### Step 2: Select a Service and Connector
From the service type dropdown, select **Pipeline Services**, then click the **Data Factory** connector tile.
### Step 3: Add Service Name and Description
* Enter a unique, descriptive **Service Name**. Collate identifies services by their service name. Enter a name that distinguishes this deployment from other Data Factory services you are ingesting metadata from.
* Optional: Enter a **Description** for the service.
#### Enter Connection Details
Enter the connection details for Data Factory. The right-hand panel in the UI displays inline help for each field.
* **Subscription ID**: Your Azure subscription’s unique identifier. In the Azure portal, navigate to Subscriptions > Your Subscription > Overview. You’ll see the subscription ID listed there.
* **Resource Group Name**: This is the name of the resource group that contains your Data Factory instance. In the Azure portal, navigate to Resource Groups. Find your resource group, and note the name.
* **Azure Data Factory Name**: The name of your Data Factory instance. In the Azure portal, navigate to Data Factories and find your Data Factory. The Data Factory name will be listed there.
* **Azure Data Factory pipeline runs day filter**: The days range when filtering pipeline runs. It specifies how many days back from the current date to look for pipeline runs, and filter runs within the given period of days. Default is `7` days. `Optional`
## Azure Data Factory Configuration
* **Client ID**: To get the Client ID (also known as application ID), follow these steps:
1. Log into [Microsoft Azure](https://ms.portal.azure.com/#allservices).
2. Search for `App registrations` and select the `App registrations link`.
3. Select the `Azure AD` app you're using for this connection.
4. From the Overview section, copy the `Application (client) ID`.
* **Client Secret**: To get the client secret, follow these steps:
1. Log into [Microsoft Azure](https://ms.portal.azure.com/#allservices).
2. Search for `App registrations` and select the `App registrations link`.
3. Select the `Azure AD` app you're using for this connection.
4. Under `Manage`, select `Certificates & secrets`.
5. Under `Client secrets`, select `New client secret`.
6. In the `Add a client secret` pop-up window, provide a description for your application secret. Choose when the application should expire, and select `Add`.
7. From the `Client secrets` section, copy the string in the `Value` column of the newly created application secret.
* **Tenant ID**: To get the tenant ID, follow these steps:
1. Log into [Microsoft Azure](https://ms.portal.azure.com/#allservices).
2. Search for `App registrations` and select the `App registrations link`.
3. Select the `Azure AD` app you're using for this connection.
4. From the `Overview` section, copy the `Directory (tenant) ID`.
* **Account Name**: Here are the step-by-step instructions for finding the account name for an Azure Data Lake Storage account:
1. Sign in to the Azure portal and navigate to the `Storage accounts` page.
2. Find the Data Lake Storage account you want to access and click on its name.
3. In the account overview page, locate the `Account name` field. This is the unique identifier for the Data Lake Storage account.
4. You can use this account name to access and manage the resources associated with the account, such as creating and managing containers and directories.
### Step 5: Configure Ingestion Options
In the **What to Ingest** step, use filter patterns to control which assets Collate ingests from your pipeline service. Filter patterns use regular expressions applied to pipeline names.
#### How Filter Patterns Work
* **Include**: Add one or more comma-separated regular expressions. Collate ingests only assets whose names match at least one expression. Leave blank to include all assets.
* **Exclude**: Add one or more comma-separated regular expressions. Collate skips any asset whose name matches an expression. Leave blank to exclude nothing.
Rules match asset names using one of five expressions:
* **contains**: matches any name containing the value. For example, `sales` matches `my_sales_data` and `sales_2024`.
* **starts with**: matches names beginning with the value. For example, `prod_` matches `prod_db` and `prod_schema`.
* **ends with**: matches names ending with the value. For example, `_raw` matches `events_raw` and `logs_raw`.
* **is exactly**: matches the exact name only. For example, `analytics` matches only `analytics`.
* **matches regex**: matches names using a regular expression. For example, `^prod_.*_v\d+$` matches `prod_events_v1`.
When both Include and Exclude are set, Exclude takes priority.
For some services, the dropdown is not available and clicking **Add Agent** takes you directly to the agent configuration page.
4. On the **Configure Ingestion** page, do the following and click **Next**.
* **Name this Ingestion**: Enter a unique recognizable name for this ingestion pipeline.
* **Agent Setup**: Configure the core parameters for this agent. The following fields are available:
| Field | Default | Description |
| ---------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Ownership Update Mode | replace | Set how owners from source metadata update Pipeline owners. In replace mode, resolved owners from the current source replace existing owners. In append mode, resolved owners are appended to active existing Pipeline owners. |
| Db Service Names | — | List of database service names used for lineage resolution. |
| Storage Service Names | — | List of storage service names used for lineage resolution. |
| Messaging Service Names | — | List of messaging service names used for lineage resolution. |
| Status Lookback Days | 1 | Number of days of pipeline run status history to ingest. Only runs within the last N days will be fetched. |
| Include Lineage | On | Turn off to stop fetching lineage from pipelines. |
| Mark Deleted Pipeline | On | Soft-delete pipelines in Collate when they are removed from the source. Associated entities like lineage are also deleted. |
| Include UnDeployed Pipelines | On | Toggle whether un-deployed pipelines should be ingested. If set to false, only deployed pipelines will be ingested. |
* **Filter Patterns**: Apply include or exclude rules to scope which pipelines this agent ingests. These follow the same filter options described in **Step 5**.
* **Scope & Behaviour**: Control what metadata to include and how to handle deletions. Toggle each option on or off based on your needs:
| Toggle | Default | Description |
| ----------------- | ------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Enable Debug Log | Off | Sets the ingestion log level to DEBUG. Useful for troubleshooting. |
| Override Metadata | Off | When on, source values overwrite existing descriptions, tags, owners, and display names in Collate. When off, Collate only updates fields that have no existing value. |
| Override Lineage | Off | When on, existing lineage is replaced with newly extracted lineage on each run. |
5. On the **Schedule Interval** page, set when the agent runs:
* **Schedule**: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression.
* **On-Demand**: No automatic schedule; trigger the agent manually when needed.
6. Click **Add** to deploy the agent.
## Displaying Lineage Information
Steps to retrieve and display the lineage information for a Data Factory service.
1. Ingest Source and Sink Database Metadata: Identify both the source and sink database used by the Azure Data Factory service for example Redshift. Ingest metadata for these databases.
2. Ingest Data Factory Service Metadata: Finally, Ingest your Data Factory service.
By successfully completing these steps, the lineage information for the service will be displayed.
### Missing Lineage
If lineage information is not displayed for a Data Factory service, follow these steps to diagnose the issue.
1. **Permissions**: Ensure that the service principal or managed identity you’re using has the necessary permissions in the Data Factory resource. (Reader, Contributor, or Data Factory Contributor role at minimum).
2. **Metadata Ingestion**: Ensure that metadata for both the source and sink database is ingested and passed to the lineage system. This typically involves configuring the relevant connectors to capture and transmit this information.
3. **Run Successful**: Ensure that the Pipeline Run is successful.
## Troubleshooting