all our FabCon and SQLCon announcements across both Fabric and our database offerings. Organizations are moving quickly to turn AI into meaningful business outcomes. But the value of AI still depends on the data behind it: whether teams can find the right data, access it securely, and use it across the tools and platforms where agents work. When data remains fragmented across clouds, business units, and technology stacks, every new AI initiative starts with the same costly work of moving, copying, and reconciling data. Microsoft OneLake provides a unified data foundation for Microsoft Fabric, helping organizations connect and govern data across their estate without creating more silos. Every Fabric user and customer is also a OneLake user and customer and at FabCon and SQLCon Barcelona 2026, I’m excited to share that we're expanding OneLake further to continue on our principal of providing customers an open, secure, and scalable foundation for connecting and sharing data and putting governed data to work across analytics and AI, Connect and share data across your distributed estate Data is increasingly distributed across clouds, applications, partners, and platforms. OneLake helps organizations work across that distributed estate without requiring every team to move and maintain another copy of the data. Share governed data and context with IQ sharing An organization’s unique IQ is its data, knowledge, and workflows. When copilots, agents, and applications can draw on this shared understanding of the business, they can deliver more consistent and relevant experiences. But that context needs to be governed and reusable so the same terminology, relationships, instructions, and domain knowledge can be applied across teams and applications. Today, we’re introducing IQ sharing in preview soon, a new capability that enables organizations to securely share governed data and business context across teams, business units, customers, partners, and ecosystems. This makes trusted organizational context portable and reusable, extending it beyond a single team or organization. At launch, IQ sharing supports data, metadata and context represented in files, including Markdown (.md) agent instructions and RDF (.rdf) ontologies. We plan to expand these capabilities with native support for Fabric IQ ontologies and Power BI semantic models, making it easier to securely share and reuse business context across AI experiences. EY, TomTom, S&P Global, Financial Fabric, and xMentium are already building integrations with IQ sharing, with more partners to come. TomTom, for example, is bringing its geospatial and transportation intelligence into Fabric through IQ sharing, with available data products discoverable through the OneLake catalog. "Trusted location data is a foundational ingredient for our customers' applications, AI solutions, and intelligent agents," said Leo Sei, Chief Product Officer, TomTom. "We're excited to be one of the first partners to onboard to IQ sharing and extend access to TomTom location data products across our customers' analytics and AI ecosystems. IQ share makes our data easy to discover, securely distribute, and centrally govern while preserving the control, context, and trust that are critical for delivering AI-ready data products." John Whittaker, VP of Cloud Marketplace at EY, sees similar potential for IQ sharing to make business context more portable and reusable: “In collaboration with Microsoft, IQ sharing is an important step toward making context reusable, governed, and portable, with the potential to bring trusted knowledge, methods, and domain insight to clients in new AI-enabled ways, helping people move faster, support stronger decision-making, and scale value with confidence.” Together, OneLake and IQ sharing provide an open foundation for distributing governed intelligence, helping organizations reuse trusted data and business context to power analytics, applications, copilots, and agents. Figure: Creating a new IQ sharing item to share governed data and business context. Connect and transform data across your entire estate We’re continuing to expand OneLake’s open data network across platforms, clouds, and business applications, giving organizations more ways to work with distributed data through a shared data foundation without having to move, duplicate, or maintain additional copies. Connect Salesforce Data 360 to OneLake with bidirectional integration OneLake and Salesforce Data 360 are coming together to give customers a simpler way to use their Salesforce data across Microsoft Fabric without moving or duplicating it. With OneLake shortcuts, Salesforce Data 360 data becomes part of the unified data estate, where teams can discover it in the OneLake catalog and use it across Fabric workloads, including SQL, notebooks, and Power BI. Customers can also use Salesforce data to build ontologies that provide rich business context to Fabric data agents, helping them reason over it alongside the rest of their enterprise data. Get started now. Figure: Connecting OneLake with Salesforce Data 360 through a OneLake shortcut. Because OneLake shortcuts access data directly from Salesforce storage, customers can work from a single copy of always-current data without building pipelines, paying for duplicate storage, or keeping copies in sync. Queries use Fabric compute without placing additional load on Salesforce instances, while Salesforce administrators remain in control of what data is shared. Salesforce controls and OneLake security provide governed access across Fabric, giving customers the flexibility to use their Salesforce data for analytics and AI while keeping it securely managed. Access versioned lakeFS data through OneLake shortcuts OneLake shortcuts now provide a complementary way to access versioned data managed in lakeFS from Microsoft Fabric. Customers can connect to the latest data from a lakeFS branch or reference a specific immutable commit, helping them create reproducible reports, experiments, and audits without building separate pipelines to copy and maintain snapshots of their data. Mirror Dynamics 365 Business Central data into OneLake We’re also extending this connected data foundation to Dynamics 365 Business Central. A new integration enables organizations to bring selected Business Central tables and company data directly into OneLake through mirroring. Built-in monitoring provides both overview and detailed logs, helping teams track integration health and troubleshoot issues. Read and write OneLake data from Azure Databricks For organizations working across data platforms, we’re strengthening interoperability with Azure Databricks through native storage in OneLake, now supported in production. This gives joint customers a more direct way to use OneLake data from Azure Databricks without copying it into another storage layer, helping teams preserve a consistent data foundation across platforms. Connect more external data to OneLake through open standards We’re continuing to extend the range of external data that organizations can make available through OneLake. Mirroring for Google BigQuery is now available, making it easier to work with Google Cloud data in Fabric for analytics and AI while reducing duplication and bespoke integrations. New Apache Iceberg-based catalog federation support for AWS Glue Data Catalog, Google Lakehouse runtime catalog, and CONNECT from AVEVA further expands the data organizations can discover and use through OneLake, bringing more of the distributed data estate within reach of Fabric. Operate and scale OneLake with greater flexibility An enterprise data foundation must be flexible to operate and powerful to use. Organizations need simpler ways to align costs with demand, monitor their data estate, and scale as analytics and AI workloads grow. On-demand billing brings more flexibility to OneLake compute In the coming weeks, we’re introducing on-demand billing for OneLake compute in preview, allowing customers to pay only for the OneLake compute they use. Previously, access to data in OneLake depended on a running Fabric capacity, which could be affected by pauses or throttling. With on-demand billing, OneLake compute can scale elastically when needed, helping minimize throttling during demand spikes. We’re also introducing Fabric zero-provisioned (F0), a new entry point to Fabric and OneLake that removes the need to provision compute before getting started. With on-demand billing enabled by default, organizations can immediately access Fabric services and pay only for the resources they use. Together, on-demand billing and Fabric zero-provisioned allow organizations to read and write data without maintaining dedicated capacity. The announcement builds on Microsoft’s broader vision of OneLake as a shared data foundation across platforms such as Fabric, Azure Databricks, and Snowflake. Recent interoperability enhancements allow organizations to store and access a single copy of data across these environments, reducing duplication and simplifying data architectures. By decoupling OneLake access from Fabric capacity, on-demand billing makes it easier for customers to adopt OneLake as their centralized enterprise data lake while continuing to use the compute engines that best fit their needs. This applies to AI workloads as well. Microsoft Foundry can seamlessly be used with OneLake on-demand billing as well. Put OneLake data to work across analytics, AI, and applications Connecting data is only the first step. Organizations also need to make governed data easy to discover and use across the tools, applications, and AI experiences where people and agents work. Discover and use governed data in Microsoft Foundry and Excel Data becomes more valuable when people can discover and use it from the tools where they already work. A new OneLake catalog integration in Excel, available through the modern Get Data experience, helps business users discover and analyze organizational data directly from Excel. By reducing the steps between discovery and analysis, the integration helps more users work with governed Fabric data through a familiar experience. We are also announcing deeper integration with Microsoft Foundry, our end-to-end platform for customizing, hosting, running, and managing AI solutions. With this integration, you can access the OneLake catalog in Foundry, making it easy to discover and build knowledge directly from OneLake data. Foundry users can explore their data estate including rich metadata, endorsements, sensitivity labels, and descriptions, and connect their OneLake items with a single selection to add data to Knowledge. Figure: Adding OneLake data as knowledge in Microsoft Foundry. Give agents, applications, and data engines secure access to OneLake tables The OneLake Table Read API (Preview), gives AI agents and applications secure, serverless access to Delta Lake and Apache Iceberg tables without requiring SQL. OneLake security automatically enforces table-, row-, and column-level access controls on every query, returning only authorized data. Built on open-source Apache Arrow, the API helps developers efficiently integrate governed enterprise data into AI agents and applications. We’re also expanding the ways external data engines can work with OneLake through open APIs. ClickHouse support for reading Apache Iceberg tables from OneLake using OneLake Table APIs is now generally available, enabling customers to query OneLake data directly without creating another copy. Support for writing Iceberg tables to OneLake using Table APIs and credential vending (Preview), allowing results produced in ClickHouse to become part of the shared OneLake data foundation and available across Fabric. In addition to working with OneLake data directly through Table APIs, customers can use the new ClickHouse workload in Microsoft Fabric when they want dedicated analytical compute. Now in public preview, the experience provisions a dedicated ClickHouse Cloud service on Azure and includes an embedded SQL console within Fabric. Teams can synchronize OneLake tables as accelerated, point-in-time copies for analysis in ClickHouse Cloud, while OneLake remains the source of truth. This gives developers a dedicated environment for responsive analytics, interactive applications, and AI agent experiences while keeping those workloads connected to their Fabric data. Teradata Autonomous Knowledge Platform is integrated OneLake Table APIs, enabling enterprise customers to run Teradata's high-performance enterprise AI directly on data stored in OneLake, without extract, transform, and load (ETL) pipelines, data duplication, or migration. Built on open Apache Iceberg standards, the integration allows Teradata users to query OneLake tables in place using standard Iceberg APIs, with cross-platform authentication and access controls handled natively. Enterprises with data in both environments can now join and analyze it without first consolidating it into a single system or rebuilding governance from scratch. LakeSail Cloud further expands the engines that can work with data in OneLake by bringing Rust-native Spark compatibility to Delta Lake and Apache Iceberg tables. With LakeSail, teams can use Spark SQL, DataFrame, and Python workloads to read and write OneLake tables without deploying separate data connectors or changing existing Spark code. This gives organizations another way to use the processing engines that fit their workloads while keeping data in the shared OneLake foundation. Build operational applications on OneLake Organizations increasingly need to turn governed data into applications and workflows that help people make decisions and take action. OneLake provides a shared foundation for these operational experiences, keeping applications and their data connected across Fabric. Last May, we introduced Fabric Apps to help developers build and deploy data and AI-powered applications on trusted, governed data in Microsoft Fabric. We’re now expanding Fabric Apps with support for OneLake sources alongside existing SQL databases. Developers can connect applications to Fabric warehouses, lakehouses, and Power BI semantic models while preserving existing permissions and trusted business logic. This brings applications closer to the data they rely on and reduces the need to move or duplicate data across systems. We’re also deepening the integration between Planning in Fabric (Preview) and OneLake. Now, planning data and writeback can be stored directly in OneLake, enabling teams to work across larger models and with more contributors while maintaining a responsive experience. These improvements increase planning performance and scale by up to 10x, while keeping planning outputs readily available for use across the broader Fabric ecosystem. Check out all the innovation coming to Planning in Fabric in my other blog, Planning in Microsoft Fabric: From insight to action. Discover, govern, and protect your data estate As data becomes available to more people, applications, and agents, governance and security become even more important. Organizations need a unified way to discover their data, manage access, and apply consistent protections wherever that data is used. Discover more of your data with deeper lineage in the OneLake catalog The OneLake catalog is becoming a richer, more granular place to discover and understand data across Fabric. Users can now go beyond finding Fabric items to explore individual tables and columns, view their details, and preview the underlying data directly in the catalog, making it easier to evaluate data before putting it to work. More granular search makes it easier to find that data across Fabric. The OneLake catalog search API now returns OneLake tables and semantic model tables alongside Fabric items in a single relevance-ranked result set. New Spark granular runtime lineage, coming soon, adds another layer of context, helping users understand how data is used and transformed across their data estate. Manage and govern your data estate in the OneLake catalog Discovery is only part of the equation. The OneLake catalog Govern experience brings centralized governance capabilities into the OneLake catalog, helping administrators manage their data across tenant, domain, capacity, and workspace scopes from a unified experience. Automatically generated signals and proactive recommendations help administrators understand their governance posture, identify areas that need attention, and take informed action. Figure: Reviewing data estate health and governance recommendations in the OneLake catalog. Governance also extends into developer workflows. Govern Skills for Fabric enable AI coding assistants such as GitHub Copilot to discover existing Fabric items and tables through the OneLake catalog. This supports a find-before-you-build approach, helping developers find and reuse governed data before creating new assets or writing code. Connect OneLake data with trusted context from Atlan Understanding data requires more than knowing where it lives. Teams also need the business context, lineage, and governance information that explain what data means and how it is used. We’re expanding our integration with Atlan, an industry-leading provider of data and AI governance that helps organizations discover, understand, and govern data across their enterprise. Atlan’s native Microsoft Fabric connector (Generally Available) and adds deeper lineage across semantic models and measures, Spark workloads through the new Fabric Platform Lineage APIs, and Dataflow Gen2. The integration also extends in the other direction with OneLake shortcuts, enabling teams to access Atlan context and Atlan lakehouses from within Fabric and query it using Fabric Spark and SQL capabilities. Together, these integrations bring OneLake data and the context surrounding it closer together, helping teams better understand and govern the data they use for analytics and AI. Apply more precise policies across Fabric Policies in Fabric (Preview) introduce precise, attribute-based controls that help organizations enforce governance requirements based on user and data attributes, rather than relying only on broad administrator settings. Policies support granular controls for key scenarios, with item creation and workspace security available at launch and external data sharing coming soon. Policies also come with a centralized policy management experience, along with monitoring and APIs that make it easier for administrators to manage and track policies across Fabric. CI/CD integration helps teams automate the policy lifecycle as their environments grow. For example, external data sharing policies will give administrators more flexibility over cross-tenant collaboration. Rather than simply turning external sharing on or off, organizations can apply more granular controls based on attributes such as users, workspaces, and recipients, helping teams collaborate externally while maintaining the controls needed for security, governance, and compliance. Strengthen data protection with OneLake security As access to data expands across users, applications, AI agents, and platforms, organizations need security controls that remain consistent wherever that data is used. New OneLake security capabilities make it easier to understand and manage access within OneLake, preserve existing protections as data moves across platforms, and control how OneLake connects to external sources. We’ve redesigned the View users in role experience for OneLake security, now generally available, to simplify permissions auditing and management. New views help administrators quickly understand who can access a specific table and what data an individual user can see. From the same experience, they can assign users to multiple roles or revoke access, making it easier to review and manage permissions as data usage grows. We’re also making it easier to maintain consistent protections for data used across platforms. Trust3.AI is introducing a capability that synchronizes row- and column-level policies between Databricks, Snowflake, and OneLake security. By mapping policies from these platforms to OneLake security rules, the integration helps organizations maintain consistent access controls for users, applications, and AI agents without manually recreating policies in each environment. Mirroring for Snowflake brings Snowflake data into OneLake and keeps it synchronized for use across Fabric. Until now, customers have had to recreate the security policies governing that data after it was mirrored. With Snowflake Security Roles Replication, now in public preview, organizations can replicate source-defined roles and permissions alongside their data and translate them into OneLake security. This carries important governance context with the data and reduces the need to manually rebuild access controls as organizations bring their Snowflake and Fabric data estates together. Finally, outbound access protection for OneLake shortcuts (Generally Available), giving Fabric administrators control over which external data sources and endpoints shortcuts in a workspace can connect to. These controls help reduce the risk of unintended or malicious data exfiltration while still allowing approved connections that support business needs. Expand what you can do through the Fabric partner ecosystem OneLake provides a shared data foundation that partners can build on to bring specialized capabilities directly into Microsoft Fabric. At FabCon and SQLCon Barcelona, we’re expanding the Fabric partner ecosystem with new workloads that give organizations more ways to analyze, manage, and act on the data in OneLake without leaving Fabric. Esri: bring location context to business analysis (Generally Available) Location can reveal patterns and relationships that are difficult to see in rows and columns alone. Esri’s ArcGIS Maps for Microsoft Fabric brings mapping and spatial analysis into Fabric, helping teams explore their business data geographically. Stream Layers add support for real-time information, while expanded Parquet and OneLake mapping makes more organizational data available for spatial analysis. Enhanced authoring, symbology, and sketching capabilities give analysts more flexibility to tailor maps to the questions they want to investigate. Learn more about ArcGIS Maps for Microsoft Fabric. Figure: Exploring spatial patterns with ArcGIS Maps for Microsoft Fabric. Telmai: Build confidence in the data behind every decision Reliable analytics and AI depend on knowing whether the underlying data is complete, current, and ready to use. Telmai Data Reliability for Microsoft Fabric, now generally available, continuously monitors critical OneLake tables for changes in data volume, schema, freshness, and completeness. Telmai uses context from the OneLake catalog to help teams focus their monitoring on critical data assets. AI-assisted incident correlation connects related issues across the data estate, while Ask Telmai provides a conversational way to investigate potential causes instead of reviewing isolated alerts one by one. Telmai can also make machine-readable trust signals available to Fabric data agents and other compatible AI clients, helping both people and AI-driven experiences account for data reliability when using OneLake data. Figure: Configuring Telmai monitors for critical OneLake tables in Fabric. Celonis: understand how work flows and where it can improve (Generally Available) Business processes rarely follow a simple path. Celonis Process Intelligence brings process visualizations and business context into Fabric, helping teams understand how work moves across their organization using the data they already have. Users can explore process flows, investigate inefficiencies and their root causes, and identify opportunities for improvement alongside the analytics they use to understand the rest of their business. Learn more about Celonis. Figure: Analyzing supply chain process flows and performance with Celonis in Fabric. Spectral Core: simplify database migration to Fabric (Generally Available) Spectral Core Fabric Migration brings data movement, SQL code translation, schema and object conversion, and validation into a guided Fabric experience. Teams can work through the steps involved in migrating databases with a clearer view of what is being moved and where additional attention is needed. Learn more about Spectral Core for Fabric. Figure: Monitoring database migration progress with Spectral Core Fabric Migration. Financial Fabric: bring capital markets data closer to analysis (Generally Available) Financial Fabric’s Capital Markets DataHub brings data from more than 200 data providers and 10,000 datasets into Fabric, helping investment, risk, and operations teams reduce the work required to source and access financial data. Ask DataHub provides conversational exploration, while Power BI Direct Lake analytics connects that data to familiar analysis experiences. Learn more about Financial Fabric. Figure: Discovering capital markets datasets in Financial Fabric DataHub. IntuigenceAI: turn industrial information into usable knowledge (Generally Available) IntuigenceAI helps energy and process manufacturing teams turn information spread across drawings, schematics, datasheets, and logs into a queryable knowledge graph. Its AI synthetic engineers provide a multilingual conversational experience for retrieving information and supporting engineering work, with data residing in the customer’s OneLake. Learn more about IntuigenceAI for Fabric. Figure: Using IntuigenceAI to retrieve engineering insights from industrial documents in Fabric. Metropolis: connect communications data with business outcomes (Preview) Metropolis VoiceLake normalizes supported communications data into a Fabric Lakehouse and provides ready-made Power BI reports and a semantic model for exploration. Organizations can combine communications records with other business data for analytics and AI, providing a broader view of service activity and operational performance. Learn more about Metropolis VoiceLake. Figure: Monitoring communications analytics and integrations with Metropolis VoiceLake. Figure: Exploring and validating data with natural-language analysis in Daivio. And there are many more. Together, these partner workloads extend the ways organizations can analyze, manage, and act on their data in Fabric. Customers can explore these experiences in the Fabric Workload Hub and choose the specialized capabilities that fit their business needs. Figure: Monitoring cloud and AI spending with Data-Driven AI CloudMonitor. Build on a more open, secure, and manageable data foundation These announcements advance a common goal: helping organizations make more of their data available for analytics and AI without creating more copies or giving up control. IQ sharing, shortcuts, mirroring, and deeper platform interoperability connect data across organizational and technology boundaries. The OneLake catalog, security, policies, and expanded partner integrations help teams discover, understand, govern, and put that data to work. And with more flexible billing and stronger monitoring, organizations can operate and scale their data foundation as their needs grow. Explore more Microsoft Fabric innovation Alongside these OneLake announcements, we are introducing several innovations across Fabric. For a broad overview of all these announcements, read Arun Ulag’s hero blog “FabCon and SQLCon 2026 in Barcelona: Building the data foundation for Microsoft Copilot and Agents.” If you want more details, check out the Fabric September 2026 Feature summary blog and the latest posts on the Fabric Updates channel, including my other blog: Planning in Microsoft Fabric: From insight to action.Get ready for table discovery in OneLake Catalog search (Preview)
In late September, OneLake Catalog will begin surfacing tables as searchable objects in Microsoft Fabric. This change expands which table metadata users can discover based on their existing item permissions, so administrators may want to review the related tenant setting before rollout. To give organizations time to prepare, the controlling tenant setting is already available in the Fabric admin portal. Administrators can review and configure this setting before table search becomes available. Tables are the first object, an entity contained inside a Microsoft Fabric item, that OneLake Catalog will return as a standalone search result. Bringing objects into search makes the catalog more useful for everyday discovery because they are often where analysis, reporting, and development work begins. Find tables directly Finding the right data often starts below the item level. You may know a table name, its purpose, or only the name of a column it contains, but not which item contains it. Table discovery will let you search directly for tables in semantic models, lakehouses, and mirrored databases without first locating and opening the parent item. We plan to support tables from more Microsoft Fabric item types, along with additional object types, as OneLake Catalog expands beyond item-level discovery. Each matching table will appear as its own result rather than as metadata attached to the parent item. Columns will not appear as standalone results, but you can still find the relevant table when a column name is your only starting point. Table discovery will be available in Fabric’s global search UI and programmatically through the OneLake Catalog Search API. You will be able to search by table name or description or use an exact column-name match to find its containing table, without knowing the workspace or parent item in advance. You can also access search through the Fabric Core remote MCP server, Fabric local MCP server, or the search skill from the Fabric Skills library. These options bring the same permission-aware discovery to applications, AI agents, GitHub Copilot, and other compatible AI coding tools. Understand how permissions affect table visibility Before table search becomes available, administrators should understand how existing permissions affect what users can discover. This change does not grant access to additional data, but it may allow users to discover metadata for tables contained within items they can already access. You can discover a table only if you have Read control-plane permission or higher on its parent item. You do not need Read All or Read Data permission to find a table in search. Data-plane permissions, including OneLake security, do not change whether a table appears in catalog search. The relevant workload still enforces its data-access permissions when you open, query, or otherwise use the table. Catalog discoverability and data access remain separate decisions. Tables in semantic models protected by object-level security are excluded from search. Object-level security can protect table and column names as well as their metadata, so OneLake Catalog currently does not surface tables from those models. Control object discovery with a tenant setting Before table search rolls out, Fabric administrators should review the Users can find objects in search tenant setting. This setting controls whether users can discover contained objects such as tables through Global Search and the Search API. The setting is enabled by default, allowing users to find supported objects when they have access to the parent item. When it is disabled, search results are limited to top-level Fabric items, such as lakehouses and reports. Disabling the setting does not change access to the underlying items or data; it changes whether contained objects appear in search. Additional search improvements Apart from table discovery, this release also introduces several improvements in search for Microsoft Fabric items. Get richer item results New filters and expanded metadata make item results more precise and useful. You can: Return and filter item results by endorsement status, workspace ID, and sensitivity label ID. Find dataflows and dashboards in search results. Identify reports and dashboards included in a workspace app and retrieve the relevant app context. This context helps you distinguish between similar items, apply governance signals, and choose the right result without making additional requests. These improvements provide more ways to narrow large result sets and enough context to understand where an item belongs before you open it. Refine searches with new operators New query operators give you more control over how search interprets your terms: Use quotation marks (" ") to search for an exact phrase. Use an asterisk (*) as a wildcard for multiple characters. Use a question mark (?) as a wildcard for a single character. Use double ampersands (&&) to return only results that contain all specified terms. These operators help you narrow results when names are similar or when you know only part of a name. Prepare for rollout Tables are the starting point for discovery below the Fabric item level. The same catalog model can support more object types over time while preserving parent context, permission trimming, and a consistent way to search. For now, table search addresses a common need: finding the table you want from its name, description, or an exact column-name match without already knowing where it lives. Table search will begin rolling out late September. Before rollout, administrators should review the tenant setting, and developers can prepare the search experiences they plan to offer: Review the Users can find objects in search tenant setting in the Fabric admin portal and decide whether object discovery should be enabled for your organization. Explore the OneLake Catalog Search API if you build data portals, governance tools, or other discovery experiences. Connect an AI development tool through a Fabric MCP server or install the search skill from the Fabric Skills library to prepare agentic discovery workflows.SharePoint and OneDrive Shortcuts in OneLake (Generally Available)
For most enterprises, the largest and fastest-growing repository of business-critical information is SharePoint and OneDrive. Contracts, financial models, project plans, meeting notes, presentations, and compliance documentation accumulate across every team and department, rich with context that rarely makes it into an analytics workflow. This content has traditionally been invisible to data platforms. Bringing it into a data lake meant building pipelines, scheduling exports, managing duplicates, and reconciling governance across two separate systems. The result: valuable knowledge stayed locked in productivity tools while analytics teams worked with an incomplete picture. Now, SharePoint and OneDrive Shortcuts in Microsoft Fabric OneLake is now generally available. With this capability, organizations can now surface this data directly inside OneLake, without requiring traditional data movement pipelines in many common scenarios. Files stay exactly where they are in SharePoint or OneDrive, and Fabric workloads see them as a native part of the data lake. This is more than a connectivity feature. It is the bridge between the world where work happens and the world where data is analyzed. Sales forecasts stored in Excel can be joined with CRM transactions. Legal documents can be indexed and grounded in AI agents. Financial trackers can feed Power BI reports the moment they are updated. This helps reduce the boundary between collaboration content and enterprise analytics workflows. Customer Use Cases Organizations across industries are already finding practical, high-value ways to connect their Microsoft 365 content with Fabric analytics and AI. Below are example scenarios that illustrate potential use cases. Data Lake Unification Without Migration Many organizations maintain structured data in their Lakehouse alongside a parallel, untouched archive of unstructured content in SharePoint. Shortcuts eliminate the need to choose between the two. Data engineering teams can now create a unified view across both sources inside OneLake, enabling joins, aggregations, and AI workflows that span the full breadth of enterprise knowledge, without moving a single file. Combine SharePoint-hosted reference tables with Lakehouse transactional data in a single Spark notebook. Surface operational documents alongside structured metrics in a single Power BI semantic model. Avoid costly migration projects by referencing content in place and retiring redundant sync processes. Self-Service Analytics on Shared Documents Finance, HR, and operations teams frequently maintain planning workbooks, trackers, and reports in OneDrive and SharePoint. These files are updated regularly by business users who have no need or desire to interact with a data platform directly. Shortcuts let analysts consume this content without asking anyone to change how they work. Finance teams can surface quarterly budget workbooks directly in Power BI without any export step. HR can connect headcount trackers and org charts to workforce analytics dashboards. Operations teams can make procurement logs and vendor documents queryable alongside ERP data. Microsoft Fabric and Foundry: AI at Enterprise Scale For organizations building production-grade AI solutions on Azure, the combination of Fabric OneLake and Microsoft Foundry creates a powerful foundation. Shortcuts ensure that the rich, unstructured knowledge stored in SharePoint and OneDrive is available as a live, governed data source for Foundry-based agents and copilots. Connect SharePoint document libraries to Foundry knowledge stores without a separate ingestion pipeline. Keep AI knowledge grounding current automatically as SharePoint content is updated by business teams. Apply Fabric data transformations to prepare document content for structured AI consumption at scale. Features Supported in General Availability The following capabilities are generally available and supported for production use as of this release: Core Shortcut Capabilities Create shortcuts from any Fabric Lakehouse directly to OneDrive folders or SharePoint document libraries. Access files in place, without requiring explicit data duplication in many scenarios. Files remain governed by their existing SharePoint and OneDrive permissions. Live synchronization ensures that as content is added or updated in SharePoint or OneDrive, Fabric workloads see the latest version automatically. Shortcut Transformations Beyond simple file access, OneLake shortcuts include an optional transformation step that converts supported file types directly into Delta tables. This can reduce or eliminate the need for a separate ETL pipeline in supported scenarios, making document-resident data queryable by analytics engines. Supported file formats for transformation at GA: CSV, Parquet, and JSON. Transformed tables are kept in sync as new files arrive in the connected SharePoint or OneDrive folder. Transformed output integrates natively with Fabric warehouses, notebooks, and Power BI, enabling immediate analytics without additional data engineering. This capability fundamentally changes how customers are working with file-based data. A SharePoint folder containing hundreds of CSV exports from a business application can be transformed into a queryable Delta table in minutes, with minimal pipeline code required in most scenarios. General Availability Improvements In addition to the core shortcut capability, this GA release introduces two significant platform-level improvements that make shortcuts enterprise-ready for automation, scale, and cross-organizational scenarios. Service Principal and Workspace Identity Authentication OneDrive and SharePoint Shortcuts now support Service Principal (SPN) and Workspace Identity (WI) authentication, in addition to organizational account sign-in. This is a critical capability for production deployments. Reduces dependency on individual user credentials, preventing pipeline failures when team members change roles or leave the organization. Authentication is managed through Microsoft Entra ID, enabling consistent security governance and audit trails. SPN and WI authentication support higher API limits, directly reducing throttling in high-throughput scenarios. Cross-tenant access: service principals can be configured to access SharePoint and OneDrive content across organizational boundaries, enabling configurable partner and subsidiary data sharing scenarios, subject to tenant policies and security configurations. Metadata Caching and Performance OneLake now caches SharePoint metadata internally, reducing the frequency and impact of calls to the SharePoint API during query execution. This improvement can deliver the following benefits: Reduced throttling under high query volumes, particularly in multi-user or scheduled workload scenarios. Improved query performance for workloads that enumerate or filter large SharePoint folder structures. These improvements make shortcuts viable for production-grade pipelines and scheduled refresh scenarios that would previously have encountered reliability issues at scale. How to Get Started Creating a SharePoint or OneDrive shortcut in Fabric takes less than five minutes. The following steps apply to any Fabric workspace with at least one Lakehouse: Open a Lakehouse in your Fabric workspace. In the Explorer pane, right-click any folder and select New shortcut. In the New Shortcut dialog, select OneDrive or SharePoint tile from the list of external sources. Choose your authentication method: Organizational account for interactive scenarios, or Workspace Identity / Service Principal for automated and production workflows. Provide the SharePoint site URL and select or create a connection. If you don’t have root level access and prefer to provide the path directly, change the view to Path View by navigating to the top right corner. Browse to the folder or library you want to connect. Select one or more target locations and select Next. On the Transform page, choose whether to apply a transformation to convert supported file types (CSV, Parquet, JSON) into Delta tables. Select Skip if you only need file access. Select Create to finalize. Your shortcuts will appear immediately in the Lakehouse Explorer. From there, you can reference them in Spark notebooks, build Power BI reports directly on the data, run SQL queries through the Lakehouse SQL endpoint, or include them in Fabric pipelines and AI workflows. Learn more by exploring Create a OneDrive or SharePoint shortcut (Microsoft Learn documentation). We are excited to see what you build SharePoint and OneDrive Shortcuts in OneLake represent a step toward a world where every document in your organization is an active participant in your data and AI strategy. As the boundary between productivity and analytics continues to dissolve, Fabric is designed to be the platform that connects them. Share your feedback, use cases, and questions in the Microsoft Fabric Community forums. Your input directly shapes the roadmap.Fabric data agents in Microsoft Foundry: Easier to connect, easier to trust
Last year, we announced the integration between Fabric data agent and Microsoft Foundry. The idea was simple: build a data agent in Fabric on top of your data in Fabric OneLake, then bring that agent into Foundry so your Foundry agents can answer questions grounded in your enterprise data. That foundation helped teams connect their data to AI agents, and your feedback highlighted two areas where we could make the experience better: simplifying how to connect a Foundry agent to Fabric data agents and improving visibility into how they operate once deployed. Today, we’re introducing a set of enhancements that make Fabric data agents easier to discover, integrate, and monitor in production. This update helps organizations build AI agents that can access trusted business data with greater confidence and transparency. Bringing Fabric data agents into Foundry through Model Context Protocol Fabric data agents now integrate with Foundry through Model Concept Protocol (MCP), an emerging industry standard for agent-to-tool communication. This means your Fabric data agents appear as tools that Foundry agents can invoke when they need access to enterpise data that lives in Fabric OneLake. When your Foundry agent decides it needs data, it calls the Fabric data agent the same way it would call any other tool. MCP provides a standard way to connect Fabric data agents and Foundry agents, making the integration easier to extend over time without requiring changes to how you configure it. Importantly, this means organizations can combine the strengths of both platforms: Fabric provides trusted, governed business context, while Foundry provides the platform for building and orchestrating agents. Together, they enable developers to create AI experiences grounded in enterprise data. Discover and add agents from the OneLake Catalog One of the most common pieces of feedback we received was that connecting a Fabric data agent required too much manual work. Previously, builders needed to locate workspace IDs and artifact IDs before they could connect a data agent to their Foundry agent. If you had more than one agent, or you were not the person who built it, tracking down those IDs was cumbersome. The following figures display the previous experience of selecting the Fabric Data Agent tool and filling in the IDs manually. Figure: The old experience: selecting the Fabric Data Agent tool. Figure: The old experience: entering the workspace ID and artifact ID manually. Now, data agents can be discovered directly through the OneLake Catalog experience. You simply add the Fabric IQ (OneLake Catalog) tool, filter for data agents, and pick the ones you want. The following figures display the new experience. Figure: The new experience: adding the Fabric data agent via the Fabric IQ (OneLake Catalog) tool. Figure: The new experience: browsing data agents in the OneLake Catalog and adding them. No more searching for IDs. You see the agents by name, along with their location and sensitivity, and you add them with a click. If you have permission to use an agent, it’s right there in the list. Connect multiple data agents to a single Foundry agent Business questions rarely live within a single domain. The previous integration allowed only one Fabric data agent per Foundry agent. The limitation often forced builders to choose which data source to prioritize. Now, you can connect multiple Fabric data agents to a single Foundry agent, allowing the agent to draw from specialized sources of business knowledge. For example, a Foundry agent can simultaneously leverage a sales analytics agent, a supply chain agent, and a customer support agent, selecting the most relevant source based on the user’s question. Tracing and logs through Foundry Observability Adding a data agent is one thing. Understanding what it does once it is running is another, and that is where the second part of this update comes in. When you add a Fabric data agent to your Foundry agent, you can now view logs and traces through Foundry Observability. This provides insight into how requests move through the agent workflow, including which tools were invoked, how long individual steps took, and what results were returned. Figure: Foundry agent trace with the Fabric data agent's tool calls, input, and output. Observability is important because agents operate as chains of decisions rather than single transactions. When an answer appears incorrect or a workflow becomes slow, builders need visibility into the entire execution path, not just the final output. Traces show you the path a request took and where time was spent, while logs provide detailed records of what occurred during execution. As organizations move agents from experimentation to production, this level of visibility becomes critical. Teams need reliable ways to troubleshoot issues, understand agent behavior, and maintain confidence in the systems they deploy. Foundry Observability helps makes that possible. Figure: Span metadata for a single tool call: timing, status, and data source. Where we’re headed Our goal is simple: help organizations build AI agents they can confidently run in production. That means making enterprise knowledge easier to discover, simplifying how agents connect to trusted business data, and providing the observability needed to understand and troubleshoot agent behavior at scale. With MCP-based integration, OneLake Catalog discovery, support for multiple data agents, and deeper observability, we’re continuing to enhance Fabric and Foundry as a unified foundation for enterprise AI. This update is rolling out to all regions over the coming days. To learn more about connecting Fabric data agents with Microsoft Foundry, explore the Observability for Fabric data agents in Microsoft Foundry documentation.What’s new for Dataverse Fabric Link: More control, stronger security, and fresher data
Every organization runs operational data: customer interactions, sales activity, service cases, finance records, inventory movements, approvals, and custom business processes. Much of that data lives in Microsoft Dataverse, the data platform behind Power Platform and many Dynamics 365 applications. Dataverse Fabric Link connects a Dataverse environment to Microsoft Fabric by creating an analytics-ready representation of selected tables in OneLake. Dataverse shortcuts make that data available across Fabric experiences without requiring customers to build and maintain custom ETL pipelines. Based on customer feedback, recent updates focus on giving administrators more control over linked data, improving authentication options, reducing data latency between Dataverse and Fabric, and making the experience easier to manage. What’s improved Choose only the tables you need What changed: Admins can now manage which Dataverse and finance and operations app tables are linked to Fabric. Instead of bringing every table into the Fabric workspace, teams can add or remove tables based on the analytics scenario they are building. Customer benefit: This gives administrators more control over cost, workspace complexity, and downstream data model design. Only selected tables consume storage for the Fabric link, and removing a table stops synchronization and removes the shortcut without deleting the source table in Dataverse. How to get started: In Power Apps, open Azure Synapse Link, select the Link to Fabric profile, and use Manage tables to add or remove tables. During new Link to Fabric setup, use the Select Entities step to choose the tables you want to sync. Review downstream reports, semantic models, and pipelines after changing table selection. For more information, refer to the Link to Fabric documentation. Use Workspace Identity and Service Principals for production deployments What changed: Dataverse Fabric Link now supports Workspace Identity and Service Principal authentication in addition to organizational account credentials. Workspace Identity uses a managed identity associated with a Fabric workspace, while Service Principal supports customer-managed application identities. Customer benefit: These options reduce dependency on individual user credentials and make Dataverse-to-Fabric integrations easier to operate in production. They help avoid failures when employees change roles, passwords expire, or interactive credentials are no longer appropriate for automated analytics workloads. How to get started: For new links, choose Workspace Identity or Service Principal during the Link to Fabric wizard connection setup. For existing connections, create or identify the workspace identity or service principal, add it as an application user in Dataverse with the required role, then update the Dataverse connection in Fabric under Settings > Manage connections and gateways. Get fresher data with low-latency sync What changed: Previously, syncing data could take up to two hours. With low-latency sync, times are reduced to under 15 minutes for most observed cases, enabling near real-time reporting and analytics. This helps organizations use more current operational data for reporting, monitoring, and decision-making. Customer benefit: Fabric users get fresher Dataverse data, faster initial sync, faster incremental updates, and better throughput for large finance and operations app workloads. This reduces the gap between an operational transaction in Dataverse or Dynamics 365 and its availability in Fabric reports, lakehouses, and AI workloads. How to get started: Low-latency sync is now enabled in selected regions. For more details on getting started and regions where low-latency sync is enabled, refer to Announcing Low-latency sync for Dataverse to Fabric (Generally Available). What this means for Fabric users For Fabric users, these improvements make Dataverse data feel more like a first-class analytics source. You can bring operational data into OneLake with less custom integration work, manage table scope with more precision, rely on stronger authentication options, and reduce the delay between business activity and analytical insight. Most importantly, these changes help customers move from isolated operational reporting to unified business analytics. Dataverse data can be combined with data from Warehouses, Lakehouses, SharePoint, OneLake shortcuts, and other enterprise sources in Fabric, enabling richer reporting and more intelligent applications. Next Steps If you already use Dataverse Fabric Link: Review your current profile and decide whether you should refine table selection. Move to Workspace Identity or Service Principal authentication. Enable low-latency sync when it becomes available in your region. If you are new to integration, start with a focused scenario and choose the tables, workspace, and authentication model that best match that workload. To learn more, refer to the documentation Link to Fabric Create a Dataverse shortcut Dataverse overviewCross-workspace role management in the OneLake catalog (Preview)
We’re introducing cross-workspace role assignment in the Secure tab of the OneLake catalog. This new capability builds on the Secure tab’s unified view of workspace roles and OneLake security roles, making it easier for security administrators and data owners to manage access consistently across large Fabric estates. Instead of updating role memberships one workspace at a time, you can now assign people and groups to workspace roles across multiple workspaces in one streamlined workflow. What’s new Assign members to compatible OneLake security roles across multiple workspaces from a single experience in the Secure tab. Edit and remove role memberships across multiple workspaces without navigating to each workspace individually. How it works From the Secure tab in the OneLake catalog, admins can filter to the workspaces they manage and select workspace roles across those workspaces for bulk membership updates. This makes it easier to onboard new teams, align access for existing groups, and keep security policy implementation consistent as projects expand. Because the workflow is centralized in the catalog, teams can move from discovery to governance to access management without losing context. le workspaces in the OneLake catalog This capability is especially valuable for quarterly access reviews, environment rollouts, and standardized data product deployments. By managing role membership centrally, organizations can more easily enforce least-privilege access patterns, reduce configuration drift, and respond faster when access needs change. Get started Open the OneLake catalog in Microsoft Fabric and switch to the Secure tab to try cross-workspace role assignment. If you’re already using the Secure tab to view users, inspect security roles, and manage row-level or column-level protections, this enhancement extends that same centralized experience to broader access administration across workspaces. We’re excited to see how teams use it to simplify governance and secure their data estate at scale. Learn more about the OneLake catalog or the secure tab.
