Great questions! Let me provide a comprehensive overview of our implementation covering all these aspects.
API Automation Architecture:
We built a Python-based orchestration system using these components:
- Scheduler: APScheduler for job scheduling
- API Client: Custom wrapper around requests library for Cognos API calls
- Queue Manager: RabbitMQ for distributing report generation tasks
- Worker Pool: 5 worker processes that execute reports in parallel
- Storage: PostgreSQL for configuration and execution history
- Notification: Integration with Slack and email (SMTP)
The workflow:
Scheduler → Queue → Workers → API Calls → Export → Distribute → Log
Scheduled Reports Implementation:
Configuration structure (JSON):
{
"reports": [
{
"id": "i8A1B2C3D4E5F",
"name": "Daily Sales Report",
"schedule": "0 6 * * *",
"parameters": {
"region": "{{DYNAMIC:user.region}}",
"date": "{{DYNAMIC:yesterday}}"
},
"outputs": [
{"format": "PDF", "recipients": ["exec@company.com"]},
{"format": "XLSX", "recipients": ["sales@company.com"]}
]
}
]
}
The system supports:
- Cron-style schedules for flexible timing
- Dynamic parameter resolution (dates, user attributes, etc.)
- Multiple output formats per report
- Conditional execution based on data availability
Error Handling Strategy:
Implemented at multiple levels:
- Parameter Validation (before execution):
def validate_parameters(report_id, params):
response = cognos_api.post(
f'/api/v1/reports/{report_id}/validate',
json={'parameters': params}
)
if not response.ok:
raise ValidationError(response.json()['errors'])
- Execution Retry Logic:
@retry(stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=60))
def execute_report(report_id, params):
response = cognos_api.post(
f'/api/v1/reports/{report_id}/execute',
json={'parameters': params, 'async': True}
)
return response.json()['executionId']
- Failure Notifications:
- Immediate Slack alert for critical reports
- Daily digest email with all failures
- Dashboard showing execution status by report
- Graceful Degradation:
- If a report fails, other reports continue
- Partial success (e.g., PDF succeeds, Excel fails) is handled
- Failed jobs are queued for manual review
Multi-Format Export Management:
Format-specific handling:
- Format Configuration (stored in report metadata):
{
"format": "XLSX",
"options": {
"includeFormatting": true,
"includeLayout": true,
"columnWidths": "auto",
"sheetName": "Sales Data"
}
}
- Async Export for Large Reports:
def export_report(execution_id, format):
# Submit export request
export_response = cognos_api.post(
f'/api/v1/executions/{execution_id}/export',
json={'outputFormat': format, 'async': True}
)
export_id = export_response.json()['exportId']
# Poll for completion
while True:
status = cognos_api.get(
f'/api/v1/exports/{export_id}/status'
).json()
if status['state'] == 'completed':
return download_export(export_id)
elif status['state'] == 'failed':
raise ExportError(status['error'])
time.sleep(10)
- Format-Specific Processing:
- PDF: Add watermarks, merge multiple reports
- Excel: Apply cell formatting, add summary sheets
- CSV: Handle encoding, delimiter options
Infrastructure and Scaling:
Deployment architecture:
- Environment: On-premises (Docker containers)
- Orchestration: Kubernetes for container management
- Scaling: Horizontal scaling of worker pods based on queue depth
- Resource Limits: Max 10 concurrent API calls to Cognos (to avoid overload)
Load management:
class CognosAPIClient:
def __init__(self, max_concurrent=10):
self.semaphore = asyncio.Semaphore(max_concurrent)
async def execute_report(self, report_id, params):
async with self.semaphore:
# Only max_concurrent requests execute simultaneously
return await self._execute(report_id, params)
Peak time handling:
- Stagger report execution (high-priority reports first)
- Pre-generate reports during off-peak hours when possible
- Cache frequently-requested reports
Distribution Implementation:
- Email Distribution:
def distribute_via_email(report_file, recipients, metadata):
msg = EmailMessage()
msg['Subject'] = f"{metadata['name']} - {date.today()}"
msg['From'] = 'reports@company.com'
msg['To'] = ', '.join(recipients)
msg.set_content(f"Please find attached {metadata['name']}")
with open(report_file, 'rb') as f:
msg.add_attachment(
f.read(),
maintype='application',
subtype='pdf',
filename=f"{metadata['name']}.pdf"
)
smtp.send_message(msg)
- File Share Distribution:
- Mount network shares in worker containers
- Organize by date and report type
- Set appropriate file permissions
Monitoring and Observability:
Metrics tracked:
- Report execution success rate (target: >98%)
- Average execution time by report
- Export time by format
- Distribution success rate
- API response times
Dashboard built in Cognos showing:
- Daily execution summary
- Failed reports with error details
- Execution time trends
- Top slowest reports
Results and Benefits:
- Time Savings: 20 hours/week manual effort eliminated
- Reliability: 99.2% success rate (vs. ~85% manual)
- Speed: Reports delivered in 10-30 minutes vs. 2-4 hours
- Scalability: Can handle 200+ reports/day with current infrastructure
- Audit Trail: Complete execution history for compliance
Lessons Learned:
- Start with a small subset of reports to prove the concept
- Invest in robust error handling early - it pays off
- Monitor Cognos server load and implement rate limiting
- Use async API calls for any report that takes >30 seconds
- Keep configuration in version control (Git)
- Build a simple UI for non-technical users to view execution status
- Document API usage patterns for capacity planning
The entire system runs with minimal maintenance - we spend about 2 hours/week on it vs. the 20+ hours of manual work it replaced. Happy to provide code samples for specific components if helpful!