In this section, we provide guides and references to use the Tableau connector.
Configure and schedule Tableau metadata and profiler workflows from the OpenMetadata UI:
- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
## Requirements
To ingest tableau metadata, minimum `Site Role: Viewer` is requried for the tableau user.
{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%}
To deploy OpenMetadata, check the Deployment guides.
{%/inlineCallout%}
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.
To create lineage between tableau dashboard and any database service via the queries provided from Tableau Metadata API, please enable the Tableau Metadata API for your tableau server.
For more information on enabling the Tableau Metadata APIs follow the link [here](https://help.tableau.com/current/api/metadata_api/en-us/docs/meta_api_start.html)
### Python Requirements
To run the Tableau ingestion, you will need to install:
**username**: Specify the User to connect to Tableau. It should have enough privileges to read all the metadata.
{% /codeInfo %}
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**password**: Password for Tableau.
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**apiVersion**: Tableau API version.
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**siteName**: Tableau Site Name. To be kept empty if you are using the default Tableau site
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**siteUrl**: Tableau Site Url. To be kept empty if you are using the default Tableau site
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**personalAccessTokenName**: Access token. To be used if not logging in with user/password.
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**personalAccessTokenSecret**: Access token Secret. To be used if not logging in with user/password.
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**env**: Tableau Environment.
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#### Source Configuration - Source Config
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The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/dashboardServiceMetadataPipeline.json):
**dbServiceNames**: Database Service Name for the creation of lineage, if the source supports it.
**dashboardFilterPattern**, **chartFilterPattern**: Note that the they support regex as include or exclude. E.g.,
**includeTags**: Set the Include tags toggle to control whether or not to include tags as part of metadata ingestion.
**markDeletedDashboards**: Set the Mark Deleted Dashboards toggle to flag dashboards as soft-deleted if they are not present anymore in the source system.
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#### Sink Configuration
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To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
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#### Workflow Configuration
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The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
#### 1. Sample config for non-default tableau site
For a non-default tableau site `siteName` and `siteUrl` fields are required.
**Note**: If `https://xxx.tableau.com/#/site/sitename/home` represents the homepage url for your tableau site, the `sitename` from the url should be entered in the `siteName` and `siteUrl` fields in the config below.
We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
## Openmetadata JWT Auth
- JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details [here](/deployment/security/enable-jwt-tokens).
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
```
- You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc [link](/deployment/security/workflow-config-auth).
### 2. Prepare the Ingestion DAG
Create a Python file in your Airflow DAGs directory with the following contents:
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{% codeInfoContainer %}
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#### Import necessary modules
The `Workflow` class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
{% /codeInfo %}
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**Default arguments for all tasks in the Airflow DAG.**
- Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.
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- **config**: Specifies config for the metadata ingestion as we prepare above.
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- **metadata_ingestion_workflow()**: This code defines a function `metadata_ingestion_workflow()` that loads a YAML configuration, creates a `Workflow` object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
{% /codeInfo %}
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- **DAG**: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
- For more Airflow DAGs creation details visit [here](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#declaring-a-dag).
{% /codeInfo %}
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.
{% /codeInfoContainer %}
{% codeBlock fileName="filename.py" %}
```python {% srNumber=13 %}
import pathlib
import yaml
from datetime import timedelta
from airflow import DAG
from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago
try:
from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
from airflow.operators.python_operator import PythonOperator
```
```python {% srNumber=14 %}
default_args = {
"owner": "user_name",
"email": ["username@org.com"],
"email_on_failure": False,
"retries": 3,
"retry_delay": timedelta(minutes=5),
"execution_timeout": timedelta(minutes=60)
}
```
```python {% srNumber=15 %}
config = """
<yourYAMLconfiguration>
"""
```
```python {% srNumber=16 %}
def metadata_ingestion_workflow():
workflow_config = yaml.safe_load(config)
workflow = Workflow.create(workflow_config)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
```python {% srNumber=17 %}
with DAG(
"sample_data",
default_args=default_args,
description="An example DAG which runs a OpenMetadata ingestion workflow",