Centralized failure logging for Azure data pipelines

Question

Centralized failure logging for Azure data pipelines

solanki srikanth 0

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?

Maksood Ahmed 100 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:

Azure Data Factory monitoring data reference

Pipeline failure and error handling Monitor Azure Data Factory
Maksood Ahmed 100 Reputation points Microsoft External Staff Moderator

2026-10-08T12:30:26.9+00:00

HI @solanki srikanth ,

Just following up to check whether the centralized failure logging approach shared above meets your requirement.

Please let us know if this was helpful or if you need any further assistance with setting up the logging for your ADF pipelines.
Maksood Ahmed 100 Reputation points Microsoft External Staff Moderator

2026-10-09T09:12:57.52+00:00

Hi @solanki srikanth ,

Just following up to check if the above solution helped resolve your query. Please let us know if you need any further assistance.

Thank you!

1 answer

Your answer

Maksood Ahmed 100 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:

Azure Data Factory monitoring data reference

Pipeline failure and error handling Monitor Azure Data Factory
Maksood Ahmed 100 Reputation points Microsoft External Staff Moderator

2026-10-08T12:30:26.9+00:00

HI @solanki srikanth ,

Just following up to check whether the centralized failure logging approach shared above meets your requirement.

Please let us know if this was helpful or if you need any further assistance with setting up the logging for your ADF pipelines.
Maksood Ahmed 100 Reputation points Microsoft External Staff Moderator

2026-10-09T09:12:57.52+00:00

Hi @solanki srikanth ,

Just following up to check if the above solution helped resolve your query. Please let us know if you need any further assistance.

Thank you!

Answer 1

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