Systems & API Integration: Reliable, Observable Data Flows

Why Silos Form

Organic tool adoption, no canonical data owners, ad-hoc exports.

Consistency emerges only when contracts & responsibilities are explicit.

Integration Pain Signals

• Frequent CSV import/export cycles
• Conflicting numbers across teams
• Delayed reporting due to merging
• Manual API key management
• High duplication of customer records

Pattern Selection

1. 1. Classify data movements (sync vs async)
2. 2. Map latency & consistency needs
3. 3. Choose pattern (webhook, poll, event, file)
4. 4. Define idempotency keys & retry
5. 5. Implement monitoring & alert thresholds
6. 6. Document contract + versioning

Common Pitfalls

• Point-to-point explosion
• No central log correlation
• Unbounded retries causing storms
• Ignoring schema evolution
• Security tokens hardcoded

Integration KPIs

• Data freshness latency
• Failed sync events %
• Duplicate entity rate
• Mean reconciliation time
• Alert MTTA
• Throughput vs error rate

Case Snapshot

Retail SME synced inventory nightly (CSV) causing oversell incidents.

Implemented event-driven delta sync + monitoring dashboard.

• Stock accuracy 92% → 99.3%
• Oversell incidents near zero
• Reconciliation time -70%
• Latency minutes → seconds

Integration Stack Components

• Event bus or queue
• Schema registry
• Centralised logging/trace
• Secret management
• Replay & dead-letter tooling
• Versioned contract docs

Pre-Integration Checklist

• Define data owners
• Catalog source & target systems
• Classify PII & compliance needs
• Decide consistency vs latency trade-offs
• Draft retry/backoff policy
• Plan versioning strategy

FAQ