## Tableau assets package

URL: https://docs.atlan.com/apps/connectors/business-intelligence/tableau/sdk/references/package-reference

> Programmatically crawl and manage Tableau assets in Atlan using the Python SDK (pyatlan). Reference for the Tableau assets package.

:::warning[Deprecated—use the app reference]
This package is deprecated. Use the [app reference](app-reference) for the new `client.app` builder, or see [Manage apps](https://docs.atlan.com/llms/platform/python/manage-apps/llms.txt).
:::

# Tableau assets package

The [Tableau assets package](https://ask.atlan.com/hc/en-us/articles/6332449996689)
crawls Tableau assets and publishes them to Atlan for discovery.

## Direct extraction

:::warning[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](#re-run-existing-workflow) below).
:::
To crawl Tableau assets directly from Tableau:

### Java

```java showLineNumbers title="Direct extraction from Tableau"
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(client); // (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. Because this operation will execute work in Atlan, you must [provide it an `AtlanClient`](https://docs.atlan.com/llms/platform/python/set-up-sdk/llms.txt) through which to connect to the tenant.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt) for details on how you can check the status and wait until the workflow has been completed.
 :::

### Python

```python showLineNumbers title="Direct extraction from Tableau"
from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import TableauCrawler

client = AtlanClient()

crawler = (
 TableauCrawler( # (1)
 client=client, # (2)
 connection_name="production", # (3)
 admin_roles=[client.role_cache.get_id_for_name("$admin")], # (4)
 admin_groups=None,
 admin_users=None,
 )
 .direct( # (5)
 hostname="example.online.tableau.com",
 site="atlan-site",
 port=443,
 ssl_enabled=True,
 )
 .basic_auth( # (6) # (7)
 username="atlan-user",
 password="atlan-pass",
 )
 .include(projects=["fc2923bd-cf94-2b29-9cdd-9cc2f7a4f029"]) # (8)
 .exclude(projects=[]) # (9)
 .crawl_unpublished(True) # (10)
 .crawl_hidden_fields(False) # (11)
 .alternate_host(hostname="https://alternate.tableau.com") # (12)
 .to_workflow() # (13)
)
response = client.workflow.run(crawler) # (14)
```

1. Base configuration for a new Tableau crawler.
2. You must provide a client instance.
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.
 - port number of the Tableau instance (use `443` for the default).
 - site to crawl within Tableau.
 - whether to use SSL for the connection.
6. When using `basic_auth()`, you must provide the following information:
 - your username for accessing Tableau.
 - your password for accessing Tableau.

7. 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.
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 None, 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 None, no projects will be excluded.)
10. You can also optionally set whether to
crawl hidden datasource fields (`True`) or not (`False`).
11. You can also optionally set whether to
crawl unpublished worksheets and dashboards (`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. Now, you can convert the package into a `Workflow` object.
14. Run the workflow by invoking the `run()` method on the workflow client, passing the created object.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Kotlin

```kotlin showLineNumbers title="Direct extraction from Tableau"
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(client) // (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. Because this operation will execute work in Atlan, you must [provide it an `AtlanClient`](https://docs.atlan.com/llms/platform/python/set-up-sdk/llms.txt) through which to connect to the tenant.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt) for details on how you can check the status and wait until the workflow has been completed.
 :::

### Raw REST API

:::tip[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

:::warning[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](#re-run-existing-workflow) below).
:::
To crawl Tableau assets from the S3 bucket:

### Java

:::warning[Coming soon]
:::

### Python

```python showLineNumbers title="Crawl assets from the S3 bucket"
from pyatlan.client.atlan import AtlanClient
from pyatlan.model.packages import TableauCrawler

client = AtlanClient()

crawler = (
 TableauCrawler( # (1)
 client=client, # (2)
 connection_name="production", # (3)
 admin_roles=[client.role_cache.get_id_for_name("$admin")], # (4)
 admin_groups=None,
 admin_users=None,
 )
 .s3( # (5)
 bucket_name="test-bucket",
 bucket_prefix="test-prefix",
 bucket_region="test-region",
 )
 .to_workflow() # (6)
)
response = client.workflow.run(crawler) # (7)
```

1. Base configuration for a new Tableau crawler.
2. You must provide a client instance.
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. 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.
6. Now, you can convert the package into a `Workflow` object.
7. Run the workflow by invoking the `run()` method on the workflow client, passing the created object.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Kotlin

:::warning[Coming soon]
:::

### Raw REST API

:::tip[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

To re-run an existing workflow for Tableau assets:

### Java

```java showLineNumbers title="Re-run existing Tableau workflow"
List existing = WorkflowSearchRequest // (1)
 .findByType(client, TableauCrawler.PREFIX, 5); // (2)
// Determine which of the results is the Tableau workflow you want to re-run...
WorkflowRunResponse response = existing.get(n).rerun(client); // (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. Because this operation will execute work in Atlan, you must [provide it an `AtlanClient`](https://docs.atlan.com/llms/platform/python/set-up-sdk/llms.txt) through which to connect to the tenant.

 - Optionally, you can use the `rerun(client, 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`

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt) for details on how you can check the status and wait until the workflow has been completed.
 :::

### Python

```python showLineNumbers title="Re-run existing Tableau workflow"
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`.

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt)
for details on how you can check the status and wait until the workflow has been completed.
 :::

### Kotlin

```kotlin showLineNumbers title="Re-run existing Tableau workflow"
val existing = WorkflowSearchRequest // (1)
 .findByType(client, TableauCrawler.PREFIX, 5) // (2)
// Determine which of the results is the
// Tableau workflow you want to re-run...
val response = existing.get(n).rerun(client) // (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. Because this operation will execute work in Atlan, you must [provide it an `AtlanClient`](https://docs.atlan.com/llms/platform/python/set-up-sdk/llms.txt) through which to connect to the tenant.

 - Optionally, you can use the `rerun(client, 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`

 :::warning[Workflows run asynchronously]
Remember that workflows run asynchronously. See the [packages and workflows introduction](https://docs.atlan.com/llms/platform/python/packages/llms.txt) for details on how you can check the status and wait until the workflow has been completed.
 :::

### Raw REST API

:::warning[Requires multiple steps through the raw REST API]
1. Find the existing workflow.
2. Send through the resulting re-run request.
:::
```json showLineNumbers title="POST /api/service/workflows/indexsearch"
{
 "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.

 :::tip[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.)
 :::
```json title="POST /api/service/workflows/submit"
{
 "namespace": "default",
 "resourceKind": "WorkflowTemplate",
 "resourceName": "atlan-tableau-1684500411" // (1)
}
```

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

---