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Merge pull request #3450 from razvanilin/main
Add Chartbrew to the data visualization integrations page
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---
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title: 'Connecting Chartbrew to ClickHouse'
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sidebar_label: 'Chartbrew'
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sidebar_position: 131
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slug: /integrations/chartbrew-and-clickhouse
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keywords: ['ClickHouse', 'Chartbrew', 'connect', 'integrate', 'visualization']
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description: 'Connect Chartbrew to ClickHouse to create real-time dashboards and client reports.'
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---
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import chartbrew_01 from '@site/static/images/integrations/data-visualization/chartbrew_01.png';
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import chartbrew_02 from '@site/static/images/integrations/data-visualization/chartbrew_02.png';
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import chartbrew_03 from '@site/static/images/integrations/data-visualization/chartbrew_03.png';
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import chartbrew_04 from '@site/static/images/integrations/data-visualization/chartbrew_04.png';
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import chartbrew_05 from '@site/static/images/integrations/data-visualization/chartbrew_05.png';
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import chartbrew_06 from '@site/static/images/integrations/data-visualization/chartbrew_06.png';
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import chartbrew_07 from '@site/static/images/integrations/data-visualization/chartbrew_07.png';
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import chartbrew_08 from '@site/static/images/integrations/data-visualization/chartbrew_08.png';
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import chartbrew_09 from '@site/static/images/integrations/data-visualization/chartbrew_09.png';
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import ConnectionDetails from '@site/docs/_snippets/_gather_your_details_http.mdx';
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import CommunityMaintainedBadge from '@theme/badges/CommunityMaintained';
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import Image from '@theme/IdealImage';
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# Connecting Chartbrew to ClickHouse
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<CommunityMaintainedBadge/>
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[Chartbrew](https://chartbrew.com) is a data visualization platform that allows users 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.
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## Goal {#goal}
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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:
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<Image img={chartbrew_01} size="lg" alt="Chartbrew dashboard" />
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:::tip Add some data
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If you do not have a dataset to work with, you can add one of the examples. This guide uses the [UK Price Paid](/getting-started/example-datasets/uk-price-paid.md) dataset.
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:::
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## 1. Gather your connection details {#1-gather-your-connection-details}
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<ConnectionDetails />
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## 2. Connect Chartbrew to ClickHouse {#2-connect-chartbrew-to-clickhouse}
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1. Log in to [Chartbrew](https://chartbrew.com/login) and go to the **Connections** tab.
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2. Click **Create connection** and select **ClickHouse** from the available database options.
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<Image img={chartbrew_02} size="lg" alt="Select ClickHouse connection in Chartbrew" />
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3. Enter the connection details for your ClickHouse database:
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- **Display Name**: A name to identify the connection in Chartbrew.
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- **Host**: The hostname or IP address of your ClickHouse server.
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- **Port**: Typically `8443` for HTTPS connections.
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- **Database Name**: The database you want to connect to.
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- **Username**: Your ClickHouse username.
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- **Password**: Your ClickHouse password.
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<Image img={chartbrew_03} size="lg" alt="ClickHouse connection settings in Chartbrew" />
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4. Click **Test connection** to verify that Chartbrew can connect to ClickHouse.
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5. If the test is successful, click **Save connection**. Chartbrew will automatically retrieve the schema from ClickHouse.
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<Image img={chartbrew_04} size="lg" alt="ClickHouse JSON schema in Chartbrew" />
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## 3. Create a dataset and run a SQL query {#3-create-a-dataset-and-run-a-sql-query}
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1. Click on the **Create dataset** button or navigate to the **Datasets** tab to create one.
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2. Select the ClickHouse connection you created earlier.
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<Image img={chartbrew_05} size="lg" alt="Select ClickHouse connection for dataset" />
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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:
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```sql
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SELECT toYear(date) AS year, avg(price) AS avg_price
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FROM uk_price_paid
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GROUP BY year
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ORDER BY year;
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```
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<Image img={chartbrew_07} size="lg" alt="ClickHouse SQL query in Chartbrew" />
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Click **Run query** to fetch the data.
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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.
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<Image img={chartbrew_06} size="lg" alt="ClickHouse AI SQL assistant in Chartbrew" />
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Once the data is retrieved, click **Configure dataset** to set up the visualization parameters.
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## 4. Create a visualization {#4-create-a-visualization}
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1. Define a metric (numerical value) and dimension (categorical value) for your visualization.
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2. Preview the dataset to ensure the query results are structured correctly.
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3. Choose a chart type (e.g., line chart, bar chart, pie chart) and add it to your dashboard.
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4. Click **Complete dataset** to finalize the setup.
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<Image img={chartbrew_08} size="lg" alt="Chartbrew dashboard with ClickHouse data" />
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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.
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<Image img={chartbrew_01} size="lg" alt="Chartbrew dashboard with ClickHouse data" />
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## 5. Automate data updates {#5-automate-data-updates}
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To keep your dashboard up-to-date, you can schedule automatic data updates:
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1. Click the Calendar icon next to the dataset refresh button.
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2. Configure the update interval (e.g., every hour, every day).
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3. Save the settings to enable automatic refresh.
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<Image img={chartbrew_09} size="lg" alt="Chartbrew dataset refresh settings" />
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## Learn more {#learn-more}
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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/).

docs/integrations/data-visualization/index.md

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- [Apache Superset](./superset-and-clickhouse.md)
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- [Astrato](./astrato-and-clickhouse.md)
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- [Chartbrew](./chartbrew-and-clickhouse.md)
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- [Deepnote](./deepnote.md)
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- [Draxlr](./draxlr-and-clickhouse.md)
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- [Explo](./explo-and-clickhouse.md)
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| [Apache Superset](./superset-and-clickhouse.md) | ClickHouse official connector ||| |
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| [Astrato](./astrato-and-clickhouse.md) | Native connector ||| Works natively using pushdown SQL (direct query only). |
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| [AWS QuickSight](./quicksight-and-clickhouse.md) | MySQL interface ||| Works with some limitations, see [the documentation](./quicksight-and-clickhouse.md) for more details |
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| [Chartbrew](./chartbrew-and-clickhouse.md) | ClickHouse official connector ||| |
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| [Deepnote](./deepnote.md) | Native connector ||| |
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| [Explo](./explo-and-clickhouse.md) | Native connector ||| |
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| [Grafana](./grafana/index.md) | ClickHouse official connector ||| |

docs/integrations/index.mdx

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import Hashboardsvg from '@site/static/images/integrations/logos/hashboard.svg';
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import luzmo_logo from '@site/static/images/integrations/logos/luzmo.png';
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import vs_logo from '@site/static/images/integrations/logos/logo_vs.png';
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import chartbrew_logo from '@site/static/images/integrations/logos/logo_chartbrew.png';
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import Image from '@theme/IdealImage';
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ClickHouse integrations are organized by their support level:
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|BlinkOps|<Image img={blinkops_logo} size="logo" alt="BlinkOps Logo"/>|Security automation|Create automations to manage data and user permissions.|[Documentation](https://docs.blinkops.com/docs/integrations/clickhouse)|
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|Bytewax|<Bytewaxsvg alt="ByteWax Logo" style={{width: '3rem'}}/>|Data ingestion|Open source Python stream processor for transforming and ingesting data to ClickHouse|[Documentation](https://bytewax.io/blog/building-a-click-house-sink-for-bytewax)|
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|Calyptia (Fluent Bit)|<Image img={calyptia_logo} size="logo" alt="Calyptia logo"/>|Data ingestion|CNCF graduated open-source project for the collection, processing, and delivery of logs, metrics, and traces|[Blog](https://clickhouse.com/blog/kubernetes-logs-to-clickhouse-fluent-bit)|
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|Chartbrew|<Image img={chartbrew_logo} size="logo" alt="Chartbrew logo" style={{width: '3rem', 'backgroundColor': 'transparent', 'boxShadow': 'none'}}/>|Data visualization|Chartbrew is a data visualization platform that allows users to create dashboards and monitor data in real time.|[Documentation](/integrations/chartbrew-and-clickhouse),<br />[Website](https://chartbrew.com/integrations/clickhouse),<br />[Blog](https://chartbrew.com/blog/visualizing-clickhouse-data-with-chartbrew-a-step-by-step-guide/)|
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|CloudCanal|<Cloudcanalsvg className="image" alt="CloudCanal logo" style={{width: '3rem'}}/>|Data integration|A data synchronization and migration tool.|[Website](https://www.cloudcanalx.com/us/)|
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|CloudQuery|<Cloudquerysvg className="image" alt="CloudQuery logo" style={{width: '3rem'}}/>|Data ingestion|Open source high-performance ELT framework.|[Documentation](https://www.cloudquery.io/docs/plugins/destinations/clickhouse/overview)|
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|Cube.js|<Cubejssvg alt="Cubejs logo" style={{width: '3rem'}}/>|Data visualization|Cube is the Semantic Layer for building data apps.|[Website](https://cube.dev/for/clickhouse-dashboard)|

scripts/aspell-ignore/en/aspell-dict.txt

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items: [
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"integrations/data-visualization/astrato-and-clickhouse",
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"integrations/data-visualization/chartbrew-and-clickhouse",
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"integrations/data-visualization/draxlr-and-clickhouse",
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"integrations/data-visualization/embeddable-and-clickhouse",
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"integrations/data-visualization/explo-and-clickhouse",
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