57K stars! Open source BI artifact, a data visualization tool that is easier to use than paid software

Estimated read time 5 min read

It is worthless for an enterprise to simply have cold data. To achieve business success, how to analyze and utilize the data it has is the key.

Today we recommend an open source data visualization project, which is more powerful than many paid software and can better generate value from data and realize business intelligence. It is: superset.

What is superset?

Superset is a modern data exploration and data visualization platform. Superset can replace or enhance many teams’ proprietary business intelligence tools. Superset integrates well with various data sources. You can quickly build your own analysis charts without coding.

superset supports almost all common databases:

In addition, Superset also provides the following features:

  • A code-free interface for quickly building diagrams
  • Powerful, web-based SQL editor for advanced queries
  • A lightweight semantic layer for quickly defining custom dimensions and metrics
  • Out-of-the-box support for nearly any SQL database or data engine
  • A variety of beautiful visualizations to showcase your data, from simple bar charts to geospatial visualizations
  • Lightweight, configurable caching layer helps reduce database load
  • Highly scalable security roles and authentication options
  • API for programmatic customization
  • Cloud-native architecture designed from the ground up to scale

Install Superset

Superset supports a variety of installation methods, such as Docker, Docker Compose, K8s, PyPI, etc.

For a quick start-up experience, I definitely recommend the Docker method.

First, set the version information of Superset. The following latest_version can be found in the official documentation:

$ export SUPERSET_VERSION=<latest_version>

Then pull Docker

$ docker pull apache/superset:$SUPERSET_VERSION

Start Docker

$ docker run -d -p 8080:8088 \
             -e "SUPERSET_SECRET_KEY=$(openssl rand -base64 42)" \
             -e "TALISMAN_ENABLED=False" \
             --name superset apache/superset:$SUPERSET_VERSION


$ docker exec -it superset superset fab create-admin \
              --username admin \
              --firstname Admin \
              --lastname Admin \
              --email admin@localhost \
              --password admin

Configuration example

$ docker exec -it superset superset db upgrade &&
  docker exec -it superset superset load_examples &&
  docker exec -it superset superset init

Some sample data will be loaded here, so it will take some time.

Use Superset

After you finish configuring the new instance with Superset, go to http://localhost:8080 and log in using the account created by default:

username: admin
password: admin

Because we imported the sample data here, there are already several sample Dashboards here. Let’s take a look at some core contents in superset, because each part will involve a lot of detailed configuration. I am just showing it here, not a detailed tutorial.

Connect to the database

Data analysis tools must first add data sources. Click the plus sign in the upper right corner to open Add Data Source.

By default, sqllite is used in docker of superset, and we loaded a Sample data. If it is actually used, you can connect to your own data source. By default, docker does not come with any connection library, so you need to install the library. I won’t explain them one by one here, you can follow the documentation yourself.

data set

After connecting to the database, you can add the tables in the data as data sets.

The sample data has been imported into the dataset, and we can also make some modifications on it.


Once we have the data set, we can create the icon. Select the associated data set and chart type to create a new chart.

After that, you will enter the detailed configuration page of the chart. Here is what you need to debug in depth until you get the data chart you want.

SQL query

Superset provides SQL query, where you can deeply customize SQL to execute the specified query and generate charts based on the query results.


Next we can create a Dashboard and add the previously created charts to the corresponding Dashboard.

Superset’s functions are very comprehensive. If you have BI needs, it is worth in-depth study and use.

Project characteristics

superset has the following characteristics:

  • Fast and intuitive: No matter how large the amount of data is, Superset can analyze and display it quickly and accurately. It provides a rich range of visualizations, from simple line charts to highly detailed geospatial plots, allowing users to easily browse and explore data visually.
  • Powerful ease of use: Superset offers great ease of use and can be quickly and easily integrated to explore data, all through the SQL IDE or without writing code through the visual builder. It also supports a variety of databases and can be connected to any SQL-based data source through SQL Alchemy, including cloud-native databases and petabyte-scale data engines.
  • Rich visualization methods and dashboards: Superset provides a variety of exquisite visualization effects, from very simple pie charts to complex geospatial charts, all of which can be supported very well. In addition, it supports dashboard functionality, allowing users to create and share customized dashboards according to their needs.
  • Visualization plug-in architecture: Superset has a visualization plug-in architecture that makes building custom visualizations easier. At the same time, it also provides rich API and plug-in interfaces, which can facilitate secondary development and function expansion.
  • Highly Customizable: Superset’s highly customizable nature allows users to configure rules to determine who can access which product features and data sets based on their needs. At the same time, it also supports functions such as customized login verification, which can flexibly meet the various needs of users.
  • Cloud-native architecture: Superset is cloud-native and designed to provide high availability. It scales to large distributed environments and runs well in containers. This means users can easily deploy and scale Superset in cloud environments.

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