Key Takeaways
- Idempotency prevents duplicate records: design integrations so processing the same event twice produces the same result. Use unique identifiers and check-before-create patterns.
- Retry with exponential backoff: transient failures are normal. Retry after 1s, 2s, 4s, 8s with jitter to avoid thundering herd problems.
- Webhooks over polling: prefer event-driven webhooks for real-time updates. Polling wastes API quota and delays data synchronization.
- Monitor integration health: track success rates, response times, and error rates. Alert on anomalies before they become business problems.
- Document everything: purpose, data flow, field mappings, error procedures, and provider contacts. Future you will be grateful.
Authentication and Security
| Auth Method | Use Case | Security Level |
|---|---|---|
| API Keys | Server-to-server, simple integrations | Medium |
| OAuth 2.0 | User-delegated access | High |
| OAuth 2.0 + PKCE | Mobile/SPA applications | High |
| JWT Bearer Tokens | Microservice communication | High |
| Mutual TLS | High-security financial/healthcare | Highest |
- Never hardcode credentials: store API keys and secrets in environment variables or a secrets manager (AWS Secrets Manager, HashiCorp Vault)
- Rotate keys regularly: set calendar reminders to rotate API keys every 90 days. Automate rotation where possible.
- Use minimum permissions: request only the API scopes your integration actually needs. Over-permissioned integrations increase breach impact.
- Encrypt in transit: enforce HTTPS for all API communication. Never send credentials or sensitive data over unencrypted connections.
Error Handling and Retry Logic
Production integrations must handle failures gracefully. APIs go down, rate limits are hit, and data can be malformed. Your integration should recover automatically from transient issues.
| Error Type | HTTP Codes | Strategy |
|---|---|---|
| Rate limited | 429 | Retry after Retry-After header, exponential backoff |
| Server error | 500, 502, 503 | Retry with backoff, alert after 3 failures |
| Client error | 400, 422 | Log error, fix data, do not retry same request |
| Auth failure | 401, 403 | Refresh token, re-authenticate, alert if persistent |
| Timeout | 408, gateway timeout | Retry with longer timeout, check idempotency |
| Not found | 404 | Log, skip record, do not retry |
Retry delays should follow: delay = min(base * 2^attempt + random_jitter, max_delay). Start at 1 second, cap at 60 seconds, add random jitter (0-1s). This prevents hundreds of failed requests from hammering the API simultaneously when it recovers.
Data Mapping and Transformation
Data mapping requires careful planning to avoid silent data corruption:
- Document field mappings: create a spreadsheet mapping every source field to its destination field, including data types and transformations
- Handle data type conversions explicitly: dates (timezone-aware), currencies (decimal precision), phone numbers (international format), and enums (map values, not labels)
- Validate before sending: check required fields, format constraints, and business rules before pushing data to avoid errors that corrupt downstream systems
- Handle nulls and defaults: define behavior for missing fields. Should null values clear the destination field, skip the update, or apply a default?
- Character encoding: enforce UTF-8 throughout. Special characters, accents, and emoji cause silent data corruption if encodings mismatch
80% of integration bugs come from 20% of edge cases: null values, special characters, timezone conversions, and unexpected data formats. Test your integration with messy real data, not clean test data.
Webhooks vs Polling: Choosing the Right Pattern
| Factor | Webhooks | Polling |
|---|---|---|
| Real-time | Yes (event-driven) | No (interval-based) |
| API quota usage | Minimal | High (constant requests) |
| Complexity | Requires endpoint + verification | Simple loop |
| Reliability | Can miss events (need retry) | Catches everything eventually |
| Server requirements | Must expose public endpoint | No server needed |
| Best for | Real-time sync, notifications | Batch processing, simple setups |
When using webhooks, implement a verification mechanism (HMAC signature validation), process events idempotently (duplicate delivery is common), and store raw payloads before processing. This enables replay if processing fails.
Monitoring, Alerting, and Documentation
Production integrations need visibility. Without monitoring, failures are invisible until users report missing data.
| Monitor | Alert Threshold | Tool Options |
|---|---|---|
| Success rate | Below 95% | Datadog, New Relic, custom |
| Response time | Above 5 seconds (p95) | APM tools |
| Error rate | Above 5% for 15 minutes | PagerDuty, Opsgenie |
| Queue depth | Above normal for 30 min | CloudWatch, Prometheus |
| Data freshness | Older than SLA threshold | Custom health checks |
Documentation is the most neglected aspect of integrations. For every integration, document: purpose, data flow diagram, field mappings, error handling procedures, API provider contact info, and escalation process.
Create a runbook for each critical integration with: common failure scenarios and fixes, how to re-process failed events, contact info for the API provider, and steps to verify the integration is healthy. This enables any team member to troubleshoot, not just the original developer.
Frequently Asked Questions
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