Lessons from Building a Data Platform for Smart Cities
We've built a data platform for smart cities. This has been deployed in over a dozen cities, and we've learned a lot in the process, about:
- why data ingestion from IoT networks can range from trivial to very painful, and how to cope;
- how to architect the system to easily handle many different 'data domains';
- getting the architecture to work well including making additions of new data sources as simple as we can;
- approaches to analytics and visualisations that have been useful;
- why end-user analytics and visualisations are critical;
- how user permissions for smart city applications can be different to more 'normal' applications.
- and lots more
In the talk, I'll walk through the lessons learned and show off examples of the system in action.
The goal is to use the platform as an exemplar of the design principles, this is not a sales pitch for the tool itself.
Outline/Structure of the Invited Talk
- Introduction/what problem are we solving
- The data platform architecture & data ingestion
- Architecting for multiple (and varying) data types
- Analytics, visualisation and dashboards
- End-user control
- Various examples/demos: traffic, parking, open data integration, environmental sensing etc.
- Summary: lessons learned.
Attendees should have a good understanding of the challenges in building and using scalable data platforms in an urban setting
Anyone interested in highly scalable IoT systems, but particularly with a 'cities' or other urban focus
Prerequisites for Attendees