BigQuery

PROD

Feature List

✓ Metadata

✓ Query Usage

✓ Lineage

✓ Column-level Lineage

✓ Data Profiler

✓ Data Quality

✓ dbt

✓ Tags

✓ Stored Procedures

✓ Sample Data

✓ Reverse Metadata (OpenMetadata Only)

✓ Auto-Classification

✕ 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
Metadata Ingestion
- Incremental Extraction
Query Usage
Lineage
Data Profiler
Data Quality
dbt Integration

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.

External Schedulers

Get more information about running the Ingestion Framework Externally

Requirements

Python Requirements

We have support for Python versions 3.9-3.11

To run the BigQuery ingestion, you will need to install:

pip3 install "openmetadata-ingestion[bigquery]"

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

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

Create Custom GCP Role

Checkout this documentation on how to create a custom role and assign it to the service account.

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:

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

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

Metadata Ingestion

1. Define the YAML Config

This is a sample config for BigQuery:

2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

metadata ingest -c <path-to-yaml>

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.

Query Usage

The Query Usage workflow will be using the query-parser processor. After running a Metadata Ingestion workflow, we can run Query Usage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

1. Define the YAML Config

This is a sample config for Usage:

2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

metadata usage -c <path-to-yaml>

Lineage

After running a Metadata Ingestion workflow, we can run Lineage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

1. Define the YAML Config

This is a sample config for Lineage:

You can learn more about how to configure and run the Lineage Workflow to extract Lineage data from here

2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

metadata ingest -c <path-to-yaml>

Data Profiler

The Data Profiler workflow will be using the orm-profiler processor. After running a Metadata Ingestion workflow, we can run the Data Profiler workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

1. Define the YAML Config

This is a sample config for the profiler:

You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from here

2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

metadata profile -c <path-to-yaml>

Note now instead of running ingest, we are using the profile command to select the Profiler workflow.

Data Profiler

Find more information about the Data Profiler here

Auto Classification

The Auto Classification workflow will be using the orm-profiler processor. After running a Metadata Ingestion workflow, we can run the Auto Classification workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

1. Define the YAML Config

This is a sample config for the Auto Classification Workflow:

2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

metadata classify -c <path-to-yaml>

Now instead of running ingest, we are using the classify command to select the Auto Classification workflow.

Data Quality

Adding Data Quality Test Cases from yaml config

When creating a JSON config for a test workflow the source configuration is very simple.

source:
  type: TestSuite
  serviceName: <your_service_name>
  sourceConfig:
    config:
      type: TestSuite
      entityFullyQualifiedName: <entityFqn>

The only sections you need to modify here are the serviceName (this name needs to be unique) and entityFullyQualifiedName (the entity for which we’ll be executing tests against) keys. Once you have defined your source configuration you’ll need to define te processor configuration.

processor:
  type: "orm-test-runner"
  config:
    forceUpdate: <false|true>
    testCases:
      - name: <testCaseName>
        testDefinitionName: columnValueLengthsToBeBetween
        columnName: <columnName>
        parameterValues:
          - name: minLength
            value: 10
          - name: maxLength
            value: 25
      - name: <testCaseName>
        testDefinitionName: tableRowCountToEqual
        parameterValues:
          - name: value
            value: 10

The processor type should be set to "orm-test-runner". For accepted test definition names and parameter value names refer to the tests page.

Note that while you can define tests directly in this YAML configuration, running the workflow will execute ALL THE TESTS present in the table, regardless of what you are defining in the YAML.This makes it easy for any user to contribute tests via the UI, while maintaining the test execution external.

You can keep your YAML config as simple as follows if the table already has tests.

processor:
  type: "orm-test-runner"
  config: {}

Key reference:

forceUpdate: if the test case exists (base on the test case name) for the entity, implements the strategy to follow when running the test (i.e. whether or not to update parameters)
testCases: list of test cases to add to the entity referenced. Note that we will execute all the tests present in the Table.
name: test case name
testDefinitionName: test definition
columnName: only applies to column test. The name of the column to run the test against
parameterValues: parameter values of the test

The sink and workflowConfig will have the same settings as the ingestion and profiler workflow.

Full `yaml` config example

source:
  type: TestSuite
  serviceName: MyAwesomeTestSuite
  sourceConfig:
    config:
      type: TestSuite
      entityFullyQualifiedName: MySQL.default.openmetadata_db.tag_usage
#     testCases: ["run_only_this_test_case"] # Optional, if not provided all tests will be executed

processor:
  type: "orm-test-runner"
  config:
    forceUpdate: false
    testCases:
      - name: column_value_length_tagFQN
        testDefinitionName: columnValueLengthsToBeBetween
        columnName: tagFQN
        parameterValues:
          - name: minLength
            value: 10
          - name: maxLength
            value: 25
      - name: table_row_count_test
        testDefinitionName: tableRowCountToEqual
        parameterValues:
          - name: value
            value: 10

sink:
  type: metadata-rest
  config: {}
workflowConfig:
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

How to Run Tests

To run the tests from the CLI execute the following command

metadata test -c /path/to/my/config.yaml

dbt Integration

You can learn more about how to ingest dbt models’ definitions and their lineage here.

Connectors

Connector

Ingestion

Run the BigQuery Connector Externally

BigQuery

How to Run the Connector Externally

External Schedulers

Requirements

Python Requirements

GCP Permissions

Create Custom GCP Role

Partitioned Tables

Resolution

Metadata Ingestion

1. Define the YAML Config

2. Run with the CLI

Query Usage

1. Define the YAML Config

2. Run with the CLI

Lineage

1. Define the YAML Config

2. Run with the CLI

Data Profiler

1. Define the YAML Config

2. Run with the CLI

Data Profiler

Auto Classification

1. Define the YAML Config

2. Run with the CLI

Data Quality

Adding Data Quality Test Cases from yaml config

Key reference:

Full `yaml` config example

How to Run Tests

dbt Integration

Connectors

Connector

Ingestion

BigQuery

​How to Run the Connector Externally

External Schedulers

​Requirements

​Python Requirements

​GCP Permissions

Create Custom GCP Role

​Partitioned Tables

​Resolution

​Metadata Ingestion

​1. Define the YAML Config

​2. Run with the CLI

​Query Usage

​1. Define the YAML Config

​2. Run with the CLI

​Lineage

​1. Define the YAML Config

​2. Run with the CLI

​Data Profiler

​1. Define the YAML Config

​2. Run with the CLI

Data Profiler

​Auto Classification

​1. Define the YAML Config

​2. Run with the CLI

​Data Quality

​Adding Data Quality Test Cases from yaml config

​Key reference:

​Full yaml config example

​How to Run Tests

​dbt Integration

How to Run the Connector Externally

Requirements

Python Requirements

GCP Permissions

Partitioned Tables

Resolution

Metadata Ingestion

1. Define the YAML Config

2. Run with the CLI

Query Usage

1. Define the YAML Config

2. Run with the CLI

Lineage

1. Define the YAML Config

2. Run with the CLI

Data Profiler

1. Define the YAML Config

2. Run with the CLI

Auto Classification

1. Define the YAML Config

2. Run with the CLI

Data Quality

Adding Data Quality Test Cases from yaml config

Key reference:

Full `yaml` config example

How to Run Tests

dbt Integration