# Build a live sensor temperature dashboard

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/)
- [Get started with TimescaleDB on Google Axion C4A](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/background/)
- [Create a firewall rule for Grafana/TimescaleDB](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/firewall-setup/)
- [Create a Google Axion C4A Arm virtual machine on GCP](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/instance/)
- [Set up TimescaleDB on Arm64](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/setup-timescaledb/)
- [Ingest real-time sensor data on Arm64](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/sensor-data-ingestion/)
- [Install Grafana and configure the TimescaleDB data source](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/grafana-timescaledb-setup/)
- [Build a live sensor temperature dashboard](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/live-sensor-dashboard/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/_next-steps/)

## Create a live sensor temperature dashboard
In this section, you’ll create a Grafana dashboard that visualizes live temperature data stored in TimescaleDB. The dashboard continuously updates to display sensor temperature changes in near real time.

## Prerequisites
Before proceeding, ensure the following are already completed:
- TimescaleDB is installed and running
- Grafana is installed and accessible
- PostgreSQL (TimescaleDB) data source is configured in Grafana
- Live data ingestion into the `sensor_data` table is running

You can verify live ingestion with:
```
sudo -u postgres psql sensors -c "SELECT COUNT(*) FROM sensor_data;"
```
The count should increase over time.

## Access Grafana
Open Grafana in your browser:
```
http://<GRAFANA_PUBLIC_IP>:3000
```
Log in using your Grafana credentials.

## Create a New Dashboard
- From the left sidebar, select **Dashboards**
- Select **New dashboard**
- Select **New visualization**

You will be redirected to the Edit panel screen.

## Configure the Live Sensor Query
In the Query section:
- **Data source:** PostgreSQL / TimescaleDB
- **Query type:** SQL
- **Format:** Time series

![Grafana visualization configuration](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/images/data-source-visualization.png)
Grafana visualization configuration

Paste the following query after selecting **Code** on the right of the query editor:
```
SELECT
  time AS "time",
  temperature
FROM sensor_data
WHERE $__timeFilter(time)
ORDER BY time;
```
![TimescaleDB SQL query editor](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/images/timescale-query.png)
TimescaleDB SQL query editor

This query retrieves live sensor temperature data within the selected time range.

Apply the following settings in the right-hand panel:
- Visualization Settings
  - **Visualization:** Time series
  - **Panel title:** Live Sensor Temperature
  - **Table view:** Disabled
- Time & Refresh Settings
  - **Time range:** Last 5 minutes
  - **Refresh interval:** 5s

These settings ensure the panel refreshes automatically with new data.

## Validate the Live Sensor Panel
Once configured, the panel should display a continuously updating temperature graph.

![Live sensor temperature panel](https://learn.arm.com/learning-paths/servers-and-cloud-computing/timescaledb-on-gcp/images/live-sensor-temperature.webp)
Live sensor temperature panel

## Save the dashboard
- Select **Save dashboard** (top-right corner)
- Enter a name, for example: Live Sensor Monitoring Dashboard
- Select **Save**

The dashboard is now active.

## What you’ve accomplished
You’ve built a complete time-series monitoring pipeline on Google Cloud C4A Axion Arm-based processors. TimescaleDB is running natively on Arm64, ingesting live sensor data through Python, and serving queries to Grafana for real-time visualization. From here you can add more sensors, create additional dashboards, set up alerting rules in Grafana, or tune TimescaleDB for your specific workload.