Challenge
EELIS was built as a monolithic, workstation-based application – a legacy architecture that limited scalability, was awkward to use in the field and did not fit modern digital workflows.
Solution
- Migration to microservices. A complete rebuild of the platform on a microservices architecture for modularity and scalability.
- PostgreSQL geodatabases. Supported by interactive map applications and automated CI/CD deployment pipelines.
- Mobile field tools. Rebuilt for seamless data exchange with external registers and for mobile use in the field.
- AI-ready data foundation. Redesigned data models enable pattern detection, decision support and spatial analytics.
- Open data publishing. Opens up the possibility of training machine learning models for environmental research and forecasting.
Results
- An intelligent, scalable and data-rich platform for managing nature across Estonia.
- Specialists have noticeably better tools for fieldwork and day-to-day work.
- Manual data flows have largely been replaced by automated processes.
- EELIS is now a strategic decision-support system, not just a static register.
Key takeawayEnvironmental and scientific registers gain especially much from microservices – the real win is the ability to use AI and automation, which was impossible with the old architecture.