> ## Documentation Index
> Fetch the complete documentation index at: https://clickhouse.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Connecting Chartbrew to ClickHouse
> Connect Chartbrew to ClickHouse to create real-time dashboards and client reports.
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[Chartbrew](https://chartbrew.com) is a data visualization platform that allows you to create dashboards and monitor data in real time. It supports multiple data sources, including ClickHouse, and provides a no-code interface for building charts and reports.
Goal
In this guide, you will connect Chartbrew to ClickHouse, run a SQL query, and create a visualization. By the end, your dashboard may look something like this:
**Add some data**
If you don't have a dataset to work with, you can add one of the examples. This guide uses the [UK Price Paid](/docs/get-started/sample-datasets/uk-price-paid) dataset.
To connect to ClickHouse with HTTP(S) you need this information:
| Parameter(s) | Description |
| ------------------------- | -------------------------------------------------------------------------------------------------------------- |
| `HOST` and `PORT` | Typically, the port is 8443 when using TLS or 8123 when not using TLS. |
| `DATABASE NAME` | Out of the box, there is a database named `default`, use the name of the database that you want to connect to. |
| `USERNAME` and `PASSWORD` | Out of the box, the username is `default`. Use the username appropriate for your use case. |
The details for your ClickHouse Cloud service are available in the ClickHouse Cloud console.
Select a service and click **Connect**:
Choose **HTTPS**. Connection details are displayed in an example `curl` command.
If you're using self-managed ClickHouse, the connection details are set by your ClickHouse administrator.
1. Log in to [Chartbrew](https://chartbrew.com/login) and go to the **Connections** tab.
2. Click **Create connection** and select **ClickHouse** from the available database options.
3. Enter the connection details for your ClickHouse database:
* **Display Name**: A name to identify the connection in Chartbrew.
* **Host**: The hostname or IP address of your ClickHouse server.
* **Port**: Typically `8443` for HTTPS connections.
* **Database Name**: The database you want to connect to.
* **Username**: Your ClickHouse username.
* **Password**: Your ClickHouse password.
4. Click **Test connection** to verify that Chartbrew can connect to ClickHouse.
5. If the test is successful, click **Save connection**. Chartbrew will automatically retrieve the schema from ClickHouse.
1. Click on the **Create dataset** button or navigate to the **Datasets** tab to create one.
2. Select the ClickHouse connection you created earlier.
Write a SQL query to retrieve the data you want to visualize. For example, this query calculates the average price paid per year from the `uk_price_paid` dataset:
```sql theme={null}
SELECT toYear(date) AS year, avg(price) AS avg_price
FROM uk_price_paid
GROUP BY year
ORDER BY year;
```
Click **Run query** to fetch the data.
If you're unsure how to write the query, you can use **Chartbrew's AI assistant** to generate SQL queries based on your database schema.
Once the data is retrieved, click **Configure dataset** to set up the visualization parameters.
1. Define a metric (numerical value) and dimension (categorical value) for your visualization.
2. Preview the dataset to ensure the query results are structured correctly.
3. Choose a chart type (e.g., line chart, bar chart, pie chart) and add it to your dashboard.
4. Click **Complete dataset** to finalize the setup.
You can create as many datasets as you want to visualize different aspects of your data. Using these datasets, you can create multiple dashboards to keep track of different metrics.
To keep your dashboard up-to-date, you can schedule automatic data updates:
1. Click the Calendar icon next to the dataset refresh button.
2. Configure the update interval (e.g., every hour, every day).
3. Save the settings to enable automatic refresh.
Learn more
For more details, check out the blog post about [Chartbrew and ClickHouse](https://chartbrew.com/blog/visualizing-clickhouse-data-with-chartbrew-a-step-by-step-guide/).
