Architecture Best Practices
Why architecture matters
Before diving into specifics, it's useful to set expectations around what a “platform integration architecture” enables (and what it tends to prevent) when embedding or connecting via Prismatic.
- The engineering cost of building, maintaining, securing, and scaling integrations is high. An embedded integration platform like Prismatic abstracts or handles much of that “plumbing” (auth, retries, error handling, logging, versioning, deployment, monitoring) so your engineers can focus on the business logic.
- But that benefit only materializes if your core platform (your API, events, data model, security posture) is designed with integration in mind. Good alignment up front reduces friction, accelerates time to value, and ensures robustness.
- Prismatic supports multiple integration styles (event‑driven, scheduled, synchronous, hybrid) and embraces a low-code + code-native model.
- From the customer perspective, your integration “surface” (configuration UI, logs, retry UI, error messaging, SLAs) becomes part of your product experience. That means quality, consistency, and resilience are key. Your platform must be ready to support that.
Use this guide to give your team a checklist / roadmap: what to think about, what engineering capabilities to build, and how to organize your platform to make integrations a first-class, scalable feature.
Key domains to get right
Below is a suggested blueprint. Use this as a guide to adapt based on your product maturity, team size, and integration complexity.
| Domain | Objectives / Key Capabilities | Recommended Practices & Approaches |
|---|---|---|
| API & interface layer | Provide a stable, well-versioned, well-documented API surface that integrations can reliably call, and that integration flows can depend on | - Design your API with integrations in mind (don’t build just “internal APIs”). - Use versioning (v1, v2, etc.) and clear deprecation paths. - Support bulk vs incremental endpoints (e.g. fetch changes since timestamp). - Implement webhooks or change notifications where possible. - Provide filtering, pagination, rate limits, batch endpoints, delta endpoints, and query parameters. - Support filtering by customer / tenant / scope. - Provide schema definitions (JSON Schema, OpenAPI) and sample payloads. - Consider a thin adapter or SDK layer over your API. |
| Change events / webhooks / event bus | Support event-driven / nearly real-time integration flows | - Emit domain events for Create / Update / Delete actions. - Provide webhook registration APIs (create/list/delete). - Bridge internal event buses (Kafka, Pub/Sub) to webhooks if needed. - Include standard webhook envelopes (metadata, versioning, timestamps, correlation IDs). - Support retries, idempotency, and dead-letter handling. - Allow deploy-time provisioning of webhooks when integrations activate. |
| Data model & canonical representation | Facilitate mapping, transformation, and reconciliation across systems | - Use consistent field naming and canonical formats (e.g. UTC ISO 8601 timestamps). - Flatten or expose integration-friendly view models. - Maintain last-updated timestamps or change tokens. - Provide stable cross-entity relationships and IDs. - Consider denormalized sync tables or exposed views. - Build translation or mapping layers via integration logic or Prismatic flows. |
| Authentication / Authorization / Multi-tenancy | Secure, scoped access for integrations and customer isolation | - Use OAuth or token-based authentication. - Support per-customer API keys or scoped credentials. - Enforce least-privilege permissions. - Support credential rotation and revocation. - Isolate integration credentials and data per customer. - Apply quotas and rate limits per tenant or integration. |
| Error handling, retries, idempotency | Make integrations robust, self-healing, and observable | - Use idempotent operations and deduplication keys. - Implement retries with exponential backoff and circuit breakers. - Distinguish transient vs permanent errors (5xx vs 4xx). - Surface structured error metadata without leaking sensitive data. - Use dead-letter queues or error flows. - Allow replay or resume of failed runs. - Track progress for long-running flows asynchronously. |
| Deployment & activation / configuration | Enable safe, manageable integration setup and lifecycle | - Define configuration models (credentials, mappings, schedules). - Support deploy-time provisioning flows. - Version integrations and support upgrades. - Provide schema migration strategies. - Enforce role-based access for integration management. - Expose APIs / CLI / SDKs for automation. - Provide sandbox or test environments. |
| Monitoring, logging, observability, alerts | Provide visibility into integration health | - Log each step with structured logs. - Mask sensitive data in logs. - Correlate logs with flow or correlation IDs. - Emit metrics (latency, errors, throughput). - Configure alerts on failures and latency. - Expose self-serve run history and logs to customers. - Define retention policies and consider distributed tracing. |
| Scalability, concurrency, performance | Prevent integrations from becoming bottlenecks | - Design stateless, horizontally scalable APIs and webhooks. - Use async ingestion and queue-based processing. - Apply batching, backpressure, and rate limits. - Cache or precompute where possible. - Control concurrency per integration or tenant. - Optimize expensive transformations. - Plan for sharding and horizontal scale as usage grows. |
| Security, data protection, compliance | Maintain confidentiality, integrity, and availability | - Use TLS 1.2+ everywhere. - Encrypt sensitive data at rest. - Apply least-privilege access models. - Validate and sanitize all inputs. - Log auth failures and suspicious activity. - Use network isolation where possible. - Audit dependencies regularly. - Plan for compliance (GDPR, HIPAA). |
| Governance, versioning, deprecation | Maintain long-term stability as integrations evolve | - Version APIs and integration flows. - Provide backward compatibility or migrations. - Surface deprecation warnings. - Maintain changelogs. - Isolate breaking changes. - Use feature flags for gradual rollout. - Track usage and retire unused integrations carefully. |
| Testing & quality assurance | Ensure integration correctness and reliability | - Provide test harnesses and sandboxes. - Run end-to-end automated tests. - Mock third-party APIs in CI. - Use contract and schema validation. - Maintain regression datasets. - Provide rollback paths. - Test edge cases thoroughly. |
| Developer & operability ergonomics | Make integrations easy to operate and support | - Surface Prismatic logs and dashboards in admin UIs. - Provide onboarding and training materials. - Offer clear error messages and troubleshooting guides. - Build configuration wizards for customers. - Expose APIs / CLI / SDKs for management. - Track metadata for analytics and SLAs. |
| Analytics, billing, usage & metering | Support monetization and usage insights | - Track invocation counts, data volume, errors, and latency. - Support usage tiers or cost attribution. - Provide dashboards and reports. - Use data to guide product decisions. - Expose usage data to enterprise customers where appropriate. |
| Fallbacks, limits, outage resilience | Prevent cascading failures and degrade gracefully | - Use circuit breakers and bulkheads. - Apply timeouts and fallback logic. - Allow customers to disable non-critical integrations. - Queue work during outages instead of dropping it. - Provide status pages and outage notifications. |
You likely don’t need to build all these at once. But as a maturity roadmap, you can phase in: first your core APIs & webhooks, then monitoring, then error handling and retries, then governance and analytics, etc.
Suggested Phased Roadmap & Priorities
When planning your work, here’s a suggested set of phases:
-
Foundational APIs and events
Build your core API surfaces and webhook / event mechanisms to support real-time / incremental data flows. Define canonical models and versioning.
-
First integrations – simple patterns
Pick 1–2 high-value integrations (e.g. export to Salesforce, import from CRM) and build them (e.g. event-driven import/export). Use these as “ground truth” to validate your API, mapping, authentication approach, error handling, and dashboards.
-
Instrument monitoring & logging
As soon as flows are running, build out logs, metrics, dashboards and alerts on error rates, latency, throughput.
-
Self-serve configuration & deployment
Build or embed your integration configuration UI / marketplace, enable deploy-time flows, support enabling/disabling integration instances via UI or API.
-
Maturity extensions
Add reconciliation, drift detection, replay / reprocessing abilities, governance/versioning, usage metering, billing, error replay UI.
-
Performance & scale
Test with many integration instances, high throughput, simulated error spikes, and scale out queueing, batching, partitioning, or horizontal scaling.
-
Security audits, hardening, compliance
Review the integration surface (APIs, webhooks, data flows) for security vulnerabilities, enforce encryption, credential rotation, least privilege, etc.
Over time, aim for your integration experience (for users/customers) to feel first-class, reliable, and self-service.
Tips & Tricks
- Think in terms of flows and pipelines, not point-to-point scripts. Integrations are rarely just “call API A, then B.” Rather, you’ll compose modular transform, filter, branch logic, error handling, retries; thinking of the integration as a pipeline or flow makes it more maintainable.
- Isolate cross-cutting concerns. Error handling, retry logic, authentication, logging are common plumbing that should be abstracted (rather than duplicated in each connector).
- Favor idempotency and deduplication. Integration traffic is inherently at risk of retries, replays, or duplicate deliveries; designing idempotent from the start prevents many tough issues.
- Design to detect and handle drift / divergence. Over time, clients' data models or third-party APIs may change; have reconciliation or drift detection to catch discrepancies.
- Plan for “broken / malformed input.” External systems may send missing fields, unknown types, or unexpected values; your flows should guard and fail gracefully with clear error messages.
- Support incremental / delta-sync, not full reloads if possible. Polling or bulk sync is expensive; incremental syncs reduce overhead, improve speed, and are kinder to both sides.
- Expose visibility to the customer. If your customers see logs, last run times, error counts, and have some self‑diagnostic tools, they feel more empowered and reduce support load.
- Be mindful of API rate limits and quotas (on both sides). Use batching, backoff, and throttling to avoid hitting partner rate limits.
- Maintain backward compatibility where possible. If you change a webhook payload or add fields, avoid breaking existing integrations. Provide version negotiation or fallback behavior.
- Embed integration in your product UX / onboarding. The smoother the setup experience (credentials, mapping, activation), the fewer handholds needed.
- Track integration usage & ROI. Use metrics to understand which connectors are most used, where failures concentrate, and how integration usage correlates with retention or upsell.
- Don’t over-engineer too early. It’s okay to start with simpler architectures (e.g. polling) and evolve toward event-driven as you grow.
Go‑Live Checklist
Before you flip the switch on your integration, run through this checklist to reduce surprises, increase stability, and ensure your users have a smooth experience.
Design & Architecture Checks
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Integration Pattern | Confirm you’ve picked an appropriate pattern: event‑driven, scheduled, synchronous, or hybrid | Using an inappropriate pattern can lead to performance, latency, or reliability issues | See common integration patterns |
| Multi‑flow vs Single Flow | If your integration supports multiple webhook events or different logical flows, consider splitting into multiple flows rather than overly complex branching | Easier to test, maintain, and monitor per flow | See multi‑flow to handle distinct payloads cleanly |
| Deploy‑time flow / webhook registration | If your integration requires registering webhooks in a third‑party system, include a deploy-time flow that runs when the instance is enabled | Ensures the correct webhook endpoints are registered and reduces manual setup errors | |
| Configurable / “config-driven” design | Make sure your integration is parameterized via config variables rather than hardcoding customer-specific values | This allows you to deploy the same integration logic to multiple customers | See Integrations |
Connection & Authentication Validation
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Connection Types | Decide whether your connections are integration-specific or integration-agnostic (global, org-activated customer, customer-activated) | Using integration-agnostic connections improves reuse, simplifies configuration, and centralizes credential management | See Connections Overview Prismatic |
| OAuth / Token Refresh Logic | If using OAuth, ensure you handle refresh tokens, error states, credential expiry gracefully | Avoid broken flows due to expired tokens | |
| Endpoint Security / API Keys | For webhook or trigger endpoints, choose appropriate security settings: no API key, customer-secured, or org-secured | Prevents unauthorized invocations | See Endpoint Configuration docs |
| Test Connection / Validation | Provide a “Test connection” action (when possible) and validate that connection inputs are correct before running full flows | Catch misconfigurations early |
Config Wizard & Deployment Setup
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Wizard Inputs & Flow | Map all required inputs properly into the configuration wizard; ensure helpers, hints, conditional logic, and validations are in place | Avoid incorrect user input leading to errors at runtime | See Config Wizard Overview |
| Display of Endpoint/API Key | If your flow endpoints or API keys should be shown to the customer, ensure the config wizard includes trigger/endpoint detail fields | Helps the customer configure external systems (e.g. third-party webhooks) correctly | |
| Versioning & Publishing | Make sure you’ve published a stable version of your integration and marked it Available; disable or hide any unstable drafts | Ensures customers don’t accidentally use untested or broken logic | |
| Templates for Reuse | If you expect variants of this integration, provide a template that your team or customers can use | Reduces redundant work and ensures consistency | See Integration templates |
Testing & Validation
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Unit & Integration Tests (for code-native) | If using TypeScript / code-native, build automated test cases (especially for edge cases, errors, timeouts) | Helps guard against regressions and unexpected failures | Building in TypeScript and testing locally |
| Sample Payloads & Mocking | Use representative sample data and boundary cases in tests (large records, missing fields, unusual formats) | To catch errors before hitting real customers | |
| Error Handling & Retries | Ensure that each step in your flow handles errors (e.g. network timeouts, HTTP 5xx responses) and includes retry/backoff logic or fallback paths | Prevents full flow failures when transient issues occur | |
| Timeouts / Resource Limits | Simulate long-running or large flows to ensure that they don’t exceed time or memory limits | Avoid execution failures under load | |
| End-to-End Dry Runs | Deploy to a sandbox or test customer, run the integration end-to-end (trigger → transformation → destination) under real-ish data | Validates real-world interoperability, catches configuration or schema mismatches |
Monitoring, Logging & Alerting
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Log Levels & Granularity | Ensure that the integration logs errors, warnings, and key milestones/events at appropriate levels | Useful for debugging and diagnosing issues in production | |
| Streaming or Exporting Logs | If desired, configure streaming of logs to external systems (e.g. DataDog, New Relic) | Centralizes observability with your existing stack | Log Streaming |
| Alert Monitors & Triggers | Define alert triggers (error conditions, execution time thresholds, failed executions) and hook them up to alert groups (Slack, PagerDuty, email) | You want to be notified proactively of issues before customers notice | See Alerting |
| Alert Group Configuration | Assign correct users or teams to alert groups and validate webhooks / notification targets | Avoid alert fatigue or incorrectly routed notifications | |
| Metrics / Performance Monitoring | Track execution durations, throughput, errors over time | Detect degradation or trends before they become outages | |
| Incident Escalation Plan | Ensure your team knows the escalation route (first responder, backup, communication, resolution) | A clear plan ensures timely handling of outages |
Deployment & Instance Activation
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Sandbox / Test Instance | First deploy an instance to a test or staging customer using real (or realistic) config values | Catch deployment environment issues before production roll-out | |
| Configuration Validation | Upon deployment, check that all config inputs, connections, and endpoints are set correctly | Misconfigured credentials or URLs are a common failure cause | |
| Webhook Registration / Callbacks | For webhook-based flows, validate that the correct URLs are registered in the third‑party app and payloads are arriving | Prevents missing events or incorrectly routed data | |
| Activation & Warm-up | Execute a few manual / test runs to “warm up” caches, connections, and validate flow stability under small load | Reduces cold-start surprises | |
| Version Compatibility | If clients may have different versions, ensure backward compatibility or migration path | Allows safe rollout and version upgrades | |
| Rollback Plan / Safeguards | Ensure you have a quick way to disable or roll back the instance or integration version if something breaks | Minimize downtime or damage in case of errors |
Customer Experience & Edge Cases
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Clear Error Messages | If something goes wrong (invalid credentials, rate limit, missing fields), ensure errors surfaced to customers are user-friendly with guidance | Helps customers self-diagnose issues instead of escalating support | |
| Time Zone / Locale Handling | For date/time fields or batch scheduling, ensure your logic handles different locales/time zones correctly | Prevents off-by-one or date-shift bugs | |
| Schema Changes & Version Tolerance | Be resilient to upstream API schema changes (e.g. optional fields added/removed) | Reduces breakage when third-party systems evolve | |
| Partial Success Handling | In cases where part of the flow succeeds but others fail, define how to retry, alert, or continue | Avoids data loss or inconsistency | |
| Rate Limits & Throttling | Respect third-party API limits; include retry logic, backoff, or rate throttling if needed | Prevents rejection or blacklisting from API providers | |
| Idempotency / Duplicate Detection | If the same event or webhook might trigger multiple times, design idempotent logic or duplicate detection | Prevents double writes or inconsistent state |
Ongoing Maintenance & Governance
| Area | What to Verify | Why | References & Tips |
|---|---|---|---|
| Component & Integration Versioning | Track and manage versions of components used within integrations; upgrade carefully | Avoid unexpected breaking changes downstream | |
| Change Control Process | For updates, test in staging, validate, deploy gradually (canary), observe then roll out broadly | Minimizes risk when updating live integrations | |
| Monitoring Trends / Health Dashboards | Regularly review error rates, latencies, integration health metrics | Enables proactive improvements and capacity planning | |
| Customer Support Tooling | Provide logging access or capture context for customer‑facing support; enable debug mode if needed | Eases troubleshooting without requiring deep internal access | |
| Backup / Resilience | Consider backup strategies or fallback paths if a third‑party API is unavailable | Improves reliability during outages | |
| Regular Audits & Tests | Periodically re-run integration health checks, load tests, schema compatibility tests | Ensures evolving dependencies haven’t broken things |