Datafactory Connector

This section provides guides and references to use the Azure Data Factory connector. Configure and schedule Azure Data Factory metadata workflows from the Collate UI:

• Requirements
  • Data Factory Versions
• Metadata Ingestion
• Troubleshooting

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Requirements

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Data Factory Versions

The Ingestion framework uses Azure Data Factory APIs to connect to the Data Factory and fetch metadata. You can find further information on the Azure Data Factory connector in the docs.

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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).

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Metadata Ingestion

To ingest metadata from Data Factory, you need to create a service connection. The service connects Data Factory with Collate. Once you create a service, Collate automatically starts ingesting metadata.

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Step 1: Add New Service

1. In the left navigation, click Connections.
2. On the Connections page, click Add New Service.

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Step 2: Select a Service and Connector

From the service type dropdown, select Pipeline Services, then click the Data Factory connector tile.

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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.

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Step 4: Configure Connection Options

Specify where ingestion runs, provide your source credentials, and verify the connection.

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Select Ingestion Runner

Select an Ingestion Runner: the runner where the ingestion pipeline will execute.

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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

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Azure Data Factory Configuration

• Client ID: To get the Client ID (also known as application ID), follow these steps:

1. Log into Microsoft Azure.
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.
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.
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.

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Test Connection

Once the credentials have been added, click on Test Connection and Save the changes.

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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.

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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. Filter Options The Pipeline section includes the following filter options:

• Pipeline: Controls which pipelines (DAGs, jobs, or workflows) Collate ingests from the source.

Each section provides the following controls:

• Scan Mode: You can choose between the following scan modes:
  • Scan all: Ingests every asset of that type the connector can access. This is the default.
  • Only specific: Enables include rules so only assets matching at least one rule are ingested.
• Exclude system toggle: Use this toggle to automatically filter out system-reserved names defined by the connector — for example, Exclude system databases for the Databases section.
• Always exclude: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules.
• Preview: Shows a real-time summary of what will be in scope based on your current rules.
• Include rules (available only in Only specific mode): Click + Add to define a rule. Added rules appear as chips; an asset is included if it matches any rule.

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Step 6: Create & Deploy

Click Create & Deploy to deploy the agent and start the first metadata ingestion run. Collate saves the service configuration and immediately begins pulling metadata from the source. To monitor ingestion progress or view the service you just added, go to Connections in the left navigation and select your service.

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Configure Metadata Agent and Schedule Ingestion

The Metadata Agent extracts pipelines, tasks, and other structural metadata from your source and keeps your Collate catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click Create & Deploy, Collate automatically deploys a Metadata Agent for this service and triggers the first ingestion run. View its status and run history from the Agents tab on the service detail page. To configure the additional Metadata Agent and schedule ingestion, follow these steps:

1. In the left navigation, click Connections and select your service.
2. Click the Agents tab.
3. Click Add Agent and select Metadata from the dropdown. 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.

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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.

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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.

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Troubleshooting