IoT platform with Grafana

Client

A manufacturing company wanted to have visibility into their production lines and machines operations. They needed dashboards to track the live status of each machine and the overall production process. Additionally, they required comprehensive historical data analytics to identify trends related to each machine and optimize performance.

Challenge

Besides gathering data from the MQTT broker, there was a need to calculate various specialized indicators that were not directly available from PLCs and production lines. To achieve this, multiple scripts had to be created to extract data from the raw database, computed essential metrics on the fly, and stored the results in a separate database for advanced visualization. This solution not only enabled real-time data monitoring but also functioned similarly to a Power BI solution for deeper analysis.

Concept

1. MQTT broker to capture and aggregate machine data and production line metrics.
2. Telegraf, Kapacitor, InfluxDB and Grafana to power live dashboards and historical trend analyses.
3. A server with Red Hat Enterprise Linux and Podman to run the applications as containers. This container-based approach simplifies deployment and maintenance while enhancing security and portability. By aligning with modern cloud-native practices, the solution is readily adaptable for Kubernetes or AWS services, providing flexibility for future expansions and integrations.

Work done

1. Configured Red Hat Enterprise Linux server with Podman as the container runtime.
2. Prepared Podman Compose for the entire application stack, enabling the client to modify and update services with minimal effort while retaining best practices for container-based deployments.
3. Configured Kapacitor tasks to continuously process and calculate custom process metrics.
4. Set up InfluxDB to store IoT data received from the MQTT broker, delivering a high-performance, time-series database for both real-time and historical analytics.
5. Configured Grafana dashboards to visualize data from InfluxDB, giving stakeholders an intuitive interface to monitor trends, spot anomalies, and drive data-backed decision-making.