> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getcollate.io/llms.txt
> Use this file to discover all available pages before exploring further.

# BigQuery | Collate Connector Setup & Integration Guide

> Connect BigQuery to Collate seamlessly with our comprehensive database connector guide. Setup instructions, configuration tips, and metadata extraction workflows.

export const MetadataIngestionUi = ({connector, selectServicePath, addNewServicePath, serviceConnectionPath}) => {
  return <>
   <p>
      To ingest metadata from your sources, you need to create a service connection.
      The service connects your source system with Collate. Once you create
      a service, you can use it to configure your ingestion workflows.<br />
      <br />
      To create a service connection and ingest your metadata, follow the steps below:
  </p>
  <Steps>
    <Step title="Select the Service">
    <ol>
          <li>
            On the left navigation bar, click <strong>Settings</strong>.
          </li>
          <li>
            On the next page, click <strong>Services</strong>, and then select the service.
            <img src="/public/images/connectors/visit-services-page.png" alt="Visit Services Page" />
          </li>
    </ol>
   </Step>


   <Step title="Create a New Service">
      
       To add a new service connection, click <strong>Add New Service</strong>.
      <img src="/public/images/connectors/create-new-service.png" alt="Create a new Service" />


   </Step>


     <Step title="Select the Connector">
       Select <strong>{connector}</strong> as the service type and click <strong>Next</strong>.


       {selectServicePath && <img src={selectServicePath} alt="Select Service" />}
   </Step>


   <Step title="Name and Describe your Service">
       Enter a unique, descriptive <strong>Service Name</strong> and <strong>Description</strong>.
       <ul>
         <li><strong>Service Name</strong>: Collate identifies services by their service name. Enter a name that distinguishes this deployment from other services, including other {connector} services you are ingesting metadata from.</li>
       </ul>


       <Note>
           The service name cannot be changed after it is set.
       </Note>


       {addNewServicePath && <img src={addNewServicePath} alt="Add New Service" />}
   </Step>


   <Step title="Configure the Service Connection">
       Set up the connection settings required for {connector}. <br /><br />
      
       Configure the following connection options to set up the service and start ingesting metadata from your sources. The right-hand panel displays help documentation for the selected connection type in the product UI

       {serviceConnectionPath && <img src={serviceConnectionPath} alt="Configure Service connection" />}
   </Step>
   </Steps>
   </>;
};

export const ConnectorDetailsHeader = ({name, icon, stage, availableFeatures, unavailableFeatures = [], availableFeaturesCollate = []}) => {
  const showSubHeading = availableFeatures?.length > 0 || unavailableFeatures?.length > 0 || availableFeaturesCollate?.length > 0;
  const totalAvailableFeatures = [...availableFeatures || [], ...availableFeaturesCollate || []];
  return <div className="container">
      <div className="Heading">
        <div className="flex items-center gap-3">
          {icon && <div className="IconContainer">
              <img src={icon} alt={name} noZoom className="ConnectorIcon" />
            </div>}
          <h1 className="ConnectorName">{name}</h1>
          <span className={`StageBadge ${stage === 'PROD' ? 'prod' : 'beta'}`}>
            {stage}
          </span>
        </div>
      </div>
      {showSubHeading && <div className="SubHeading">
          <div className="FeaturesHeading">Feature List</div>
          <div className="FeaturesList">
            {totalAvailableFeatures.map(feature => <div className="FeatureTag AvailableFeature" key={feature}>
                ✓ {feature}
              </div>)}
            {unavailableFeatures.map(feature => <div className="FeatureTag UnavailableFeature" key={feature}>
                ✕ {feature}
              </div>)}
          </div>
        </div>}
    </div>;
};

<ConnectorDetailsHeader icon="/public/images/connectors/bigquery.webp" name="BigQuery" stage="PROD" availableFeatures={["Metadata", "Query Usage", "Lineage", "Column-level Lineage", "Data Profiler", "Data Quality", "dbt", "Tags", "Stored Procedures", "Sample Data", "Reverse Metadata (Collate Only)", "Auto-Classification"]} unavailableFeatures={["Owners"]} />

In this section, we provide guides and references to use the BigQuery connector.
Configure and schedule BigQuery metadata and profiler workflows from the OpenMetadata UI:

* [Requirements](#requirements)
* [Metadata Ingestion](#metadata-ingestion)
  * [Incremental Extraction](/connectors/ingestion/workflows/metadata/incremental-extraction/bigquery)
* [Query Usage](/connectors/ingestion/workflows/usage)
* [Data Profiler](/how-to-guides/data-quality-observability/profiler/profiler-workflow)
* [Data Quality](/how-to-guides/data-quality-observability/quality/configure)
* [Lineage](/connectors/ingestion/lineage)
* [dbt Integration](/connectors/database/dbt)
* [Troubleshooting](/connectors/database/bigquery/troubleshooting)
* [Reverse Metadata](#reverse-metadata)

## How to Run the Connector Externally

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with
custom Airflow plugins to handle the workflow deployment.

If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check
the following docs to run the Ingestion Framework **anywhere**.

<Columns cols={2}>
  <Card title="External Schedulers" href="https://docs.open-metadata.org/v1.11.x/deployment/ingestion">
    Get more information about running the Ingestion Framework Externally
  </Card>
</Columns>

## Requirements

You need to create an service account in order to ingest metadata from bigquery refer [this](/connectors/database/bigquery/create-credentials) guide on how to create service account.

<Columns cols={2}>
  <Card title="Create Custom GCP Role" href="/connectors/database/bigquery/create-credentials">
    Check out this documentation on how to create a custom role and assign it to the service account.
  </Card>
</Columns>

## Partitioned Tables

When profiling **partitioned tables** in BigQuery, OpenMetadata applies a **default partition query duration of 1 day** for time-based partitions. This conservative setting prevents excessive data scans but may result in no **Sample Data** or **Column Profile Metrics** if no data falls within the default window.

## Resolution

You can adjust this behavior directly from the UI:

1. **Navigate to the table's detail page.**
2. **Edit the profiler configuration.**
3. **Update the `partitionQueryDuration`** under **Partition Config** to a wider window (e.g., 30 days) as needed.

<img src="https://mintcdn.com/collatedocs/XRLyE3HymqoH13_i/public/images/connectors/bigquery/partitioned-tables.gif?s=729e33c5cd07f9aadc39ee1c76e9b174" alt="Partitioned Tables" width="1456" height="780" data-path="public/images/connectors/bigquery/partitioned-tables.gif" />

This change allows OpenMetadata to access a broader data range during profiling and sample data collection, resolving the issue for partitioned tables.

### Data Catalog API Permissions

* Go to [https://console.cloud.google.com/apis/library/datacatalog.googleapis.com](https://console.cloud.google.com/apis/library/datacatalog.googleapis.com)
* Select the `GCP Project ID` that you want to enable the `Data Catalog API` on.
* Click on `Enable API` which will enable the data catalog api on the respective project.

<Tip>
  Access to the Google Data Catalog API is optional and only required if you want to retrieve policy tags from BigQuery. The BigQuery connector does not require this permission for general metadata ingestion.
</Tip>

### GCP Permissions

To execute metadata extraction and usage workflow successfully the user or the service account should have enough access to fetch required data. Following table describes the minimum required permissions

| #  | GCP Permission                | Required For                      |
| :- | :---------------------------- | :-------------------------------- |
| 1  | bigquery.datasets.get         | Metadata Ingestion                |
| 2  | bigquery.tables.get           | Metadata Ingestion                |
| 3  | bigquery.tables.getData       | Metadata Ingestion                |
| 4  | bigquery.tables.list          | Metadata Ingestion                |
| 5  | resourcemanager.projects.get  | Metadata Ingestion                |
| 6  | bigquery.jobs.create          | Metadata Ingestion                |
| 7  | bigquery.jobs.listAll         | Metadata Ingestion                |
| 8  | bigquery.routines.get         | Stored Procedure                  |
| 9  | bigquery.routines.list        | Stored Procedure                  |
| 10 | datacatalog.taxonomies.get    | Fetch Policy Tags                 |
| 11 | datacatalog.taxonomies.list   | Fetch Policy Tags                 |
| 12 | bigquery.readsessions.create  | Bigquery Usage & Lineage Workflow |
| 13 | bigquery.readsessions.getData | Bigquery Usage & Lineage Workflow |
| 14 | logging.operations.list       | Incremental Metadata Ingestion    |

<Tip>
  If the user has `External Tables`, please attach relevant permissions needed for external tables, alongwith the above list of permissions.
</Tip>

<Tip>
  If you are using BigQuery and have sharded tables, you might want to consider using partitioned tables instead. Partitioned tables allow you to efficiently query data by date or other criteria, without having to manage multiple tables. Partitioned tables also have lower storage and query costs than sharded tables.
  You can learn more about the benefits of partitioned tables [here](https://cloud.google.com/bigquery/docs/partitioned-tables#dt_partition_shard).
  If you want to convert your existing sharded tables to partitioned tables, you can follow the steps in this [guide](https://cloud.google.com/bigquery/docs/creating-partitioned-tables#convert-date-sharded-tables).
  This will help you simplify your data management and optimize your performance in BigQuery.
</Tip>

## Metadata Ingestion

<MetadataIngestionUi connector={"BigQuery"} selectServicePath={"/public/images/connectors/bigquery/select-service.png"} addNewServicePath={"/public/images/connectors/bigquery/add-new-service.png"} serviceConnectionPath={"/public/images/connectors/bigquery/service-connection.png"} />

# Connection Options

<Steps>
  <Step title="Connection Options">
    **Host and Port**: BigQuery APIs URL. By default, the API URL is `bigquery.googleapis.com` you can modify this if you have custom implementation of BigQuery.
    **GCP Credentials**:
    You can authenticate with your bigquery instance using either `GCP Credentials Path` where you can specify the file path of the service account key or you can pass the values directly by choosing the `GCP Credentials Values` from the service account key file.
    You can check out [this](https://cloud.google.com/iam/docs/keys-create-delete#iam-service-account-keys-create-console) documentation on how to create the service account keys and download it.
    **GCP Credentials Values**: Passing the raw credential values provided by BigQuery. This requires us to provide the following information, all provided by BigQuery:

    * **Credentials type**: Credentials Type is the type of the account, for a service account the value of this field is `service_account`. To fetch this key, look for the value associated with the `type` key in the service account key file.
    * **Project ID**: The GCP project ID (or list of project IDs) that Collate should **read metadata from** — i.e., the project(s) containing the BigQuery datasets you want to catalog. To fetch this key, look for the value associated with the `project_id` key in the service account key file. You can also pass multiple project IDs to ingest metadata from different BigQuery projects into one service.
    * **Billing Project ID (Optional)**: The GCP project that will be **charged for the BigQuery jobs** Collate runs (metadata, usage, and lineage queries). This is separate from the data project(s) above. In simple setups where your data and billing are in the same project, you can leave this blank or set it to the same value as **Project ID**. Set it explicitly when your organization uses a centralized billing project, a shared service account that spans multiple data projects, or when the service account's home project should not receive the query charges.
    * **Private Key ID**: This is a unique identifier for the private key associated with the service account. To fetch this key, look for the value associated with the `private_key_id` key in the service account file.
    * **Private Key**: This is the private key associated with the service account that is used to authenticate and authorize access to BigQuery. To fetch this key, look for the value associated with the `private_key` key in the service account file.
    * **Client Email**: This is the email address associated with the service account. To fetch this key, look for the value associated with the `client_email` key in the service account key file.
    * **Client ID**: This is a unique identifier for the service account. To fetch this key, look for the value associated with the `client_id` key in the service account key  file.
    * **Auth URI**: This is the URI for the authorization server. To fetch this key, look for the value associated with the `auth_uri` key in the service account key file. The default value to Auth URI is [https://accounts.google.com/o/oauth2/auth](https://accounts.google.com/o/oauth2/auth).
    * **Token URI**: The Google Cloud Token URI is a specific endpoint used to obtain an OAuth 2.0 access token from the Google Cloud IAM service. This token allows you to authenticate and access various Google Cloud resources and APIs that require authorization. To fetch this key, look for the value associated with the `token_uri` key in the service account credentials file. Default Value to Token URI is [https://oauth2.googleapis.com/token](https://oauth2.googleapis.com/token).
    * **Authentication Provider X509 Certificate URL**: This is the URL of the certificate that verifies the authenticity of the authorization server. To fetch this key, look for the value associated with the `auth_provider_x509_cert_url` key in the service account key file. The Default value for Auth Provider X509Cert URL is [https://www.googleapis.com/oauth2/v1/certs](https://www.googleapis.com/oauth2/v1/certs)
    * **Client X509Cert URL**: This is the URL of the certificate that verifies the authenticity of the service account. To fetch this key, look for the value associated with the `client_x509_cert_url` key in the service account key  file.
      **GCP Credentials Path**: Passing a local file path that contains the credentials.
      **Taxonomy Project ID (Optional)**: Bigquery uses taxonomies to create hierarchical groups of policy tags. To apply access controls to BigQuery columns, tag the columns with policy tags. Learn more about how yo can create policy tags and set up column-level access control [here](https://cloud.google.com/bigquery/docs/column-level-security)
      If you have attached policy tags to the columns of table available in Bigquery, then OpenMetadata will fetch those tags and attach it to the respective columns.
      In this field you need to specify the id of project in which the taxonomy was created.
      **Taxonomy Location (Optional)**: Bigquery uses taxonomies to create hierarchical groups of policy tags. To apply access controls to BigQuery columns, tag the columns with policy tags. Learn more about how yo can create policy tags and set up column-level access control [here](https://cloud.google.com/bigquery/docs/column-level-security)
      If you have attached policy tags to the columns of table available in Bigquery, then OpenMetadata will fetch those tags and attach it to the respective columns.
      In this field you need to specify the location/region in which the taxonomy was created.
      **Usage Location (Optional)**:
      Location used to query `INFORMATION_SCHEMA.JOBS_BY_PROJECT` to fetch usage data. You can pass multi-regions, such as `us` or `eu`, or your specific region such as `us-east1`. Australia and Asia multi-regions are not yet supported.
      **Cost Per TiB (Optional)**:
      The cost (in USD) per tebibyte (TiB) of data processed during BigQuery usage analysis. This value is used to estimate query costs when analyzing usage metrics from `INFORMATION_SCHEMA.JOBS_BY_PROJECT`.
      This setting does **not** affect actual billing—it is only used for internal reporting and visualization of estimated costs.
      The default value, if not set, may assume the standard on-demand BigQuery pricing (e.g., \$5.00 per TiB), but you should adjust it according to your organization's negotiated rates or flat-rate pricing model.

    <Tip>
      **Application Default Credentials (ADC) Authentication**
      If you want to use [ADC authentication](https://cloud.google.com/docs/authentication#adc) for BigQuery, configure the GCP credentials with type `gcp_adc`:

      ```yaml theme={null}
      credentials:
        gcpConfig:
          type: gcp_adc
          projectId: ["your-data-project-id"]  # The project(s) to scan metadata from
      ```

      **Using ADC with Billing Project ID**: When using ADC authentication, you can still specify a **Billing Project ID** at the connection level to control which project is charged for the BigQuery jobs Collate runs. This is useful when:

      * Your service account has access to multiple data projects but billing should go to a single project
      * You want to separate data access from billing attribution
      * The ADC identity is not associated with a single billable project

      **ADC Setup**: ADC authentication works automatically when running in Google Cloud environments (GKE, Compute Engine, Cloud Run) or when you've configured it locally using `gcloud auth application-default login`.
    </Tip>
  </Step>

  <Step title="Advanced Configuration">
    Database Services have an Advanced Configuration section, where you can pass extra arguments to the connector
    and, if needed, change the connection Scheme.

    This would only be required to handle advanced connectivity scenarios or customizations.

    * **Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to database during the connection. These details must be added as Key-Value pairs.
    * **Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent during the connection. These details must be added as Key-Value pairs.

          <img src="https://mintcdn.com/collatedocs/lyQrThlmh8cYozqe/public/images/connectors/advanced-configuration.png?fit=max&auto=format&n=lyQrThlmh8cYozqe&q=85&s=5c8159f3c92aa4501c116c5f4f2139ae" alt="Advanced Configuration" width="1398" height="534" data-path="public/images/connectors/advanced-configuration.png" />
  </Step>

  <Step title="Test the Connection">
    Once the credentials have been added, click on *Test Connection* and *Save* the changes.

    <img src="https://mintcdn.com/collatedocs/L7psA65ao88vmcRI/public/images/connectors/test-connection.png?fit=max&auto=format&n=L7psA65ao88vmcRI&q=85&s=2133f0d65f18df1e57f69d2cc3bdeff4" alt="Test Connection" width="1494" height="310" data-path="public/images/connectors/test-connection.png" />
  </Step>

  <Step title="Configure Metadata Ingestion">
    In this step we will configure the metadata ingestion pipeline,
    Please follow the instructions below

    <img src="https://mintcdn.com/collatedocs/12mtkhFtvTP7FUxZ/public/images/connectors/configure-metadata-ingestion-database-1.png?fit=max&auto=format&n=12mtkhFtvTP7FUxZ&q=85&s=43bd09ba1eeee6f5c7a8100fbcac9f65" alt="Configure Metadata Ingestion" width="1327" height="1271" data-path="public/images/connectors/configure-metadata-ingestion-database-1.png" />

    <img src="https://mintcdn.com/collatedocs/12mtkhFtvTP7FUxZ/public/images/connectors/configure-metadata-ingestion-database-2.png?fit=max&auto=format&n=12mtkhFtvTP7FUxZ&q=85&s=4a62a34c638864218a356c7371851761" alt="Configure Metadata Ingestion" width="1327" height="1271" data-path="public/images/connectors/configure-metadata-ingestion-database-2.png" />

    #### Metadata Ingestion Options

    <Tip>
      If the owner's name is openmetadata, you need to enter `openmetadata@domain.com` in the name section of add team/user form, click [here](/connectors/database/dbt/ingest-dbt-owner#following-steps-shows-adding-a-user-to-openmetadata) for more info.
    </Tip>

    * **Name**: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.

    * **Database Filter Pattern (Optional)**: Use to database filter patterns to control whether or not to include database as part of metadata ingestion.
      * **Include**: Explicitly include databases by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all databases with names matching one or more of the supplied regular expressions. All other databases will be excluded.
      * **Exclude**: Explicitly exclude databases by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all databases with names matching one or more of the supplied regular expressions. All other databases will be included.

    * **Schema Filter Pattern (Optional)**: Use to schema filter patterns to control whether to include schemas as part of metadata ingestion.
      * **Include**: Explicitly include schemas by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all schemas with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
      * **Exclude**: Explicitly exclude schemas by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all schemas with names matching one or more of the supplied regular expressions. All other schemas will be included.

    * **Table Filter Pattern (Optional)**: Use to table filter patterns to control whether to include tables as part of metadata ingestion.
      * **Include**: Explicitly include tables by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded.
      * **Exclude**: Explicitly exclude tables by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included.

    * **Enable Debug Log (toggle)**: Set the Enable Debug Log toggle to set the default log level to debug.

    * **Mark Deleted Tables (toggle)**: Set the Mark Deleted Tables toggle to flag tables as soft-deleted if they are not present anymore in the source system.

    * **Mark Deleted Tables from Filter Only (toggle)**: Set the Mark Deleted Tables from Filter Only toggle to flag tables as soft-deleted if they are not present anymore within the filtered schema or database only. This flag is useful when you have more than one ingestion pipelines. For example if you have a schema

    * **includeTables (toggle)**: Optional configuration to turn off fetching metadata for tables.

    * **includeViews (toggle)**: Set the Include views toggle to control whether to include views as part of metadata ingestion.

    * **includeTags (toggle)**: Set the 'Include Tags' toggle to control whether to include tags as part of metadata ingestion.

    * **includeOwners (toggle)**: Set the 'Include Owners' toggle to control whether to include owners to the ingested entity if the owner email matches with a user stored in the OM server as part of metadata ingestion. If the ingested entity already exists and has an owner, the owner will not be overwritten.

    * **includeStoredProcedures (toggle)**: Optional configuration to toggle the Stored Procedures ingestion.

    * **includeDDL (toggle)**: Optional configuration to toggle the DDL Statements ingestion.

    * **queryLogDuration (Optional)**: Configuration to tune how far we want to look back in query logs to process Stored Procedures results.

    * **queryParsingTimeoutLimit (Optional)**: Configuration to set the timeout for parsing the query in seconds.

    * **useFqnForFiltering (toggle)**: Regex will be applied on fully qualified name (e.g service\_name.db\_name.schema\_name.table\_name) instead of raw name (e.g. table\_name).

    * **Incremental (Beta)**: Use Incremental Metadata Extraction after the first execution. This is done by getting the changed tables instead of all of them. **Only Available for BigQuery, Redshift and Snowflake**
      * **Enabled**: If `True`, enables Metadata Extraction to be Incremental.
      * **lookback Days**: Number of days to search back for a successful pipeline run. The timestamp of the last found successful pipeline run will be used as a base to search for updated entities.
      * **Safety Margin Days**: Number of days to add to the last successful pipeline run timestamp to search for updated entities.

    * **Threads (Beta)**: Use a Multithread approach for Metadata Extraction. You can define here the number of threads you would like to run concurrently.

    Note that the right-hand side panel in the Collate UI will also share useful documentation when configuring the ingestion.
  </Step>

  <Step title="Schedule the Ingestion and Deploy">
    Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The
    timezone is in UTC. Select a Start Date to schedule for ingestion. It is
    optional to add an End Date.

    Review your configuration settings. If they match what you intended,
    click Deploy to create the service and schedule metadata ingestion.

    If something doesn't look right, click the Back button to return to the
    appropriate step and change the settings as needed.

    After configuring the workflow, you can click on Deploy to create the
    pipeline.

    <img src="https://mintcdn.com/collatedocs/piJyXg9wW6Ik1lg-/public/images/connectors/schedule.png?fit=max&auto=format&n=piJyXg9wW6Ik1lg-&q=85&s=f1add591824b44456f0e2ff259a21c6f" alt="Schedule the Workflow" width="2733" height="1083" data-path="public/images/connectors/schedule.png" />
  </Step>

  <Step title="View the Ingestion Pipeline">
    Once the workflow has been successfully deployed, you can view the
    Ingestion Pipeline running from the Service Page.

    <img src="https://mintcdn.com/collatedocs/cOe_QuHYxAbkMtTI/public/images/connectors/view-ingestion-pipeline.png?fit=max&auto=format&n=cOe_QuHYxAbkMtTI&q=85&s=8c754af74f99ee70e714f6f707b827e4" alt="View Ingestion Pipeline" width="2733" height="1271" data-path="public/images/connectors/view-ingestion-pipeline.png" />

    <Tip>
      If [AutoPilot](/how-to-guides/admin-guide/applications/autopilot) is enabled, workflows like usage tracking, data lineage, and similar tasks will be handled automatically. Users don’t need to set up or manage them - AutoPilot takes care of everything in the system.
    </Tip>
  </Step>
</Steps>

### Cross Project Lineage

We support cross-project lineage, but the data must be ingested within a single service. This means you need to perform lineage ingestion for just one service while including multiple projects.

## Reverse Metadata

<Step title="Reverse Metadata">
  ### Description Management

  BigQuery supports description updates at the following levels:

  * Schema level
  * Table level

  ### Owner Management

  ❌ Owner management is not supported for BigQuery.

  ### Tag Management

  BigQuery supports tag management at the following levels:

  * Schema level
  * Table level

  ### Custom SQL Template

  BigQuery supports custom SQL templates for metadata changes. The template is interpreted using python f-strings.

  Here are examples of custom SQL queries for metadata changes:

  ```sql theme={null}
  -- Update table labels
  ALTER TABLE `{database}.{schema}.{table}` SET OPTIONS (labels = {tags});
  ```

  The list of variables for custom SQL can be found [here](/how-to-guides/admin-guide/applications/reverse-metadata#custom-sql-template).

  ### Requirements for Reverse Metadata

  In addition to the basic ingestion requirements, for reverse metadata ingestion the user needs:

  | #  | GCP Permission           | Required For                        |
  | :- | :----------------------- | :---------------------------------- |
  | 1  | bigquery.datasets.update | Update dataset description & labels |
  | 2  | bigquery.tables.update   | Update table description & labels   |

  For more information about reverse metadata ingestion, see [Reverse Metadata Application](/how-to-guides/admin-guide/applications/reverse-metadata).
</Step>

<Tip>
  When using a **Hybrid Ingestion Runner**, any sensitive credential fields—such as passwords, API keys, or private keys—must reference secrets using the following format:

  ```
  password: secret:/my/database/password
  ```

  This applies **only to fields marked as secrets** in the connection form (these typically mask input and show a visibility toggle icon).
  For a complete guide on managing secrets in hybrid setups, see the [Hybrid Ingestion Runner Secret Management Guide](https://docs.getcollate.io/getting-started/day-1/hybrid-saas/hybrid-ingestion-runner#3.-manage-secrets-securely).
</Tip>

## Troubleshooting

<Columns cols={2}>
  <Card title="BigQuery Troubleshooting" href="/connectors/database/bigquery/troubleshooting">
    Learn more about how to troubleshoot common BigQuery connector issues and resolve configuration or ingestion errors.
  </Card>
</Columns>

## Related

<Columns cols={2}>
  <Card title="Usage Workflow" href="/how-to-guides/guide-for-data-users/ingestion/workflows/usage">
    Learn more about how to configure the Usage Workflow to ingest Query information from the UI.
  </Card>

  <Card title="Lineage Workflow" href="/how-to-guides/guide-for-data-users/ingestion/workflows/lineage">
    Learn more about how to configure the Lineage from the UI.
  </Card>

  <Card title="Profiler Workflow" href="/how-to-guides/data-quality-observability/profiler/profiler-workflow">
    Learn more about how to configure the Data Profiler from the UI.
  </Card>

  <Card title="Data Quality Workflow" href="/how-to-guides/data-quality-observability/quality/configure">
    Learn more about how to configure the Data Quality tests from the UI.
  </Card>

  <Card title="dbt Integration" href="/connectors/database/dbt">
    Learn more about how to ingest dbt models' definitions and their lineage.
  </Card>
</Columns>