Tableau assets package

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

Direct extraction

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).

2.0.3 1.9.0

To crawl Tableau assets directly from Tableau:

Java Python Kotlin Raw REST API

AtlanClient client = Atlan.getDefaultClient();
Workflow crawler = TableauCrawler.creator( // (1)
      client, // (2)
      "production", // (3)
      List.of(client.getRoleCache().getIdForName("$admin")), // (4)
      null,
      null
    )
    .direct(  // (5)
      "example.online.tableau.com",
      "atlan-site",
      true
    )
    .basicAuth( // (6) (7)
      "atlan-user",
      "atlan-pass"
    )
    .include( // (8)
      List.of("fc2923bd-cf94-2b29-9cdd-9cc2f7a4f029")
    )
    .exclude(List.of()) // (9)
    .crawlUnpublished(true)  // (10)
    .crawlHiddenFields(false)  // (11)
    .alternateHost("https://alternate.tableau.com")  // (12)
    .build()  // (13)
    .toWorkflow();  // (14)
WorkflowResponse response = crawler.run();  // (15)

1. The TableauCrawler package will create a workflow to crawl assets from Tableau.
2. You must provide Atlan client.
3. You must provide a name for the connection that the Tableau assets will exist within.

4. 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.

5. To configure the crawler for extracting data directly from Tableau then you must provide the following information:

   • hostname of your Tableau instance.
   • site to crawl within Tableau.
   • whether to use SSL for the connection.

6. When using basicAuth(), you must provide the following information:

   • your username for accessing Tableau.
   • your password for accessing Tableau.
7. or you also can use personalAccessToken() auth, then you must provide the following information:
   • your username for accessing Tableau.
   • your access token for accessing Tableau.
8. You can also optionally specify the list of projects to include in crawling. For Tableau assets, this should be specified as a list of project GUIDs. (If set to null, all projects will be crawled.)
9. You can also optionally specify the list of projects to exclude from crawling. For Tableau assets, this should be specified as a list of project GUIDs. (If set to null, no projects will be excluded.)
10. You can also optionally set whether to crawl unpublished worksheets and dashboards (true) or not (false).
11. You can also optionally set whether to crawl hidden datasource fields (true) or not (false).
12. You can also optionally set an alternate host to use for the "View in Tableau" button for assets in the UI.
13. Build the minimal package object.
14. Now, you can convert the package into a Workflow object.

15. You can then run the workflow using the run() method on the object you've created.

    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.cache.role_cache import RoleCache
from pyatlan.model.packages import TableauCrawler

client = AtlanClient()

crawler = (
    TableauCrawler(  # (1)
        connection_name="production", # (2)
        admin_roles=[RoleCache.get_id_for_name("$admin")], # (3)
        admin_groups=None,
        admin_users=None,
    )
    .direct(  # (4)
        hostname="example.online.tableau.com",
        site="atlan-site",
        port=443,
        ssl_enabled=True,
    )
    .basic_auth(  # (5) # (6)
        username="atlan-user",
        password="atlan-pass",
    )
    .include(projects=["fc2923bd-cf94-2b29-9cdd-9cc2f7a4f029"])  # (7)
    .exclude(projects=[])  # (8)
    .crawl_unpublished(True)  # (9)
    .crawl_hidden_fields(False)  # (10)
    .alternate_host(hostname="https://alternate.tableau.com")  # (11)
    .to_workflow()  # (12)
)
response = client.workflow.run(crawler) # (13)

1. Base configuration for a new Tableau crawler.
2. You must provide a name for the connection that the Tableau 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. To configure the crawler for extracting data directly from Tableau then you must provide the following information:

   • hostname of your Tableau instance.
   • port number of the Tableau instance (use 443 for the default).
   • site to crawl within Tableau.
   • whether to use SSL for the connection.

5. When using basic_auth(), you must provide the following information:

   • your username for accessing Tableau.
   • your password for accessing Tableau.

6. or you also can use personal_access_token() auth, then you must provide the following information:

   • your username for accessing Tableau.
   • your access token for accessing Tableau.
7. You can also optionally specify the list of projects to include in crawling. For Tableau assets, this should be specified as a list of project GUIDs. (If set to None, all projects will be crawled.)
8. You can also optionally specify the list of projects to exclude from crawling. For Tableau assets, this should be specified as a list of project GUIDs. (If set to None, no projects will be excluded.)
9. You can also optionally set whether to crawl hidden datasource fields (True) or not (False).
10. You can also optionally set whether to crawl unpublished worksheets and dashboards (True) or not (False).
11. You can also optionally set an alternate host to use for the "View in Tableau" button for assets in the UI.
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.

val client = Atlan.getDefaultClient()
val crawler = TableauCrawler.creator( // (1)
      client, // (2)
      "production", // (3)
      listOf(client.getRoleCache().getIdForName("\$admin")), // (4)
      null,
      null
    )
    .direct(  // (5)
        "example.online.tableau.com",
        "atlan-site",
        true
    )
    .basicAuth( // (6) (7)
        "atlan-user",
        "atlan-pass"
    )
    .include( // (8)
        listOf("fc2923bd-cf94-2b29-9cdd-9cc2f7a4f029")
    )
    .exclude(emptyList()) // (9)
    .crawlUnpublished(true)  // (10)
    .crawlHiddenFields(false)  // (11)
    .alternateHost("https://alternate.tableau.com")  // (12)
    .build()  // (13)
    .toWorkflow()  // (14)
val response = crawler.run()  // (15)

1. The TableauCrawler package will create a workflow to crawl assets from Tableau.
2. You must provide Atlan client.
3. You must provide a name for the connection that the Tableau assets will exist within.

4. 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.

5. To configure the crawler for extracting data directly from Tableau then you must provide the following information:

   • hostname of your Tableau instance.
   • site to crawl within Tableau.
   • whether to use SSL for the connection.

6. When using basicAuth(), you must provide the following information:

   • your username for accessing Tableau.
   • your password for accessing Tableau.
7. or you also can use personalAccessToken() auth, then you must provide the following information:
   • your username for accessing Tableau.
   • your access token for accessing Tableau.
8. You can also optionally specify the list of projects to include in crawling. For Tableau assets, this should be specified as a list of project GUIDs. (If set to null, all projects will be crawled.)
9. You can also optionally specify the list of projects to exclude from crawling. For Tableau assets, this should be specified as a list of project GUIDs. (If set to null, no projects will be excluded.)
10. You can also optionally set whether to crawl unpublished worksheets and dashboards (true) or not (false).
11. You can also optionally set whether to crawl hidden datasource fields (true) or not (false).
12. You can also optionally set an alternate host to use for the "View in Tableau" button for assets in the UI.
13. Build the minimal package object.
14. Now, you can convert the package into a Workflow object.

15. You can then run the workflow using the run() method on the object you've created.

    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.

Create the workflow via UI only

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

Offline extraction

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).

2.6.0

To crawl Tableau assets from the S3 bucket:

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 TableauCrawler

client = AtlanClient()

crawler = (
    TableauCrawler(  # (1)
        connection_name="production", # (2)
        admin_roles=[RoleCache.get_id_for_name("$admin")], # (3)
        admin_groups=None,
        admin_users=None,
    )
    .s3( # (4)
        bucket_name="test-bucket",
        bucket_prefix="test-prefix",
        bucket_region="test-region",
    )
    .to_workflow()  # (5)
)
response = client.workflow.run(crawler) # (6)

1. Base configuration for a new Tableau crawler.
2. You must provide a name for the connection that the Tableau 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. When using s3(), you need to provide the following information:

   • name of the bucket/storage that contains the extracted metadata files.
   • prefix is everything after the bucket/storage name, including the path.
   • (Optional) name of the region if applicable.

5. Now, you can convert the package into a Workflow object.

6. 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 Tableau assets:

Java Python Kotlin Raw REST API

List<WorkflowSearchResult> existing = WorkflowSearchRequest // (1)
            .findByType(TableauCrawler.PREFIX, 5); // (2)
// Determine which of the results is the Tableau 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 TableauCrawler. (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.TABLEAU, max_results=5
)

# Determine which Tableau 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 TableauCrawler. (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(TableauCrawler.PREFIX, 5); // (2)
// Determine which of the results is the
// Tableau 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 TableauCrawler. (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-tableau" // (1)
                }
              }
            }
          }
        }
      ]
    }
  },
  "sort": [
    {
      "metadata.creationTimestamp": {
        "nested": {
          "path": "metadata"
        },
        "order": "desc"
      }
    }
  ],
  "track_total_hits": true
}

1. Searching by the atlan-tableau prefix will ensure you only find existing Tableau 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-tableau-1684500411" // (1)
}

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

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