Teams with data in databases need a way to investigate it and share understandable views with others. Apache Superset provides a browser-based environment for exploring datasets, running SQL and building charts and dashboards. It connects to data sources while maintaining its own metadata about users, saved work and connections. That metadata and its encryption key are essential recovery material. On Kubernetes, use the project’s current installation guidance and treat a chart-to-operator change as a managed migration.
Current guidance
The current upstream Helm-chart README explicitly deprecates that chart and directs new Kubernetes deployments to the official Apache Superset Kubernetes Operator. Older installation documentation still describes the chart, so use the current deprecation notice and operator migration guide together rather than assuming the familiar repository remains the preferred installation.
The operator changes lifecycle management from chart hooks to reconciliation tasks and expects separately provisioned database and Redis/Valkey dependencies. Its migration guide requires production secrets to come from Kubernetes Secrets. Preserve the metadata database, application SECRET_KEY, connection credentials and configuration; losing or casually replacing the key can make encrypted connection information unusable.
Export the actual release values and compare them with the migration guide’s stated chart-version baseline. Rehearse the ownership handover, database migration and secret-key handling with isolated copies before removing bundled dependencies. Verify dashboards, saved SQL, permissions, workers and scheduled reports as representative users. Keep a matched data/configuration backup and prevent the old chart and new operator from simultaneously managing the same resources. No Helm-to-operator migration was executed for this advisory.
Historical upstream link check · 2026-10-09
The recorded upstream address redirects to https://superset.apache.org/ and returned HTTP 200 on 2026-10-09. Link availability does not certify the historical installation instructions or current security support.
Website availability is separate from project, chart and image support. Use the current guidance and primary sources on this page to assess the distribution.
Historical Kubedex content
Original publication: 2018-09-19T20:03:28+00:00. Preserved for context. Commands, versions, prices and results below reflect the original research.
Apache Superset (incubating) is a modern, enterprise-ready business intelligence web application. This chart bootstraps an Apache superset deployment on a Kubernetes cluster using the Helm package manager.
Apache Superset is a data exploration and visualization web application.
Superset provides:
- An intuitive interface to explore and visualize datasets, and create interactive dashboards.
- A wide array of beautiful visualizations to showcase your data.
- Easy, code-free, user flows to drill down and slice and dice the data underlying exposed dashboards. The dashboards and charts act as a starting point for deeper analysis.
- A state of the art SQL editor/IDE exposing a rich metadata browser, and an easy workflow to create visualizations out of any result set.
- An extensible, high granularity security model allowing intricate rules on who can access which product features and datasets.
Integration with major authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, …) - A lightweight semantic layer, allowing to control how data sources are exposed to the user by defining dimensions and metrics
Out of the box support for most SQL-speaking databases - Deep integration with Druid allows for Superset to stay blazing fast while slicing and dicing large, real-time datasets
Fast loading dashboards with configurable caching
Database Support
Superset speaks many SQL dialects through SQLAlchemy, a Python ORM that is compatible with most common databases.
The superset can be used to visualize data out of most databases:
- MySQL
- Postgres
- Vertica
- Oracle
- Microsoft SQL Server
- SQLite
- Greenplum
- Firebird
- MariaDB
- Sybase
- IBM DB2
- Exasol
- MonetDB
- Snowflake
- Redshift
- Clickhouse
- Apache Kylin
- more! look for the availability of an SQLAlchemy dialect for your database to find out whether it will work with Superset
Features
- A rich set of data visualizations
- An easy-to-use interface for exploring and visualizing data
- Create and share dashboards
- Enterprise-ready authentication with integration with major authentication providers (database, OpenID, LDAP, OAuth & REMOTE_USER through Flask AppBuilder)
- An extensible, high-granularity security/permission model allowing intricate rules on who can access individual features and the dataset
- A simple semantic layer, allowing users to control how data sources are displayed in the UI by defining which fields should show up in which drop-down and which aggregation and function metrics are made available to the user
- Integration with most SQL-speaking RDBMS through SQLAlchemy
- Deep integration with Druid.io
Selection process
We used Superset in our project for a fitness mobile app with a huge fast-growing customer base. On the one hand, a BI tool was requested by business stakeholders, who needed a number of specific reports to monitor trend changes in application usage and better understand customer behavior. On the other hand, a BI tool could be used by our data science team to perform exploratory data analysis in relation to different user cohorts before building Machine Learning models.
We needed a tool that would satisfy the following requirements:
- interactivity. The BI users from the marketing department wanted to have interactive filters on many fields of different field types, e.g., string, date, integer filters.
- no coding. Our users were mostly marketing professionals so it was supposed that all the functionality should be accessible via buttons and other controls.
- completely free! We were looking for an open source data visualization tool.
- After searching for available solutions, we selected Superset and Pentaho for further evaluation.
Superset was seen as a more attractive tool for us for the following reasons:
- Superset visualizations looked more appealing to us. Both the customer and our team loved the visualizations right away.
- The superset is completely open-source and implemented in Python, while Pentaho is in Java. It was a big plus for us because we were mostly a pythonic team. And running ahead, I should say that it served us really well.
- The superset is quite a new tool, so we were interested in testing it. We were already familiar with Pentaho from the previous projects but we were not completely satisfied with it.
A short introduction to the tool
The superset is a data exploration platform designed to be visual, intuitive and interactive. Superset’s main goal is to make it easy to slice, dice and visualize data. Its developer claims that Superset can perform analytics at the speed of thought. As we have already mentioned the open source data visualization tool is written in pythonic web framework Flask.
This project was originally named Panoramix, was renamed to Caravel in March 2016, and is currently named Superset as of November 2016. Source.
Key Features
- Superset supports 30 types of visualizations
- Airbnb uses Superset with Druid, but it accepts all the data sources that support SQL Alchemy.
- configurable caching options for loading dashboards
- easy to use the constructor for visualizations
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