> For the complete documentation index, see [llms.txt](https://medomicslab.gitbook.io/medomics-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://medomicslab.gitbook.io/medomics-docs/tutorials/superset/superset-features.md). # Superset Features This page goes through all the superset features and provides information on how to use them. ### Connect to Other Databases {% hint style="info" %} Pro Tip: Ensure you have installed the proper database driver(s) for your database before attempting to establish the connection. You can find more details [here](https://superset.apache.org/docs/configuration/databases). {% endhint %} Superset provides users with a streamlined way to connect to various remote databases, including SQLite, PostgreSQL, and more. This feature enables the integration of remote data for creating dashboards and visualizing charts. In MEDomicsLab, after [launching Superset](/medomics-docs/tutorials/superset.md#launch-superset-button), you can easily connect to any remote database via the "Connect a Database" interface. To access this, navigate to ***Settings -> Database Connections***

How to open Database Connections Interface

Once the interface is open (see image below), select your desired database—this tutorial uses PostgreSQL as an example.

Then enter your database credentials, such as the host, port, username, and other required details. Refer to the image below for guidance.

Connection interface to PostgreSQL through Superset

Once you hit and ***CONNECT***, you should see a confirmation message as depicted below:

If you encounter an error, please refer to the [Superset documentation](https://superset.apache.org/docs/configuration/databases) for guidance on troubleshooting the issue. Should the problem persist, feel free to [reach out to us](/medomics-docs/forms/contact-us.md) for further assistance. ### Explore & Visualize To explore data from your connected dataset, you can use either SQL Lab or the "Add Dataset" option, as illustrated in the figure below: * ***SQL Lab***: Provides a complete preview of the data, displays column data types, and allows for custom SQL queries on the dataset.

How to save the output data of your SQL query

* ***Add new dataset***: Lets you explore the columns and their data types, with the ability to create charts based on the data if needed.

Steps to create a dataset and a chart from a connected database

### Create Dashboards One of the main features of Superset is charts and dashboard creation. It offers a no-code procedure for building charts quickly. To do so: * Create a new dashboard:

* Create a new chart and add it to the dashboard:

Create new chart and add it to a dashboard

### Export Data to Workspace Superset enables data exportation through SQL Lab. This empowers users to further process and explore their data within MEDomicsLab by leveraging the different tools available.

### Useful resources * How to [configure your Superset](https://superset.apache.org/docs/configuration/configuring-superset)? * [Connect to a database](https://superset.apache.org/docs/configuration/databases). * How to [use Superset](https://superset.apache.org/docs/using-superset/creating-your-first-dashboard) (dashboard creation, data exploration, etc.) * [FAQ](https://superset.apache.org/docs/faq) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://medomicslab.gitbook.io/medomics-docs/tutorials/superset/superset-features.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.