Azure Data Factory

Euno's Azure Data Factory (ADF) integration auto-discovers your data factories and resolves the lineage of the tables your pipelines and data flows read and write. It discovers the following resources:

• Data factories

• Pipelines

• Datasets

• Mapping Data Flows

• Power Query (Wrangling) Data Flows

• The warehouse tables and columns your pipelines and data flows read and write

Euno reads factory metadata through the Azure Resource Manager API only. It never modifies your factories, and it never runs pipelines.

Prerequisites

To connect a source, you need a Microsoft Entra (Azure AD) application (service principal) that can read the data factories you want Euno to discover:

• An app registration with a client secret.

• Read access to the target factories — the built-in Reader role assigned at the subscription, resource group, or individual factory scope is sufficient.

• The Subscription ID that contains the factories.

Setting up Euno's Azure Data Factory integration

Step 1: Access the Sources page

1. Go to the Sources page.

2. Click Add New Source and select Azure Data Factory from the list of supported platforms.

Step 2: General configuration

Asterisk (*) means a mandatory field.

Configuration	Description
Name*	A name for your Azure Data Factory source (e.g., "Azure Data Factory")
Tenant ID*	Your Microsoft Entra tenant identifier
Client ID*	The Azure AD application (service principal) client identifier
Client Secret*	The Azure AD application secret
Subscription ID*	The Azure subscription ID that contains the factories to crawl

Step 3: Advanced configuration (optional)

Configuration	Description
Crawl Data Flows	Whether to discover Mapping Data Flows and Power Query (Wrangling) Data Flows and resolve their lineage. Enabled by default.
Resource Group Pattern	Allow/deny regular expressions on resource group names. .* matches everything; add a deny pattern to exclude specific resource groups.
Factory Name Pattern	Allow/deny regular expressions on factory names within the filtered resource groups.
Linked Service to Platform Instance	Manual mapping from a Linked Service to a canonical warehouse URI prefix, for the cases Euno can't auto-resolve (see the note below).
Runtime Parameter Overrides	Per-pipeline overrides for runtime-only parameters whose defaults aren't representative, as a nested object: { "pipelineName": { "paramName": "value" } }.

When is a Linked Service mapping needed? Euno resolves most warehouse tables automatically from the dataset's Linked Service. Supply a mapping only when the payload doesn't carry enough to resolve — for example an Azure Blob Storage Linked Service (Azure redacts connection strings) or an Azure Databricks Delta Lake Linked Service (the dataset doesn't carry the catalog). Provide only the prefix parts the dataset lacks; Euno appends the database, schema, and table from the dataset itself. Use the Linked Service name to apply the mapping across all factories, or factoryName/linkedServiceName to scope it to one factory when two factories share a Linked Service name.

Step 4: Schedule

Set how often Euno re-crawls the source to keep discovered resources and lineage current.

Step 5: Resource cleanup

To keep your data relevant and free of outdated resources, Euno provides automatic resource cleanup options. These settings determine when a resource is removed if it is no longer detected by a source integration run. For a detailed explanation of Euno's cleanup strategies, see Resource Sponsorship in Euno.

• Immediate Cleanup: Remove resources not detected in the most recent successful source integration run.

• No Cleanup: Keep all resources indefinitely, even if they are no longer detected.

Step 6: Save configuration

Click Save to complete the setup.

What Euno discovers

Once configured, the integration discovers, for each factory it can access:

• Factories, pipelines, datasets, and data flows — the full inventory, with each pipeline and data flow linked to its parent factory.

• Table-level lineage — for every table a pipeline or data flow writes, the source tables it reads. Euno resolves this from Copy activities, Script activities, Mapping Data Flows, and Power Query (Wrangling) Data Flows, including embedded native SQL queries.

• Column-level lineage — where the transformation names its columns (for example a Power Query rename, or a Copy activity with a column mapping), the source column each output column derives from.

• Definer relationships — the pipeline or data flow that writes a table is recorded as its definer, so you can navigate from a table to the ADF resource that produces it.

The warehouse tables and columns ADF resolves are emitted by reference and merge with the same tables discovered by your warehouse integration (Snowflake, BigQuery, Databricks, Azure SQL, Azure Synapse), so ADF lineage appears alongside the rest of your graph.

For the full list of discovered resource types, their properties, and their relationships, see Azure Data Factory Integration Discovered Resources.