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Redash

307
439
+ 1
11
Superset

309
824
+ 1
39
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Redash vs Superset: What are the differences?

Developers describe Redash as "Easily query an existing database, share the dataset and visualize it in different ways". 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. On the other hand, Superset is detailed as "Data exploration and visualization platform, by Airbnb". 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.

Redash and Superset can be categorized as "Business Intelligence" tools.

Some of the features offered by Redash are:

  • Query Editor
  • Dashboards/Visualizations
  • Alerts

On the other hand, Superset provides the following key features:

  • 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

Redash and Superset are both open source tools. Superset with 25.1K GitHub stars and 4.83K forks on GitHub appears to be more popular than Redash with 13.1K GitHub stars and 2.17K GitHub forks.

Sentry, Implisit, and OTOBANK are some of the popular companies that use Redash, whereas Superset is used by Airbnb, Dial Once, and Streamroot. Redash has a broader approval, being mentioned in 38 company stacks & 17 developers stacks; compared to Superset, which is listed in 18 company stacks and 5 developer stacks.

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Pros of Redash
Pros of Superset
  • 8
    Open Source
  • 3
    SQL Friendly
  • 10
    Awesome interactive filtering
  • 6
    Wide SQL database support
  • 6
    Free
  • 6
    Shareable & editable dashboards
  • 5
    Great for data collaborating on data exploration
  • 3
    User & Role Management
  • 3
    Easy to share charts & dasboards

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Cons of Redash
Cons of Superset
  • 1
    All results are loaded into RAM before displaying
  • 1
    Memory Leaks
  • 3
    Link diff db together "Data Modeling "
  • 3
    It is difficult to install on the server
  • 3
    Ugly GUI

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What is 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.

What is Superset?

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.

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What companies use Redash?
What companies use Superset?
See which teams inside your own company are using Redash or Superset.
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What tools integrate with Redash?
What tools integrate with Superset?

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What are some alternatives to Redash and Superset?
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.
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.
Metabase
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.
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.
See all alternatives