Centralized failure logging for Azure data pipelines

solanki srikanth 0 Reputation points
2026-10-06T07:52:29.4566667+00:00

One thing we have been tightening at DataToBiz is how pipeline failures are captured across different workloads.

Instead of making each pipeline responsible for its own logging structure, we have been looking at a common failure record containing the pipeline/run identifier, activity, source, failure type, timestamp, and error details.

The useful part is being able to distinguish an actual data failure from an orchestration or dependency failure before someone starts debugging the transformation itself.

For teams running a larger number of ADF pipelines, what fields have you found essential in a shared failure log?

Azure Data Factory
Azure Data Factory

An Azure service for ingesting, preparing, and transforming data at scale.

Answer recommended by moderator
Maksood Ahmed 195 Reputation points Microsoft External Staff Moderator
2026-10-07T15:09:18.9066667+00:00

Hi @solanki srikanth

For multiple Azure Data Factory pipelines, keeping a centralized failure log is a good approach.

I would keep the log simple and capture the key details:

  • Pipeline name and Run ID
  • Activity name and Activity Run ID
  • Start/end time and status
  • Error code and error message
  • Failure type, such as data, dependency, authentication, or orchestration
  • Source and destination
  • Retry count and correlation ID

For centralized monitoring, you can also send ADF pipeline, activity, and trigger logs to Azure Monitor / Log Analytics. This makes it easier to search failures across multiple pipelines and build alerts or reports.

FYR:

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  1. Alex Burlachenko 25,530 Reputation points MVP Volunteer Moderator
    2026-10-09T11:24:09.3333333+00:00

    Hi solanki srikanth & thx for join me at Q&A platform,

    For ADF, I'd definitely keep PipelineRunId and ActivityRunId as separate fields. They're probably the most useful ones when u need to trace a failure back to the exact execution, especially with retries or nested pipelines.

    I'd also capture TriggerName, TriggerType, ActivityType, IntegrationRuntime, environment, error code, error message, start/end timestamps, and retry count. For data-related failures, source and sink identifiers help a lot, but I'd avoid logging connection strings or anything sensitive.

    One thing that's easy to miss is the difference between the original failure and the final pipeline status. A pipeline might fail bc a dependency timed out, then recover on retry. If u only log the final result, that issue disappears from ur failure history.

    For classification, I'd keep a simple FailureCategory field with values like Data, Connectivity, Authentication, Timeout, Dependency, and Orchestration. Don't rely entirely on the raw ADF error message, bc those can be inconsistent across activities. Btw, I'd centralize this in Log Analytics or a dedicated logging table rather than having every pipeline write its own custom JSON. Makes cross-pipeline troubleshooting and trend analysis way easier.

    rgds,

    Alex

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