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  5. Metabase vs Redash vs Superset

Metabase vs Redash vs Superset

OverviewComparisonAlternatives

Overview

Redash
Redash
Stacks338
Followers502
Votes12
Metabase
Metabase
Stacks926
Followers1.2K
Votes271
GitHub Stars44.4K
Forks6.0K
Superset
Superset
Stacks420
Followers1.0K
Votes45

Metabase vs Redash vs Superset: What are the differences?

Comparison of Metabase, Redash, and Superset

Metabase, Redash, and Superset are popular open-source business intelligence tools that allow users to easily explore, visualize, and share their data. Despite having similar capabilities, there are several key differences between these three platforms.

  1. Data source connectivity: Metabase enables users to connect to various databases using a simple point-and-click interface, making it beginner-friendly. Redash, on the other hand, offers a wider range of data source connectivity options, including SQL databases, cloud storage, NoSQL databases, and more. Superset excels in this aspect, supporting a vast number of data sources, including SQL-based databases, data lakes, Druid, and many more.

  2. Visualization options: Metabase offers a straightforward set of visualization options, providing basic charts like bar graphs, line graphs, and pie charts. Redash provides a similar range of visualizations, with the addition of heatmaps, pivot tables, and more advanced charting capabilities. Superset stands out with its extensive collection of visualizations, including geospatial charts, bubble charts, treemaps, and sankey diagrams, allowing users to create more complex and sophisticated visualizations.

  3. Data exploration capabilities: Metabase offers a user-friendly data exploration experience, allowing non-technical users to easily navigate and analyze data. Redash provides a more advanced data exploration environment, incorporating features like ad-hoc queries, parameterized queries, and interactive dashboards. Superset excels in this area, offering advanced exploration features such as filtering, drilling down, data slicing, and dicing, empowering users to dive deeper into their data.

  4. Collaboration and sharing: Metabase allows simple sharing of dashboards and reports by generating shareable links. Redash takes collaboration a step further by providing team collaboration features, allowing users to work together and share queries, dashboards, and visualizations within a team or organization. Superset offers extensive collaboration features, including dashboards versioning, annotation layers, and granular access controls, enabling seamless collaboration and information sharing within large organizations.

  5. Extensibility and customization: Metabase allows limited customization options, primarily focusing on configuring dashboards and email schedules. Redash provides more flexibility, allowing users to customize visualizations with custom SQL queries, create custom alerts, and integrate with external services through webhooks and API. Superset excels in extensibility, offering a rich set of customizable options through its plugin architecture, allowing users to extend and modify the platform to suit their specific needs.

  6. Community support and development: Metabase has a large and active community with regular updates and bug fixes. Redash has a smaller community but offers regular updates and features driven by the community. Superset has a strong community support and development, backed by companies like Airbnb, Lyft, and Twitter, ensuring regular updates, bug fixes, and new feature additions.

In summary, Metabase is a beginner-friendly tool with easy data connectivity; Redash offers more advanced features for data exploration and collaboration; and Superset stands out with its extensive visualization options, sophisticated exploration capabilities, and strong community support. Ultimately, the choice among these three platforms depends on the specific needs and requirements of your organization.

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Detailed Comparison

Redash
Redash
Metabase
Metabase
Superset
Superset

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.

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.

Query Editor;Dashboards/Visualizations;Alerts;API;Support for querying multiple databases
-
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
-
GitHub Stars
44.4K
GitHub Stars
-
GitHub Forks
-
GitHub Forks
6.0K
GitHub Forks
-
Stacks
338
Stacks
926
Stacks
420
Followers
502
Followers
1.2K
Followers
1.0K
Votes
12
Votes
271
Votes
45
Pros & Cons
Pros
  • 9
    Open Source
  • 3
    SQL Friendly
Cons
  • 1
    Memory Leaks
  • 1
    All results are loaded into RAM before displaying
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
    Wide SQL database support
  • 6
    Shareable & editable dashboards
  • 5
    Great for data collaborating on data exploration
Cons
  • 4
    Link diff db together "Data Modeling "
  • 3
    Ugly GUI
  • 3
    It is difficult to install on the server
Integrations
PostgreSQL
PostgreSQL
Cassandra
Cassandra
MongoDB
MongoDB
Amazon DynamoDB
Amazon DynamoDB
Amazon RDS
Amazon RDS
Amazon Athena
Amazon Athena
Jira
Jira
PagerDuty
PagerDuty
Prometheus
Prometheus
Slack
Slack
PostgreSQL
PostgreSQL
MongoDB
MongoDB
Amazon Redshift
Amazon Redshift
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server
No integrations available

What are some alternatives to Redash, Metabase, Superset?

Cube

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

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

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

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

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

Shiny

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.

Azure Synapse

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

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

Looker

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.

Tableau

Tableau

Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.

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