How do you make decades of time-series data usable at scale? CERN operates 800+ WinCC OA-based SCADA systems that generate hundreds of gigabytes of time-series data every day from Large Hadron Collider experiments to critical infrastructure.
In this webinar, CERN engineers share how they modernized their legacy archiving stack with the NextGen Archiver (NGA) and TimescaleDB (PostgreSQL) to improve performance, reduce storage costs, and simplify long-term operations.
In this 60-minute session, you’ll learn how CERN:
- Built a pluggable archiving architecture (NGA) to modernize legacy stack
- Reduced storage by up to 95% and sped up historical analytics by up to 40% using TimescaleDB's hybrid rowstore/columnstore architecture
- Leveraged continuous aggregates (auto-updating materialized views) to power fast dashboards across massive historical data sets
- Supported complex, time-saving metadata by combining relational data and time-series signals in one system
- Designed for maintainability and flexibility, reducing lock-in and technical debt
If you work on industrial monitoring, IoT, observability, or any system that needs fast analytics over large volumes of time-series data, this session will give you practical patterns you can apply to your own architecture.
Presenters
- Rafal Kulaga, Staff Software Engineer, CERN
- Martin Zemko, Software Engineer, CERN
- Brandon Purcell, Director of Product Management, Tiger Data
Can’t make it live? Register anyway and we’ll send you the recording.
