Low-code platforms offer tremendous value for rapidly integrating diverse systems. They enable faster development cycles and reduce the need for complex custom code.

However, for mission-critical system integrations, it’s important that organisations follow a well-architected approach to ensure scalability, reliability, and security.

This post outlines best practices for achieving a balanced mix of low-code and conventional engineering to integrate systems across an enterprise. It also addresses nuances that can affect performance, resilience, and the long-term success of integrations.

1. Adopt an API-first approach for Integrations

APIs form the backbone of system integrations, and as such, adopting an API-first approach ensures that your integrations are scalable, maintainable, and flexible. Low-code platforms certainly simplify API connectivity, but without careful design, you may end up with tightly coupled systems that are difficult to evolve.

Best practices

  • Design all system integrations around well-documented and version-controlled APIs. Choose RESTful APIs for most scenarios. For more complex querying or legacy system compatibility, consider GraphQL or SOAP.

  • Use an API gateway (e.g., Azure API Management, AWS API Gateway) to centralize API management. API gateways provide version control, rate limiting, caching, and monitoring. These features are essential for maintaining the health of your integrations and enabling API governance across teams.

Additionally, an API-first strategy should incorporate API versioning and backward compatibility. This ensures that changes to one API don’t break dependencies for other systems. By prioritizing APIs as the central integration point, you make it easier to scale and evolve your architecture as new services are added.

2. Leverage pre-built Connectors—but with care

Most low-code platforms offer pre-built connectors to common enterprise systems like Salesforce, SAP, and Microsoft Dynamics. These connectors save development time and simplify integration workflows, but they can sometimes introduce performance bottlenecks or lack flexibility.

Best practices

  • Use pre-built connectors for standard integrations, but always validate them against your performance, security, and customization needs. If you’re integrating with mission-critical systems, stress-test connectors under load to evaluate their capacity for handling large data volumes.

  • Investigate the extensibility of connectors before committing to their use. Can they be customized if the out-of-the-box functionality falls short? Does using them mean vendor- or developer- lock-in? If not, consider custom API integrations to gain more control over data architecture and performance tuning.

In some cases, connectors may abstract too much logic, making it difficult to optimise or troubleshoot issues. Balancing convenience with customizability, ensures that performance and scalability are not compromised in the long run.

3. Architect for loose coupling and scalability

System integrations should be as loosely coupled as possible to ensure flexibility and scalability. While low-code platforms facilitate quick integrations, it’s important to avoid direct dependencies between systems that could become single points of failure. Where and when possible, use intermediary services like message queues, service buses, or event streams to decouple components.

Best practices

  • Architect your integrations using message brokers (e.g., Azure Service Bus, Amazon Simple Queue Service (SQS), Apache Kafka) or event-driven architectures. This decouples systems and enables asynchronous communication, allowing each service to scale independently without affecting others.

  • For event-driven systems, adopt patterns like publish/subscribe to propagate changes across multiple systems while keeping them independent. This improves fault tolerance, ensuring that failures in one system don’t propagate to others.

  • For large-scale systems, consider using partitioning in message brokers (e.g., Kafka partitions) and sharding techniques to handle massive throughput. This ensures that your system can grow without introducing bottlenecks or tightly coupled integrations.

4. Implement centralized monitoring and error handling

Low-code platforms often abstract key details of performance and failure modes, so implementing centralized monitoring and error handling ensures visibility into the health of your integrations.

Best practices

  • Set up centralized monitoring to track key performance indicators (KPIs), error rates, and latency across all integrated systems. Consider using tools such as Azure Monitor, AWS CloudWatch, or the ELK Stack for deeper observability.

  • Implement error-handling patterns like circuit breakers and retry logic. Circuit breakers prevent cascading failures by temporarily halting interactions with a failing service. Retry logic ensures your system attempts recovery without immediate failure.

  • For advanced architectures, integrate distributed tracing tools (e.g., OpenTelemetry) to track requests across multiple services. This is particularly important for identifying performance bottlenecks in distributed systems or microservices architectures.

5. Ensure secure data transfer across systems

Security should be your top-most priority in any system integration, especially when sensitive or regulated data is involved. While low-code platforms often include built-in security features, you must ensure that data transmission and storage between- and on- systems is secure.

Best practices

  • Use encryption protocols like HTTPS, TLS, and AES for all data transfers. Enforce authentication standards such as OAuth2, JWT, or SAML to ensure secure access between services. Apply Role-Based Access Control (RBAC) to restrict access to sensitive APIs and data.

  • If integrating across regions with different regulatory environments, ensure that data residency and privacy controls comply with local regulations (e.g., GDPR, CCPA). Regularly audit your security policies to ensure compliance.

  • It’s also important to implement data lineage tracking and audit trails. These are essential for enterprises in regulated industries, ensuring you can trace the flow of data across systems and demonstrate compliance with regulations.

6. Optimize for performance in system integrations

Performance optimization is easy to overlook in low-code system integrations, but it’s crucial for scalability. High data volumes and frequent API requests can quickly lead to performance bottlenecks if not properly managed.

Best practices

  • Where possible, implement asynchronous processing and bulk data transfers to reduce API load and avoid bottlenecks. Use caching at strategic points to reduce the number of repeated API calls (e.g., caching frequently requested data).

  • Monitor API rate limits and use queue depth monitoring to track the health of your message queues and service buses. Leverage load testing tools (e.g., Apache JMeter, Locust) to simulate high-throughput scenarios and identify performance issues early.

Optimizing performance from the get-go helps avoid scalability issues as the volume of data and the number of integrated systems grow.

7. Standardize and reuse integration patterns

It’s important to standardize integration patterns for consistency and maintainability across your systems. Whether you’re connecting legacy systems to cloud services or integrating third-party SaaS platforms, adopting standardized patterns simplifies your architecture.

Best practices

  • Use well-known patterns like request/response, publish/subscribe, and event-driven integration. Build a library of reusable patterns and components that can be shared across teams to accelerate development and ensure architectural consistency.</p>

  • Consider using iPaaS (Integration Platform as a Service) solutions like Workato. These platforms abstract integration logic into a centralized layer, offering reusable connectors, workflows (or Recipes in Workato parlance), and governance tools to ensure visibility across integrations.

By standardizing your integration patterns, you ensure that each new integration aligns with enterprise standards. This reduces complexity and shortens development timelines.

8. Use middleware for complex workflow orchestration

In scenarios where multiple systems need to coordinate complex workflows, middleware helps orchestrate data flows and ensures that business logic is followed.

Best practices

  • Use middleware for orchestrating complex workflows. Middleware centralizes data transformation and business logic, freeing your low-code environment from overly complex operations.

  • Understand when to use orchestration vs. choreography. Orchestration relies on a central coordinator to manage tasks, while choreography allows services to interact autonomously. Orchestration is better for business process management (BPM), while choreography is ideal for microservices architectures.

Middleware prevents integrations from overwhelming your low-code platform’s capabilities, especially in environments with complex business logic.

9. Implement reliable CI/CD pipelines

Low-code development should align with DevOps practices to ensure smooth, reliable deployments and consistent quality. Integrating low-code systems into your existing CI/CD pipelines automates testing and deployment.

Best practices

  • Include contract testing and end-to-end integration tests to validate that APIs and connectors work as expected. Use mock services to simulate external systems during testing, reducing the risk of integration failures in production.

A robust CI/CD pipeline for low-code integrations helps maintain velocity while ensuring that each component meets rigorous quality standards.

10. Design vendor-agnostic integrations

Vendor lock-in is a potential risk when using a specific low-code platform or committing to any particular Cloud vendor like Amazon Web Services (AWS), Google Cloud (GCP), or Microsoft Azure. To maintain flexibility and control, design your system integrations with portability in mind. This minimizes dependencies on platform-specific features, and keeps viable the option of either switching vendors or adopting a multi-cloud strategy down the line.

Best practices

  • Where possible, rely on open standards like OpenAPI, OAuth, and JSON for integrations. This ensures your system architecture remains vendor-neutral and can be migrated to other platforms if necessary.

  • Use external API management tools (e.g., Kong, Tyk) to create an abstraction layer over your low-code integrations. This gives you more control over APIs and reduces reliance on the underlying platform.

Designing for vendor independence ensures that your architecture can adapt to future changes, such as adopting new platforms or switching vendors without significant rework.

Conclusion

Low-code platforms are great for building system integrations quickly, but their success depends on a well-architected approach. By following these best practices—adopting API-first design, leveraging pre-built connectors where appropriate, optimizing performance, securing data, and using middleware and CI/CD pipelines—you can ensure your integrations are scalable, secure, and can grow with the needs of your organisation.

Low-code should be viewed as a strategic tool for accelerating digital transformation efforts, but with the same level of architectural discipline as any other enterprise solution.

With the right approach, low-code can play a central role in modernizing your enterprise architecture while maintaining flexibility and control.

If you’d like to explore how low-code technology can support your business process management and automation needs, let's connect and discuss how we can help you 🎯