> ## Documentation Index
> Fetch the complete documentation index at: https://cubed3-feat-druid-driver-streaming.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Apache Superset / Preset

> Apache Superset is a popular open-source data exploration and visualization platform. Preset is a fully-managed service for Superset.

[Apache Superset][superset] is a popular open-source data exploration and
visualization platform. [Preset][preset] is a fully-managed service for
Superset.

Here's a short video guide:

<iframe width="100%" height="400" src="https://www.loom.com/embed/3e85b7fe3fef4c7bbb8b255ad3f2c675" title="Loom video" frameBorder="0" allowFullScreen />

## Connect from Cube Cloud

It is recommended to use [Semantic Layer Sync][ref-sls] to connect Cube Cloud to
Superset or Preset. It automatically synchronizes the [data model][ref-data-model]
with Superset or Preset.

Navigate to the [Integrations](/admin/connect-to-data/visualization-tools)
page, click **Connect to Cube**, and choose **Apache Superset** or
**Preset** to get detailed instructions.

## Connect from Cube Core

You can connect a Cube deployment to Superset or Preset using the [SQL API][ref-sql-api].

In Cube Core, the SQL API is disabled by default. Enable it and [configure
the credentials](/reference/core-data-apis/sql-api#configuration) to
connect to Metabase.

## Connecting from Superset or Preset

Superset and Preset connect to Cube as to a Postgres database.

### Creating a connection

In Superset or Preset, go to **Data > Databases**, then click **+ Database** to add
a new database:

<Frame>
  <img src="https://ucarecdn.com/cb3fe91a-6ce2-4c0e-8997-fdb2d4507beb/" alt="Apache Superset: databases page" />
</Frame>

### Querying data

Your cubes will be exposed as tables, where both your measures and dimensions
are columns.

Let's use the following Cube data model:

<CodeGroup>
  ```yaml title="YAML" theme={null}
  cubes:
    - name: orders
      sql_table: orders

      measures:
        - name: count
          type: count

      dimensions:
        - name: status
          sql: status
          type: string

        - name: created
          sql: created_at
          type: time
  ```

  ```javascript title="JavaScript" theme={null}
  cube(`orders`, {
    sql_table: `orders`,

    measures: {
      count: {
        type: `count`
      }
    },

    dimensions: {
      status: {
        sql: `status`,
        type: `string`
      },

      created_at: {
        sql: `created_at`,
        type: `time`
      }
    }
  })
  ```
</CodeGroup>

Using the SQL API, `orders` will be exposed as a table. In Superset, we can
create datasets based on tables. Let's create one from `orders` table:

<Frame>
  <img src="https://ucarecdn.com/eebd3839-03db-4492-a7e4-a7cc1d9ffc73/" alt="Apache Superset: SQL Editor page with successful query" />
</Frame>

Now, we can explore this dataset. Let's create a new chart of type line with
"Orders" dataset.

<Frame>
  <img src="https://ucarecdn.com/c509c6fb-f633-4ded-ae94-d4768978c47a/" alt="Apache Superset: SQL Editor page with successful query" />
</Frame>

We can select the `COUNT(*)` as a metric and `created_at` as the time column
with a time grain of `month`.

The `COUNT(*)` aggregate function is being mapped to a measure of type
[count](/reference/data-modeling/measures#type) in Cube's
**Orders** data model file.

[superset]: https://superset.apache.org/

[preset]: https://preset.io

[ref-sls]: /docs/integrations/semantic-layer-sync

[ref-sql-api]: /reference/core-data-apis/sql-api

[ref-data-model]: /docs/data-modeling/overview