Backend Infrastructure
Event-driven backends and data pipelines built for production scale.
Event-driven backends, integration meshes, queue-based architectures and data pipelines built for production. We have rebuilt failing webhook integrations into fault-tolerant processing layers, delivered zero-downtime migrations, and built real-time data consolidation systems for multi-entity operations.
- →Event-driven and queue-based architecture design
- →Real-time data pipelines and consolidation layers
- →Zero-downtime migration from legacy backends
- →Observability, alerting and incident runbooks
- →Production hardening and SLA design
Backend infrastructure built for production scale
The backend is the part of your system that nobody sees and everybody depends on. When it is built well, it is invisible. When it is built badly, it becomes the reason your product cannot scale, your integrations break under load, and your team spends engineering time on incidents instead of features.
Zeacle builds backends that do not make the news. Event-driven architectures that handle burst traffic without degrading. API layers that are documented, versioned, and maintained. Data pipelines that deliver clean, timely data to the systems that depend on them. Infrastructure that performs under real conditions, not just in a development environment.
What we build
Event driven architectures where components communicate through events rather than direct calls. Systems where a new event source means adding a subscriber rather than modifying existing code. When a downstream service is temporarily unavailable, events queue rather than causing cascading failures.
RESTful and GraphQL APIs that are consistent, documented, versioned, and designed to serve both internal consumers and external integrations. Database architecture and optimisation covering schema design, query optimisation, indexing strategy, and connection pool management.
Queue-based processing and data pipelines
Asynchronous processing via message queues is the correct architecture for any operation that does not need to complete within the HTTP request cycle. Document processing, notification dispatch, data synchronisation, and report generation are all better served by queue-based workers than synchronous API calls.
Reliable data pipelines extract from source systems, transform to the required schema, and load into destination systems on schedule or in real time. We have built pipelines connecting advertising platforms, CRM systems, transactional databases, and third-party data sources into unified data warehouses, with data quality validation at every stage and alerting when source data deviates from expected patterns.
Our infrastructure standards
Every system is built for the failure modes it will actually encounter in production, not just the happy path. Observability is built in from day one: logs, metrics, and traces are available before the first production deployment. Infrastructure as code means your environment configuration is version controlled and reproducible.
API documentation is generated from code and kept current automatically. Performance testing is conducted against realistic load profiles before production deployment. Security review is part of the delivery process, not an optional add-on. Authentication, authorisation, secrets management, audit logging, and data encryption are first-class architectural concerns.
We build on AWS, Google Cloud, and Azure, selecting the platform that best fits the client's existing infrastructure, team expertise, and specific requirements. For clients without an existing cloud preference, we provide a clear recommendation based on the system's requirements and the long-term operational cost implications.
Yes. A significant portion of our infrastructure work involves inheriting systems that have accumulated technical debt, performance problems, or architectural limitations. We conduct a structured assessment of the existing system before proposing any changes, prioritising the improvements with the highest impact on reliability and performance relative to the cost and risk of making them.
We start with measurement. Performance problems cannot be solved without data. We instrument the system to identify where time is actually being spent, distinguish between problems that require architectural changes and those that can be resolved through query optimisation or caching, and prioritise fixes based on the impact on the user experience and the system's ability to scale.
Yes, and we treat zero-downtime as the default. Dual-write, shadow-read, gradual cutover, with the ability to roll back at every step.