Data fabric tables

  • Release version: Yokohama
  • Updated March 7, 2025
  • 3 minutes to read
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    Summary of Data fabric tables

    Data fabric tables on the ServiceNow AI Platform provide a virtual, read-only representation of external data sources directly within your instance. This capability allows you to access and view external data in lists and forms as if it were stored locally, without consuming instance storage or impacting performance. Access control for data fabric tables follows the same model as physical tables, ensuring secure data visibility.

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    Key Features

    • Real-time external data access: Fetch and display data from external sources instantly without importing it into your instance.
    • Storage and performance efficiency: Reduce local storage needs and minimize instance performance load by avoiding data duplication.
    • Controlled access: Manage user permissions to restrict who can view external data through familiar ServiceNow access controls.
    • Integration with AI tools: Use data fabric tables with AI Data Explorer for conversational data analysis and with Knowledge Graph to enhance AI-driven experiences such as Now Assist Virtual Agent and generative AI skills.
    • Flexible application data handling: Applications can choose between using physical tables with imported data or data fabric tables for real-time external data access.

    Practical Use Cases

    • Unified data view across multiple systems: For example, in manufacturing, combine local asset and maintenance data with real-time sensor data from an external data lake to enable proactive maintenance and prevent production outages.
    • Real-time data retrieval for applications: Provide administrators with options to either import data into physical tables or access live external data through data fabric tables, depending on operational needs.
    • AI-powered insights: Analyze and visualize external data using AI Data Explorer and improve AI agent performance by leveraging data fabric tables within the Knowledge Graph.

    Access and Management

    Data fabric tables are managed by users with roles containing dfdatasteward or connectionadmin. They are created and maintained in the Workflow Data Fabric Hub and can be filtered and searched by various criteria such as data source, connection status, and creator. Tables belong to the application scope designated by the data steward at creation.

    Important Considerations

    • Data fabric tables provide read-only access to external data; unlike remote tables, they do not support data modifications (insert, update, delete) in the external source.
    • To enable AI Data Explorer functionality, data fabric tables must be added to the Semantic Table Configuration within Query Generation.

    Fuel your AI agents and enrich workflows on the ServiceNow AI Platform with external data using data fabric tables.

    Key benefits

    • Fetch external data in real time and view the data in lists and forms as if it's stored in your instance.
    • Reduce storage consumption and performance load in your instance.
    • Control access to external data so that only authorized users can view the data.

    A data fabric table is a virtual representation of data stored in an external source, accessible directly from the ServiceNow AI Platform. The data fabric table definition is stored in the ServiceNow AI Platform, but its external records live in the memory in read-only mode. You can view external records from a data fabric table in lists and forms the same way you view records in a physical table.

    Access to a data fabric table is controlled the same way access is controlled to a physical table. Data fabric tables belong to the application scope selected by the data steward during their creation.

    Figure 1. Data fabric tables in Workflow Data Fabric Hub
    A list of your data fabric tables.

    Required ServiceNow AI Platform roles

    A role containing the df_data_steward role or the connection_admin role is required to create and manage data fabric tables.

    Accessing data fabric tables

    View and manage data fabric tables on the Data fabric tables tab by navigating to Admin > Workflow Data Fabric Hub > Data fabric tables or All > Workflow Data Fabric Hub > Data fabric tables.

    Viewing data fabric tables

    View a list of all the data fabric tables that data stewards have created on the Data fabric tables tab.

    • Search for a data fabric table by label or name.
    • Filter the list of tables by data source and connection.
    • Filter the list of tables by creator.
    • View a list of data fabric tables from active connections in the Active tab.
    • View a list of data fabric tables from connections that are deactivated or not configured in the Others tab.

    Use cases

    Unifying data from multiple sources
    A manufacturer experiences an outage when a key piece of machinery fails, stopping production entirely. Unfortunately, the data needed to help prevent these failures is scattered across multiple systems.
    • Asset inventory data in IT Asset Management (ITAM) and maintenance personnel data are stored locally in your instance.
    • Historical asset maintenance records and real-time sensor are stored in an external data lake.

    To help prevent potential failures and outages, you can provide service technicians with all the necessary data by connecting these systems using data fabric tables. For example:

    1. Sensor data is fed to the data lake and analyzed by machine learning, generating a failure score.
    2. When the failure score crosses a certain threshold, an alert is generated and sent to your instance.
    3. The alert triggers a maintenance request flow, creating a case assigned to a service technician.
    4. The technician reviews the case details and accesses inventory data from the instance, along with maintenance records and real-time sensor data from the external data lake, all in one place using data fabric tables.
    5. The technician makes an informed decision and acts to address the issue before another outage occurs.
    Retrieving real-time data in an application
    An application can include both a physical table and a data fabric version of the same table. This gives the instance admin flexibility when installing the application. The admin can choose whether to populate the physical table through data import or allow users to access real-time data from an external data source via the data fabric table.
    Analyzing data and generating AI-guided insights using AI Data Explorer
    Create visualizations and analyses of fetched data through a conversational interface in AI Data Explorer. For more information, see Use AI to explore data with AI Data Explorer.
    Note:
    You must first add the relevant data fabric tables to the Semantic Table Configuration [sn_query_gen_table_config] table in Query Generation. See Add a table to the semantic data layer.
    Enhancing the performance of AI experiences using the Knowledge Graph
    Enhance the performance of Now Assist Virtual Agent, AI agents, and generative AI skills by leveraging data fabric tables in the Knowledge Graph application. For more information, see Knowledge Graph.

    Differences between data fabric tables and remote tables

    Data fabric tables are similar to remote tables on the ServiceNow AI Platform, but data fabric tables query external data sources and retrieve data using a zero copy connection instead of a script.

    A data fabric table enables you to view external data, but you can't insert, update, or delete data in an external data source like you can from a remote table.