BigQuery Hybrid Runner

• Requirements
• Metadata Ingestion
  • Incremental Extraction
• Query Usage
• Data Profiler
• Data Quality
• Lineage
• dbt Integration
• Troubleshooting
• Reverse Metadata

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How to Run the Connector Externally

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Requirements

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Partitioned Tables

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Resolution

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.

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Data Catalog API Permissions

• Follow Google’s instructions for enabling an API.
• Select the GCP Project ID that you want to enable the Data Catalog API on.
• Search for and enable the Data Catalog API.

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GCP Permissions

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Metadata Ingestion

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Step 1: Add New Service

1. In the left navigation, click Connections.
2. On the Connections page, click Add New Service.

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Step 2: Select a Service and Connector

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Step 3: Add Service Name and Description

• Enter a unique, descriptive Service Name. Collate identifies services by their service name. Enter a name that distinguishes this deployment from other BigQuery services you are ingesting metadata from.
• Optional: Enter a Description for the service.

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Step 4: Configure Connection Options

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Select Ingestion Runner

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Enter Connection Details

• GCP Credentials: You can authenticate with your BigQuery instance using either GCP Credentials Path, where you specify the file path of the service account key, or GCP Credentials Values, where you pass the values directly from the service account key file. Check out this 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 the following information, all provided by BigQuery:
    • 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. Choose Single Project ID or Multiple Project ID and enter one or more project IDs to ingest metadata from different BigQuery projects into one service.
    • 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.
    • 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.
    • 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. You can paste the key directly or use Upload key file to load it from the service account JSON file.
    • Expand advanced credential settings for the following fields:
      • 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.
      • Authentication 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 is 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. The default token URI is 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 is https://www.googleapis.com/oauth2/v1/certs.
      • Client X509 Certificate 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.
  • GCP Impersonate Service Account Configuration (Optional): Expand impersonation settings to enable the authenticated service account to impersonate another service account, instead of ingesting directly with the credentials above.
    • Target Service Account Email: The email of the service account to impersonate.
    • Lifetime: Number of seconds the delegated credential should remain valid. Defaults to 3600.
• Host and Port: BigQuery APIs URL. By default, the API URL is bigquery.googleapis.com. You can modify this if you have a custom implementation of BigQuery.
• 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) configured under Project ID. 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.
  Tip: Project ID tells Collate where to read metadata from. Billing Project ID tells BigQuery which project should pay for the queries.

  • Same-project setup: if your data lives in analytics-prod and that same project should pay for the queries, use analytics-prod as the Project ID and either leave Billing Project ID empty or set it to analytics-prod.
  • Cross-project billing setup: if your data lives in marketing-prod and finance-prod, but all query costs should be charged to central-billing, use marketing-prod and finance-prod as Project ID values and set Billing Project ID to central-billing.
  Tip: Application Default Credentials (ADC) AuthenticationIf you want to use ADC authentication for BigQuery, configure the GCP credentials with type gcp_adc:

  credentials:
    gcpConfig:
      type: gcp_adc
      projectId: ["your-project-id"]  # Optional: specify project(s) for data access
  

  Using ADC with Billing Project ID: When using ADC authentication, you can still specify a Billing Project ID to control which project pays for the BigQuery queries Collate runs. This is particularly useful when:

  • Your service account has access to multiple projects
  • You want to bill queries to a specific project different from the one containing your data
  • You’re running queries that span multiple projects

  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.
• Include Policy Tags (Optional): Option to include policy tags as part of the column description. Enabled by default.
• 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 you can create policy tags and set up column-level access control here. If you have attached policy tags to the columns of a table available in BigQuery, Collate will fetch those tags and attach them to the respective columns. In this field, specify the ID of the 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 you can create policy tags and set up column-level access control here. If you have attached policy tags to the columns of a table available in BigQuery, Collate will fetch those tags and attach them to the respective columns. In this field, 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.

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Advanced Configuration

• 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.

password: secret:/my/database/password

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Test Connection

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Step 5: Configure Ingestion Options

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How Filter Patterns Work

• Include: Add one or more comma-separated regular expressions. Collate ingests only assets whose names match at least one expression. Leave blank to include all assets.
• Exclude: Add one or more comma-separated regular expressions. Collate skips any asset whose name matches an expression. Leave blank to exclude nothing.

• contains: matches any name containing the value. For example, sales matches my_sales_data and sales_2024.
• starts with: matches names beginning with the value. For example, prod_ matches prod_db and prod_schema.
• ends with: matches names ending with the value. For example, _raw matches events_raw and logs_raw.
• is exactly: matches the exact name only. For example, analytics matches only analytics.
• matches regex: matches names using a regular expression. For example, ^prod_.*_v\d+$ matches prod_events_v1.

• Database: Controls which databases Collate ingests from the source.
• Schema: Controls which schemas within the ingested databases are included.
• Table: Controls which tables and views within the ingested schemas are included.
• Stored Procedure: Controls which stored procedures are included in metadata ingestion.

• Scan Mode: You can choose between the following scan modes:
  • Scan all: Ingests every asset of that type the connector can access. This is the default.
  • Only specific: Enables include rules so only assets matching at least one rule are ingested.
• Exclude system toggle: Use this toggle to automatically filter out system-reserved names defined by the connector — for example, Exclude system databases for the Databases section.
• Always exclude: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules.
• Preview: Shows a real-time summary of what will be in scope based on your current rules.
• Include rules (available only in Only specific mode): Click + Add to define a rule. Added rules appear as chips; an asset is included if it matches any rule.

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Step 6: Create & Deploy

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Configure Metadata Agent and Schedule Ingestion

1. In the left navigation, click Connections and select your service.
2. Click the Agents tab.
3. Click Add Agent and select Metadata from the dropdown. For some services, the dropdown is not available and clicking Add Agent takes you directly to the agent configuration page.
4. On the Configure Ingestion page, do the following and click Next.
   • Name this Ingestion: Enter a unique recognizable name for this ingestion pipeline.
   • Agent Setup: Configure core parameters for metadata extraction. The following fields are available:

     Field	Default	Description
     Default Owner	—	Owner applied to all entities when no specific level owner is configured. Accepts a user or team name/email.
     Service Owner	—	Owner assigned to the service entity.
     Database Owner	—	Owner assigned to all ingested databases. Accepts a single owner or a per-database name mapping.
     Database Schema Owner	—	Owner assigned to all ingested schemas. Accepts a single owner or a per-schema name mapping.
     Table Owner	—	Owner assigned to all ingested tables. Accepts a single owner or a per-table name mapping.
     Enable Inheritance	On	When on, child entities inherit the owner from their parent when they have no owner configured.
     Query Log Duration	1	Number of days to look back in query logs when processing stored procedure results.
     Query Parsing Timeout Limit	300	Timeout in seconds for parsing a single query.
     Number of Threads	1	Number of threads to use for parallel table ingestion.
     Incremental Extraction	Off	When enabled, subsequent runs only extract entities changed since the last successful run.
     Successful Pipeline Run Lookback Days	7	Number of days to search back for a prior successful run to use as a baseline for incremental extraction.
     Safety Margin Days	1	Additional days added to the baseline timestamp as a buffer for incremental extraction.
     JSON Schema Sample Size	10	Number of rows sampled to infer JSON column schema. Only applies when Extract JSON Schema is enabled in Advanced Config.

     
   • Filter Patterns: Apply include or exclude rules to scope which databases, schemas, tables, and stored procedures this agent ingests. For more information about various filter options, see Step 5: Configure Ingestion Options.
   • Scope & Behaviour: Control how the agent handles metadata during ingestion. Toggle each option on or off based on your needs:

     Toggle	Default	Description
     Include Tables	On	Ingest table metadata from the source. Turn off to skip tables entirely.
     Include Tags	On	Ingest tags from the source.
     Include Stored Procedures	On	Ingest stored procedure metadata.
     Include DDL Statements	Off	Ingest DDL statements alongside table metadata.
     Include Owners	Off	Assign owners to ingested entities when the source owner’s email matches a user in Collate. Does not overwrite an existing owner.
     Include Custom Properties	Off	Ingest connector-specific custom properties onto entities.
     Mark Deleted Tables	On	Soft-delete tables in Collate when they are removed from the source. Applies only within the currently ingested schema.
     Mark Deleted Stored Procedures	On	Soft-delete stored procedures in Collate when they are removed from the source.
     Mark Deleted Schemas	Off	Soft-delete schemas and all their child assets when removed from the source.
     Mark Deleted Databases	Off	Soft-delete databases and all their child assets when removed from the source.
     Override Metadata	Off	When on, source values overwrite existing descriptions, tags, owners, and display names in Collate. When off, Collate only updates fields that have no existing value.
     Enable Debug Log	Off	Sets the ingestion log level to DEBUG. Useful for troubleshooting.

     Note: Available toggles vary by connector. Stored procedure options only appear for connectors that support stored procedures.
     
   • Advanced Config: Optional connector-specific settings such as Include Views and Extract JSON Schema.
5. On the Schedule Interval page, set when the agent runs:
   • Schedule: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression.
   • On-Demand: No automatic schedule; trigger the agent manually when needed.
   
6. Click Add to deploy the agent.

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Cross Project Lineage

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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:
  -- Update table labels
  ALTER TABLE `{database}.{schema}.{table}` SET OPTIONS (labels = {tags});
  
  The list of variables for custom SQL can be found here.
• 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

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Troubleshooting