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ADF integration in data-observability docs - #38642

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ADF integration in data-observability docs#38642
mobuchowski wants to merge 1 commit into
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mobuchowski/adf-jobs-monitoring-docs

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@mobuchowski mobuchowski commented Jul 31, 2026

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What does this PR do? What is the motivation?

Adds the Azure Data Factory setup documentation for the Data Observability product.

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  • Ready for merge

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Used Claude Code for initial draft, iterated on it manually.

@github-actions github-actions Bot added Architecture Everything related to the Doc backend Images Images are added/removed with this PR labels Jul 31, 2026
Add a setup page for Data Observability: Jobs Monitoring with Azure Data
Factory, covering the Azure role assignment the integration needs, the
in-app configuration steps, what Datadog collects, and dataset lineage.

Also add the page to the Jobs Monitoring nav and card grid, and list
Azure Data Factory among the integrations that feed Lineage.

Signed-off-by: Maciej Obuchowski <maciej.obuchowski@datadoghq.com>
@mobuchowski
mobuchowski force-pushed the mobuchowski/adf-jobs-monitoring-docs branch from 478a9d8 to e19bca0 Compare July 31, 2026 16:01
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mobuchowski marked this pull request as ready for review July 31, 2026 16:50
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mobuchowski requested a review from a team as a code owner July 31, 2026 16:50

Without them, Datadog can list your factories and pipelines but collects no runs.

### Option 1: Create a custom role (recommended)

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UX: Can we use the Datadog tabs to toggle "Create a custom role (recommended)" and "Use a built-in role" instead of listing all options vertically?


## What Datadog collects

After setup, Datadog refreshes your data factory inventory once a day: factories, pipelines, datasets, linked services, data flows, and triggers. New pipeline and activity runs are collected every few minutes.

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Q: Does this mean if you spin up a new pipeline, you have to wait a day to see the runs? That feels odd. Anyway we can see new pipeline faster?

- The pipeline run and each of its activity runs, with start time, end time, and duration.
- The run status, and the error message for failed runs.
- The trigger that started the run.
- An `env` tag set to the name of the data factory.

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No custom tags?


For activities that move data, such as a copy, Datadog reports the source and sink datasets. For activities that run SQL, Datadog reports the tables the statement read and wrote.

Datadog resolves datasets across the major warehouse, database, and object storage connectors, including Snowflake, Google BigQuery, Amazon Redshift, SQL Server and Azure SQL, Azure Databricks Delta Lake, Amazon S3, Azure Blob Storage, and Azure Data Lake Storage. Assets resolve to the same entities that Datadog's other Data Observability integrations report, so lineage connects across your data platforms.

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format technologies this as bulleted list

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