Hey everyone. Still getting my feet wet with cloud ops, but my team just streamlined our financial close process and I thought I’d share the technical bits. We got our month-end reconciliation between Expensify and NetSuite down from a couple of days to under 4 hours. The key was automating the data sync and validation.
We use a scheduled AWS Lambda function (Python) to pull the approved expense report data from Expensify's API, transform it, and push it to NetSuite via a RESTlet. The most important part was adding validation checks before the sync runs. Here’s a snippet of our core validation logic:
```python
def validate_expense_report(report):
# Check critical fields
required_fields = ['id', 'amount', 'date', 'customer_id']
for field in required_fields:
if field not in report or report[field] is None:
return False, f"Missing required field: {field}"
# Validate amount is positive
if report['amount'] <= 0:
return False, f"Invalid amount for report ID: {report['id']}"
# Check date is within the closing period
report_date = parse_date(report['date'])
if not (period_start <= report_date <= period_end):
return False, f"Report date out of closing period: {report['id']}"
return True, "Valid"
```
We run this in a Step Functions workflow that stops if validation fails, sending an alert to our #finance-ops Slack channel. We also built a simple dashboard in CloudWatch to track sync status. This automation, plus strict policies in Expensify (like required receipts for anything over $25), cut our manual review time way down. Curious if others have done something similar, especially around handling failed transactions or cost tracking for the Lambda jobs themselves 😅
That sounds like a huge time saver! I'm looking at a similar automation for our old Access database migrations. When you pull from Expensify's API, did you run into any issues with rate limits or pagination that slowed things down at first? That's one of my worries.
Also, the validation check for the closing period is smart. How do you handle edge cases, like an expense date being exactly on the period_end timestamp? Does your parse_date function account for time zones?
One step at a time
"Under 4 hours" is cute until someone's expense date lands in a daylight savings gap and your parse_date function chokes. What's the fallback when validation fails, does the whole job bomb out or do you have manual cleanup waiting?
Also, AWS Lambda for a monthly job? You're paying for a serverless bill to avoid running a 20 line cron script on a box you already own.
-- old school