Overview

Metabase

Stacks926

Followers1.2K

Votes271

GitHub Stars44.4K

Forks6.0K

Superset

Stacks420

Followers1.0K

Votes45

Metabase vs Superset: What are the differences?

Metabase: Metabase is an open-source business intelligence (BI) tool that allows users to generate charts, dashboards, and ad hoc queries for data analysis. It provides a simple and intuitive interface for non-technical users to explore and visualize data without the need for SQL knowledge. Metabase supports a wide range of data sources, including SQL databases, and offers various visualization options.
Superset: Superset, also known as Apache Superset, is an open-source data exploration and visualization platform that supports data exploration, data visualization, and data dashboards. It offers an intuitive interface for users to create, organize, and share interactive dashboards. Superset supports a variety of data sources, including SQL databases, and provides extensive customization options for visualizations.
Data Sources: Metabase supports a wide range of data sources, including SQL databases, MongoDB, and Google Analytics, to name a few. In contrast, Superset primarily focuses on SQL databases and supports various relational databases like MySQL, PostgreSQL, and SQLite.
User Interface and Usability: Metabase prioritizes simplicity and ease of use, making it suitable for non-technical users. Its user interface is designed to be minimalistic and intuitive, allowing users to easily explore and analyze data without extensive technical knowledge. On the other hand, Superset offers a more extensive set of features and customization options, making it better suited for users with more advanced technical skills and specific data exploration needs.
Community and Open-Source Ecosystem: Metabase has a relatively larger and more active community of contributors and users, which results in frequent updates, bug fixes, and community-driven plugins. Superset, being part of the Apache Software Foundation, benefits from a wider open-source ecosystem and community support, ensuring a robust and stable platform with constant development and improvement.
Advanced Analytics and Data Processing: While both Metabase and Superset provide basic data exploration and visualization capabilities, Superset offers more advanced analytics features such as parameterized queries, data slicing and dicing, and cohort analysis. It also supports complex data processing operations like transformations, aggregations, and joins, making it suitable for more complex data analysis scenarios.

In summary, the key differences between Metabase and Superset lie in their supported data sources, user interface and usability, community and open-source ecosystem, and advanced analytics capabilities. Metabase focuses on simplicity and ease of use, whereas Superset offers more customization options and advanced features for users with specific data exploration needs.

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Detailed Comparison

Metabase	Superset
It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.	Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.
-	A rich set of visualizations to analyze your data, as well as a flexible way to extend the capabilities;An extensible, high granularity security model allowing intricate rules on who can access which features, and integration with major authentication providers (database, OpenID, LDAP, OAuth & REMOTE_USER through Flask AppBuiler);A simple semantic layer, allowing to control how data sources are displayed in the UI, by defining which fields should show up in which dropdown and which aggregation and function (metrics) are made available to the user;Deep integration with Druid allows for Caravel to stay blazing fast while slicing and dicing large, realtime datasets;
Statistics
GitHub Stars 44.4K	GitHub Stars -
GitHub Forks 6.0K	GitHub Forks -
Stacks 926	Stacks 420
Followers 1.2K	Followers 1.0K
Votes 271	Votes 45
Pros & Cons
Pros 62 Database visualisation 45 Open Source 41 Easy setup 36 Dashboard out of the box 23 Free Cons 7 Harder to setup than similar tools	Pros 13 Awesome interactive filtering 9 Free 6 Shareable & editable dashboards 6 Wide SQL database support 5 Great for data collaborating on data exploration Cons 4 Link diff db together "Data Modeling " 3 It is difficult to install on the server 3 Ugly GUI
Integrations
PostgreSQL MongoDB Amazon Redshift MySQL Microsoft SQL Server	No integrations available

What are some alternatives to Metabase, Superset?

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

Mode

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Shiny

It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

Redash

Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Periscope

Periscope is a data analysis tool that uses pre-emptive in-memory caching and statistical sampling to run data analyses really, really fast.

Looker

We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.