Data Export/Import Batch Job Monitoring and Retry
Scheduled batch jobs that export data from one system and import it into another run unattended overnight or on a recurring cycle, and most monitoring set up for them checks whether the job itself completed, not whether it actually processed every record it was supposed to. A batch job that times out halfway through, hits a rate limit partway into a large file, or silently skips malformed records instead of erroring on them, reports as a successful completion because the job process itself didn't crash, and the actual gap in data only surfaces when someone downstream notices a report is short or a record they expect isn't there, at which point reconstructing exactly what was missed from a routine overnight run is its own investigation.
STARTING PRICE
From €99
Starter tier · Single-workflow automation, one core integration, fast turnaround.
Get a quote →Saves roughly 3-6 hrs/week for teams running multiple scheduled batch jobs, plus avoided downstream reporting errors from undetected gaps.
How the automation works
We monitor scheduled export/import batch jobs for the difference between 'the job finished' and 'the job actually processed everything it should have,' comparing expected record counts against what was actually exported and imported, rather than relying on the job's own exit status. Partial failures, a job that stops partway through a large file due to a timeout or rate limit, are caught by this count comparison even when the job process itself reports success. For failure types known to be safely retryable, a timeout, a transient connection error, a targeted retry of just the affected records runs automatically rather than requiring a full batch rerun; anything that looks like a genuine data problem is flagged for human review instead.
Process flow
- 01
Batch job monitoring scheduled trigger
Monitoring runs aligned to each scheduled batch job's completion, independent of the job's own self-reported exit status.
- 02
Expected vs. actual record count check integration
The expected record count (from the source query or file) is compared against what actually landed in the destination, catching partial failures that the job itself reported as a clean completion.
- 03
Classify failure type ai
Detected gaps are classified by likely cause, timeout, rate limit, malformed record skip, connection drop, to determine whether an automatic retry is safe or the issue needs human review.
- 04
Targeted retry for safe failure types integration
For failure types known to be safely retryable, a targeted retry of just the affected records or file segment runs automatically, rather than rerunning the entire batch job from scratch.
- 05
Alert on unresolved gaps output
Gaps that aren't resolved by automatic retry, or that were classified as needing human review, generate an alert with the specific missing records identified, not just a generic failure notice.
Inputs
- Batch job schedule and expected record counts/source queries
- Destination system access to verify actual imported counts
- Failure types approved for automatic retry
- Alert escalation contact
Outputs
- Batch job health monitoring log
- Expected-vs-actual count discrepancy alerts
- Automatic retry log for resolved gaps
- Unresolved gap alert with specific missing records
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- A batch job's own exit status reflects whether the job process crashed, not whether every record it was supposed to process actually landed correctly, a job that times out after processing 80% of a file and stops there without erroring will report as a completed success while a fifth of the data never made it.
- Rate limiting partway through a large export or import is a common and specifically silent failure mode, the job doesn't crash, it just stops receiving or sending records past the limit and either queues them indefinitely or drops them, depending on how the integration was built, neither of which shows up in a simple completion check.
- Auto-retrying every detected gap without classifying the likely cause first risks repeatedly retrying something that will fail identically every time, a genuinely malformed source record, which wastes cycles and can mask the real underlying data quality issue that actually needs fixing at the source.
- The longer a partial batch failure goes undetected, the more subsequent scheduled runs compound on top of an already-incomplete dataset, which is why count-based monitoring needs to run on the same schedule as the batch job itself, not as a periodic manual spot-check that could miss several cycles of accumulating gaps.
Frequently asked questions
How is this different from the batch job's own success/failure logging?
The job's own status reflects whether the process crashed, not whether every expected record actually landed; this independently compares expected versus actual record counts, which catches partial failures the job itself reports as successful.
Does it retry failed jobs automatically?
For failure types classified as safely retryable, like a transient timeout or rate limit, yes, a targeted retry of just the affected records runs automatically rather than rerunning the whole batch.
What happens to failures that shouldn't be auto-retried?
They're flagged for human review with the specific cause and affected records identified, rather than retried blindly, since some failures (like a genuinely malformed source record) will fail identically on every retry.
Can this monitor multiple batch jobs across different schedules at once?
Yes, monitoring scales to however many scheduled jobs you're running, each on its own schedule aligned to when that specific job is expected to complete.