---
sourceDocument: Australia Workflow Data Fabric
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/integrate-applications

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Workflow Data Fabric

ft:clusterId :

    - crint

bundleId :

    - crint

workflow :

    - Creator


---

# Data fabric tables

# Data fabric tables {#ariaid-title1}

Release version: Australia  
Updated March 12, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 3 minutes to read
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![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Data fabric tables

Data fabric tables in the ServiceNow AI Platform allow you to access and display external data sources in real time as if the data were stored within your ServiceNow instance.
These tables provide a virtual, read-only representation of external records, reducing storage needs and performance impact on your instance while maintaining controlled access similar to physical tables.
Data fabric tables are defined and managed within the platform but keep external data in memory without copying it.
Show full answer Show less  

## Key Features

* **Real-time external data fetching:** Access external data instantly and view it seamlessly in lists and forms.
* **Reduced instance load:** Minimize storage consumption and performance overhead by avoiding data duplication.
* **Access control:** Secure external data visibility by managing permissions as you do with physical tables.
* **Role requirements:** Creating and managing data fabric tables requires roles including `dfdatasteward` or `connectionadmin`.
* **Management interface:** Use the Data fabric tables tab under Zero Copy Connector Hub to view, search, and filter tables by label, source, connection, or creator.
* **Integration with AI tools:** Data fabric tables can feed AI Data Explorer for visualizations and the Knowledge Graph to enhance AI agent performance.
* **Comparison with remote tables:** Unlike remote tables, data fabric tables use zero copy connections for querying external data and support only read-only operations.

## Practical Use Cases

* **Unifying data from multiple sources:** Combine internal instance data (like asset inventory) with external data lakes (like sensor data and maintenance records) to empower technicians with comprehensive, real-time information for proactive maintenance and outage prevention.
* **Real-time application data:** Provide application flexibility by installing both physical and corresponding data fabric tables, enabling direct access to live external data or importing data into physical tables as needed.
* **AI-driven insights:** Use data fabric tables in AI Data Explorer to analyze external data conversationally and generate actionable insights, enhancing decision-making workflows.
* **Improved AI agent performance:** Leverage external data via data fabric tables in the Knowledge Graph to improve Now Assist Virtual Agent and generative AI capabilities.  
Fuel your AI agents and enrich workflows on the ServiceNow AI Platform with external data using data fabric tables.

## Key benefits {#data-fabric-tables-zcc__section_kwx_whg_p2c}

* 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.

{#data-fabric-tables-zcc__ul_ncr_yhg_p2c}

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

## Required ServiceNow AI Platform roles {#data-fabric-tables-zcc__section_vbd_c3g_p2c}

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 {#data-fabric-tables-zcc__section_z3l_c3g_p2c}

View and manage data fabric tables on the Data fabric tables tab by navigating to AdminZero Copy Connector HubData fabric tables or AllZero Copy Connector HubData fabric tables.

## Viewing data fabric tables {#data-fabric-tables-zcc__id_t5h_4qz_vfc}

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.
{#data-fabric-tables-zcc__ul_gwh_pxd_kfc}

## Use cases {#data-fabric-tables-zcc__section_y5q_c3g_p2c}

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.

    {#data-fabric-tables-zcc__ul_znp_3td_y2c}

    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. They also access maintenance records and real-time sensor data from the external data lake. The technician does all these things in one place using data fabric tables.
    5. The technician makes an informed decision and acts to address the issue before another outage occurs.
    {#data-fabric-tables-zcc__ol_j13_15t_bfc}

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 duality gives the instance admin flexibility when installing the application. Via the data fabric table, the admin can populate
    the physical table through data import or allow users to access real-time data from an external data source.

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](https://www.servicenow.com/docs/access?context=ai-data-explorer&version=australia&pubname=australia-now-intelligence&ft:locale=en-US).  
    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](https://www.servicenow.com/docs/access?context=add-table-semantic-layer&version=australia&pubname=australia-now-intelligence&ft:locale=en-US).

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](https://www.servicenow.com/docs/access?context=knowledge-graph-landing&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US).

## Differences between data fabric tables and remote tables {#data-fabric-tables-zcc__section_qnt_p4n_52c}

Data fabric tables are similar to remote tables on the ServiceNow AI Platform. However, 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. However, you can't insert, update, or delete data in an external data source like you can from a remote table.

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