BigQuery assets package

The BigQuery assets package crawls Google BigQuery assets and publishes them to Atlan for discovery.

Service account

Will create a new connection

This should only be used to create the workflow the first time. Each time you run this method it will create a new connection and new assets within that connection — which could lead to duplicate assets if you run the workflow this way multiple times with the same settings.

Instead, when you want to re-crawl assets, re-run the existing workflow (see Re-run existing workflow below).

2.1.9

To crawl assets from BigQuery using service account authentication:

Java Python Kotlin Raw REST API

Coming soon

from pyatlan.client.atlan import AtlanClient
from pyatlan.cache.role_cache import RoleCache
from pyatlan.model.packages import BigQueryCrawler

client = AtlanClient()

crawler = (
    BigQueryCrawler( # (1)
        connection_name="production", # (2)
        admin_roles=[RoleCache.get_id_for_name("$admin")], # (3)
        admin_groups=None,
        admin_users=None,
        row_limit=10000, # (4)
        allow_query=True, # (5)
        allow_query_preview=True, # (6)
    )
    .service_account_auth( # (7)
        project_id="test-project-id",
        service_account_json="test-account-json",
        service_account_email="test@test.com",
    )
    .include(assets={"test-include": ["test-asset-1", "test-asset-2"]}) # (8)
    .exclude(assets=None) # (9)
    .exclude_regex(regex=".*_TEST") # (10)
    .custom_config(config={"ignore-all-case": True}) # (11)
    .to_workflow() # (12)
)
response = client.workflow.run(crawler) # (13)

1. Base configuration for a new BigQuery crawler.
2. You must provide a name for the connection that the BigQuery assets will exist within.

3. You must specify at least one connection admin, either:

   • everyone in a role (in this example, all $admin users).
   • a list of groups (names) that will be connection admins.
   • a list of users (names) that will be connection admins.

4. You can specify a maximum number of rows that can be accessed for any asset in the connection.

5. You can specify whether you want to allow queries to this connection. (True, as in this example) or deny all query access to the connection (False).
6. You can specify whether you want to allow data previews on this connection (True, as in this example) or deny all sample data previews to the connection (False).

7. When using service_account_auth(), you need to provide the following information:

   • project ID of your Google Cloud project.
   • entire service account json.
   • service account email.

8. You can also optionally specify the set of assets to include in crawling. For BigQuery assets, this should be specified as a dict keyed by project name with each value being a list of tables to include. (If set to None, all table will be crawled.)

9. You can also optionally specify the list of assets to exclude from crawling. For BigQuery assets, this should be specified as a dict keyed by project name with each value being a list of tables to exclude. (If set to None, no table will be excluded.)
10. You can also optionally specify the exclude regex for crawler ignore tables and views based on a naming convention.
11. You can also optionally specify the custom JSON configuration controlling experimental feature flags for the crawler, eg: {"ignore-all-case": True} to enable crawling assets with case-sensitive identifiers.
12. Now, you can convert the package into a Workflow object.

13. Run the workflow by invoking the run() method on the workflow client, passing the created object.

    Workflows run asynchronously

    Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

Coming soon

Create the workflow via UI only

We recommend creating the workflow only via the UI. To rerun an existing workflow, see the steps below.

Re-run existing workflow

1.9.5 1.10.6

To re-run an existing workflow for BigQuery assets:

Java Python Kotlin Raw REST API

List<WorkflowSearchResult> existing = WorkflowSearchRequest // (1)
            .findByType(BigQueryCrawler.PREFIX, 5); // (2)
// Determine which of the results is the BigQuery workflow you want to re-run...
WorkflowRunResponse response = existing.get(n).rerun(); // (3)

1. You can search for existing workflows through the WorkflowSearchRequest class.
2. You can find workflows by their type using the findByType() helper method and providing the prefix for one of the packages. In this example, we do so for the BigQueryCrawler. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

3. Once you've found the workflow you want to re-run, you can simply call the rerun() helper method on the workflow search result. The WorkflowRunResponse is just a subtype of WorkflowResponse so has the same helper method to monitor progress of the workflow run.

   • Optionally, you can use the rerun(true) method with idempotency to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to false

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

from pyatlan.client.atlan import AtlanClient
from pyatlan.model.enums import WorkflowPackage

client = AtlanClient()

existing = client.workflow.find_by_type(  # (1)
  prefix=WorkflowPackage.BIGQUERY, max_results=5
)

# Determine which BigQuery workflow (n)
# from the list of results you want to re-run.
response = client.workflow.rerun(existing[n]) # (2)

1. You can find workflows by their type using the workflow client find_by_type() method and providing the prefix for one of the packages. In this example, we do so for the BigQueryCrawler. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

2. Once you've found the workflow you want to re-run, you can simply call the workflow client rerun() method.

   • Optionally, you can use rerun(idempotent=True) to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to False.

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

val existing = WorkflowSearchRequest // (1)
            .findByType(BigQueryCrawler.PREFIX, 5); // (2)
// Determine which of the results is the
// BigQuery workflow you want to re-run...
val response = existing.get(n).rerun(); // (3)

1. You can search for existing workflows through the WorkflowSearchRequest class.
2. You can find workflows by their type using the findByType() helper method and providing the prefix for one of the packages. In this example, we do so for the BigQueryCrawler. (You can also specify the maximum number of resulting workflows you want to retrieve as results.)

3. Once you've found the workflow you want to re-run, you can simply call the rerun() helper method on the workflow search result. The WorkflowRunResponse is just a subtype of WorkflowResponse so has the same helper method to monitor progress of the workflow run.

   • Optionally, you can use the rerun(true) method with idempotency to avoid re-running a workflow that is already in running or in a pending state. This will return details of the already running workflow if found, and by default, it is set to false

   Workflows run asynchronously

   Remember that workflows run asynchronously. See the packages and workflows introduction for details on how you can check the status and wait until the workflow has been completed.

Requires multiple steps through the raw REST API

1. Find the existing workflow.
2. Send through the resulting re-run request.

{
  "from": 0,
  "size": 5,
  "query": {
    "bool": {
      "filter": [
        {
          "nested": {
            "path": "metadata",
            "query": {
              "prefix": {
                "metadata.name.keyword": {
                  "value": "atlan-bigquery" // (1)
                }
              }
            }
          }
        }
      ]
    }
  },
  "sort": [
    {
      "metadata.creationTimestamp": {
        "nested": {
          "path": "metadata"
        },
        "order": "desc"
      }
    }
  ],
  "track_total_hits": true
}

1. Searching by the atlan-bigquery prefix will ensure you only find existing BigQuery assets workflows.

   Name of the workflow

   The name of the workflow will be nested within the _source.metadata.name property of the response object. (Remember since this is a search, there could be multiple results, so you may want to use the other details in each result to determine which workflow you really want.)

{
  "namespace": "default",
  "resourceKind": "WorkflowTemplate",
  "resourceName": "atlan-bigquery-1684500411" // (1)
}

1. Send the name of the workflow as the resourceName to rerun it.

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