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  • Dr I Morgan

IoT Data Visualisation

Updated: May 27

The customer required a setup that could be deployed on-premise or a third party server. They required a service with a means of injesting API data from their own service and visualising the data in a dashboard, custom intervals and various other configurations that would be different for each deployment. This data consists of analytic results, calculated periodically, typically from sensor measurements in an industrial environment.

Our stack consisted of docker-compose, git branches for segregating individual customer setups, Grafana for the visualisation and a lightweight job server called DigDag for running primarily Python scripts at set intervals. Seamless provisioning was key, so we also setup a continuous integration process hosted by CircleCI to build code changes and deploy to the appropriate customer server, whereupon the whole setup was redeployed and provisioned. Working with the Grafana API allowed us to provide custom provisioning of organisations, users, datasources and dashboards dependent upon configuration.




We also used TimescaleDB - an extension on top of Postgresql - which gave us the advantage of efficient time series storage and materialised views while making use of all the benefits that Postgresql brings. This included the range feature that allowed us to easily incorporate red line limits for alerting purposes on a machine by machine basis.

Finally we set up load balancing to redirect queries to the appropriate endpoint and allowed simple management of the certificates. Initial deployments have already taken place, and we're working further with the customer to enhance their offering to their own clients.




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