Apache Superset and Tableau are Business Intelligence tools used by organizations to visualize and gain insight from their data. Apache Superset is an open-source cloud-native application that can handle data at the petabyte scale. Tableau is known in the community as a leader in Analytics with a platform that is easy to use and offers countless integrations.

This article will give you an idea about Apache Superset vs Tableau on 4 different fronts namely platform features, flexibility, supported data sources, authorization, authentication, and pricing.

Understanding the Key Differences between Apache Superset and Tableau

Here is a look at the six critical factors you can keep in mind to make an educated guess about Apache Superset vs Tableau and pick the Business Intelligence tool that best suits your needs:

Platform

Tableau operates on a number of platforms as follows:

  • Desktop
  • Mobile
  • Web
  • Embedded

Tableau can very much operate where you can need it. This provides a major advantage; accessing data from anywhere at any time.

Apache Superset on the other hand primarily works on-premise/on the server. Although, experienced developers can find a way around it to get it running on a desktop. It does not yet support mobile, cloud, embedded as Tableau does.

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Supported Data Sources

Tableau uses Tableau Prep when connecting to data sources to combine, shape, and clean your data for analysis. Tableau has a built-in data interpreter that does some cleaning when you import your data. Tableau Prep(which is part of the product suite) can be opted for when you need more cleaning for your data.

Tableau supports a wide range of data sources including File type – JSON, PDF, CSV examples include – Microsoft Excel and Access, database connectors from Saas companies – Amazon Redshift, Amazon Aurora, Google BigQuery, IBM BigInsights, Cloudera Hadoop, Dropbox, Microsoft Azure, etc. Tableau also supports ODBC 3.0 connectors and JDBC.

Apache Superset also supports a large number of databases. The primary ones include:

  • Microsoft SQL Server, Amazon Redshift, Big Query, MySQL, Snowflake, Apache Druid, Firebird, MariaDB, SQLite, Oracle, Postgres, Elasticsearch, Vertica, and some others.

Apache Superset supports any database supported by SQLAlchemy. 

The Apache Superset vs Tableau comparison favors Tableau on the basis of supported data sources. This is because Apache Superset has limited data sources to connect to.

Authentication and Authorization

Apache Superset security is based on Flask App Builder(FAB) – an application development framework built on top of Flask. This framework supports custom security and authentication in case its authentication methods don’t suit a user’s needs. The major authentication types include: 

  • Databases
  • OpenID
  • LDAP
  • REMOTE_USER
  • OAuth

Tableau on the other hand requires authentication on different levels. Tableau allows you to set security at the project level or individual dashboard level. This means even if you have access to the server, you might not be able to access some features if you are not authenticated. This does not exist on Apache Superset’s security layer. The authentication types Tableau supports includes:

  • SAML
  • OpenID
  • Active Directory
  • OAuth

Visualization

Apache Superset comes with rich visualizations and dashboards that help users to explore the dataset using the array of Data Visualizations. The interactive dashboards help users view real-time data updates and monitor business activities. Also, it comes with SQL Lab that allows them to investigate the data thoroughly.

Tableau allows companies to organize and present data intuitively. It allows users to drill down into data and create hierarchies for easy access and understanding of data better. Tableau Filters help users to filter out the data easily so that they can analyze data deeply. Users can easily drag and drop their data into beautiful visualizations.

Pricing

Apache Superset is an open-source platform, so it’s free to install and use if you manage it yourself. Apache Superset is cost-effective for those with in-house IT to manage open-source installations.

Tableau’s pricing is modeled to fit different users. Their pricing plans show that they have their customers in mind. They have pricing for Individuals, Teams/Organizations – this depends on the deployment option(On-premise or cloud) chosen by the organization, and Embedded Analytics. Tableau also allows you to add more licenses to your plans at extra costs.

The pricing plans are quite flexible for users as seen below. For individuals you get the following pricing plan:

Tableau Creator Pricing

For teams:

Pricing Plan for Teams on Tableau
Pricing Plans for Teams deploying the solution with Tableau Online

The pricing for Embedding analytics in an application requires contacting the Tableau sales team. 

Apache Superset breaks the ice here, It is completely free! This concludes the discussion of Apache Superset vs Tableau.

Challenges

Apache Superset

  • Visualization Formats: Apache Superset provides limited visualization formats up to 30.
  • Data Source Connections: Apache Superset connects to only a limited number of data sources.
  • Scope for Growth: The software is still in its development stage and operates only on the server.

Tableau

  • Cost: This is one of the major downsides of Tableau. A small organization might not be able to acquire an appropriate package because of the cost. Tableau’s additional licenses also require additional fees to get, so if any organization using Tableau is not well funded, operating at maximum capacity might be a problem.
  • The complexity of software: Tableau is a Data Visualization software with numerous capabilities. It might require an expert to oversee some activities and this might even call for training and certification. Although Tableau is very beginner-friendly, like any other data analysis software, it requires some level of expertise to perform some activities.
  • Backup Irregularity: Tableau does not regularly backup its software.

Quick Comparison

FeatureTableauApache Superset
PlatformDesktop, mobile, web, embeddedPrimarily server-based, no mobile app
Data SourcesExtensive, including files and APIsLimited, SQL databases only
AuthenticationAdvanced options (SAML, OAuth, etc.)Basic (OAuth, LDAP, etc.)
VisualizationRich and customizable with advanced filtersBasic, fewer visualization options
PricingSubscription-based, flexible plansFree (open-source)
ChallengesCostly, requires expertise for advanced featuresLimited visuals, only server-focused

Conclusion

This article analyzes the discussion of Apache Superset vs Tableau and examines their capacities. Compare Apache Superset and Tableau to determine the best data visualization tool for your specific needs. Tableau is a great choice if security and a variety of visualizations are important. It is also a go-to tool if a company will use Embedded Analytics and dashboard sharing to individuals for personal exploration.

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FAQs

1. What is the Apache equivalent of Tableau?

The Apache equivalent of Tableau is Apache Superset, an open-source business intelligence platform designed for creating interactive dashboards and data visualizations.

2. What are the limitations of the Apache Superset?

Apache Superset has some limitations, such as a limited number of visualization formats (compared to Tableau), fewer data source integrations, and a primary focus on server-based deployment.

3. What is Tableau not really good for?

Tableau can be expensive for smaller teams, especially when scaling up, as additional licenses are required. Additionally, while it’s powerful for data visualization, Tableau can be complex for non-technical users, requiring training for advanced functionality.

Teniola Fatunmbi
Technical Content Writer, Hevo Data

Teniola Fatunmbi is a full-stack software engineer with a keen focus on data analytics. He excels in creating content that bridges the gap between technical complexity and practical application. Teniola's strong analytical skills and exceptional communication abilities enable him to effectively collaborate with non-technical stakeholders to deliver valuable, data-driven insights.