Lightdash
Lightdash is the AI-first BI platform built for modern data teams, combining the openness of dbt with the performance of ClickHouse. By connecting ClickHouse to Lightdash, teams get an AI-powered self-serve analytics experience grounded in their dbt semantic layer, so every question is answered with governed, consistent metrics.
Developers love Lightdash for its open architecture, version-controlled YAML models, and integrations that fit directly into their workflow - from GitHub to the IDE.
This partnership brings together ClickHouse’s speed and Lightdash’s developer experience, making it easier than ever to explore, visualize, and automate insights with AI.
Build an interactive dashboard with Lightdash and ClickHouse
In this guide, you’ll see how Lightdash connects to ClickHouse to explore your dbt models and build interactive dashboards.
The example below shows a finished dashboard powered by data from ClickHouse.
Gather connection data
When setting up your connection between Lightdash and ClickHouse, you’ll need the following details:
- Host: The address where your ClickHouse database is running
- User: Your ClickHouse database username
- Password: Your ClickHouse database password
- DB name: The name of your ClickHouse database
- Schema: The default schema used by dbt to compile and run your project (found in your
profiles.yml) - Port: The ClickHouse HTTPS interface port (default:
8443) - Secure: Enable this option to use HTTPS/SSL for secure connections
- Retries: Number of times Lightdash retries failed ClickHouse queries (default:
3) - Start of week: Choose which day your reporting week starts; defaults to your warehouse setting
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 are using self-managed ClickHouse, the connection details are set by your ClickHouse administrator.
Configure your dbt profile for ClickHouse
In Lightdash, connections are based on your existing dbt project.
To connect ClickHouse, make sure your local ~/.dbt/profiles.yml file contains a valid ClickHouse target configuration.
For example:
Create a Lightdash project connected to ClickHouse
Once your dbt profile is configured for ClickHouse, you’ll also need to connect your dbt project to Lightdash.
Because this process is the same for all data warehouses, we won’t go into detail here — you can follow the official Lightdash guide for importing a dbt project:
Import a dbt project → Lightdash Docs
After connecting your dbt project, Lightdash will automatically detect your ClickHouse configuration from the profiles.yml file. Once the connection test succeeds, you’ll be able to start exploring your dbt models and building dashboards powered by ClickHouse.
Explore your ClickHouse data in Lightdash
Once connected, Lightdash automatically syncs your dbt models and exposes:
- Dimensions and measures defined in YAML
- Semantic layer logic, such as metrics, joins, and explores
- Dashboards powered by real-time ClickHouse queries
You can now build dashboards, share insights, and even use Ask AI to generate visualizations directly on top of ClickHouse — no manual SQL required.
Define metrics and dimensions in Lightdash
In Lightdash, all metrics and dimensions are defined directly in your dbt model .yml files. This makes your business logic version-controlled, consistent, and fully transparent.
Defining these in YAML ensures your team is using the same definitions across dashboards and analyses. For example, you can create reusable metrics like total_order_count, total_revenue, or avg_order_value right next to your dbt models — no duplication required in the UI.
To learn more about how to define these, see the following Lightdash guides:
Query your data from tables
Once your dbt project is connected and synced with Lightdash, you can start exploring data directly from your tables (or “explores”).
Each table represents a dbt model and includes the metrics and dimensions you’ve defined in YAML.
The Explore page is made up of five main areas:
- Dimensions and Metrics — all fields available on the selected table
- Filters — restrict the data returned by your query
- Chart — visualize your query results
- Results — view the raw data returned from your ClickHouse database
- SQL — inspect the generated SQL query behind your results
From here, you can build and adjust queries interactively — dragging and dropping fields, adding filters, and switching between visualization types such as tables, bar charts, or time series.
For a deeper look at explores and how to query from your tables, see:
An intro to tables and the Explore page → Lightdash Docs
Build dashboards
Once you’ve explored your data and saved visualizations, you can combine them into dashboards to share with your team.
Dashboards in Lightdash are fully interactive — you can apply filters, add tabs, and view charts powered by real-time ClickHouse queries.
You can also create new charts directly from within a dashboard, which helps keep your projects organized and clutter-free. Charts created this way are exclusive to that dashboard — they can’t be reused elsewhere in the project.
To create a dashboard-only chart:
- Click Add tile
- Select New chart
- Build your visualization in the chart builder
- Save it — it will appear at the bottom of your dashboard
Learn more about how to create and organize dashboards here:
Building dashboards → Lightdash Docs
Ask AI: self-serve analytics powered by dbt
AI Agents in Lightdash make data exploration truly self-serve.
Instead of writing queries, users can simply ask questions in plain language — like “What was our monthly revenue growth?” — and the AI Agent automatically generates the right visualization, referencing your dbt-defined metrics and models to ensure accuracy and consistency.
It’s powered by the same semantic layer you use in dbt, meaning every answer stays governed, explainable, and fast — all backed by ClickHouse.
Learn more about AI Agents here: AI Agents → Lightdash Docs
Learn more
To learn more about connecting dbt projects to Lightdash, visit the Lightdash Docs → ClickHouse setup.
