API Integration Guide: Connecting Custom Software to Existing Systems

Integration Patterns: REST, GraphQL, Webhooks & Message Queues

Choosing the right integration pattern depends on the specific data flow and timing requirements between your systems, and defaulting to whichever pattern you're most familiar with, rather than what the actual use case calls for, is a common and costly mistake. Getting this choice right upfront avoids the costly rework of realizing partway through a project that the wrong pattern was chosen for the actual use case. Getting this right avoids costly rework mid-project.

REST

REST APIs suit straightforward request-response interactions where a client asks for specific data and gets a defined response back, remaining the most common and broadly supported integration pattern across virtually every modern platform and vendor available.

GraphQL

GraphQL suits situations where a client needs to request precisely the fields it needs from a complex, nested data structure, reducing over-fetching compared to REST at the cost of added server-side complexity and setup.

Webhooks

Webhooks suit event-driven integrations where one system needs to notify another the moment something happens, rather than the receiving system repeatedly polling for updates that may not have occurred yet at all.

Message Queues

Message queues suit high-volume, asynchronous processing where reliability and eventual delivery matter more than immediate response time, decoupling systems so a temporary outage in one doesn't immediately cascade into failures across the other.

Authentication, Rate Limiting & Error Handling

Authentication, rate limiting, and error handling are where integration reliability actually gets built, and skipping careful design here is the most common reason a working integration in testing breaks down under real production traffic and conditions. Skipping careful design in any of these three areas is the most common reason integrations that work fine in a demo fail once real users and real volume show up. Skipping this design work is the top cause of production failures.

Authentication Patterns

API key and OAuth authentication patterns each carry different security and complexity tradeoffs, and choosing the wrong one for your specific integration can create either an unnecessary security gap or considerable unneeded implementation overhead.

Rate Limiting

Rate limiting on the systems you integrate with needs to be respected proactively, with retry logic and backoff strategies built in, rather than discovering the limit only after your integration starts failing under real production load.

Error Handling & Retries

Error handling and retry logic need to distinguish between temporary failures worth retrying and permanent failures that need a different response entirely, since treating every failure identically leads to either wasted retries or lost data.

Integration Testing

Integration testing deserves its own dedicated attention beyond standard application testing, since integrations depend on external systems whose behavior you don't fully control and can change without notice, unlike code entirely within your own application. Building this testing discipline in from the start catches problems while they're still cheap to fix, rather than after a partner's API has already changed silently in production. This discipline catches problems while they're still cheap to fix.

Sandbox & Staging Testing

Testing against a sandbox or staging environment for the external system, when available, catches integration issues before they reach production, though many integrations require additional monitoring specifically because sandbox behavior doesn't always perfectly match production behavior.

Contract Testing

Contract testing, verifying that both sides of an integration agree on the exact data format and behavior expected, catches breaking changes on either end before they cause a production failure that's often harder to diagnose after the fact.

Integrating With Systems That Have No Real API

Some systems genuinely have no usable API, and integrating with these requires riskier techniques, each carrying real tradeoffs worth understanding honestly before committing to one, since these approaches are considerably more fragile than a proper API integration. We recommend evaluating all three of these approaches carefully before committing, since the wrong choice here creates ongoing maintenance burden that compounds over the life of the integration. Evaluate all three carefully given the ongoing maintenance burden each carries.

Screen Scraping

Screen scraping, extracting data by parsing a system's user interface directly, breaks whenever that interface changes, making it the most fragile option and one we recommend only when genuinely no better alternative actually exists.

File-Drop Integrations

File-drop integrations, exchanging data through shared files on a schedule, work reliably for batch processes but introduce latency and lack the real-time responsiveness a webhook or API-based integration can provide instead.

Database-Level Integration

Database-level integration, connecting directly to another system's underlying database, offers more reliability than screen scraping but risks breaking when the vendor changes their schema, and this approach connects directly to our broader API and integration work.

Frequently Asked Questions