ADF triggers failing after migrating to new resource group in same subscriptioin

Hi VickyD, Thank you so much for your query. Below is the research you can consider for your query.

What's happening is that the debug runs in your pipeline are using a different execution environment than the runs triggered manually or on schedule. That’s the root of the problem.

In debug mode, ADF spins up a separate Spark cluster (called a debug cluster) which is usually pre-configured and often more forgiving. It also tends to use your own credentials or the context of your session. But when you trigger a pipeline either manually or via a scheduled trigger ADF creates a new, production-grade Spark cluster and runs the pipeline as its managed identity.

That change in environment introduces a few potential failure points:

• Access or permission issues: For example, maybe the ADF managed identity doesn't have permission to read from your source or write to the sink, while it worked fine in debug because it used your personal access.
• Networking or IR misconfiguration: If your integration runtime (IR) is tied to a virtual network, the triggered cluster might not be able to reach your source or destination if NSGs, firewalls, or DNS settings aren't correct. Again, in debug, this might not be an issue if it's bypassing those network rules.
• Parameter issues: If your pipeline or data flow is parameterized, it’s possible that the trigger isn’t passing values correctly or at all and the defaults aren't sufficient. That often causes internal errors because the pipeline ends up with unexpected nulls or empty paths.

Sink or data write behavior: Sometimes the sink configuration (like file naming, partitioning, or staging) behaves differently when the cluster scales out in a production run, especially with Parquet/Delta formats.

ADF logs don’t always spell it out clearly and just show "internal error," but if you look at the output or logs for the failed activity in Monitor, you’ll usually see more useful details. Quick steps to isolate the issue:

• Try running the same pipeline with a manual trigger (not debug) and pass in the exact same parameters. This helps confirm if the problem is environment specific.
• Look at the Monitor > Output > Activity > Error details—you might find more meaningful clues.
• If needed, you can enable logging to a blob storage for the Data Flow activity to get detailed Spark execution logs.
• Kindly check if the was error with web hook in the repository. Updated the webhook and it is working fine now if you are using.
• You can refer Documentation

Let me know if any of this won't help or if you’re seeing any specific error messages! Happy to help you.

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Hi VickyD, We haven’t heard from you on the last response and was just checking back to see if you have a resolution yet. In case if you have any resolution, please do share that same with the community as it can be helpful to others. Otherwise, will respond with more details.

happy to help you.