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  5. Redash vs Superset

Redash vs Superset

OverviewComparisonAlternatives

Overview

Redash
Redash
Stacks338
Followers502
Votes12
Superset
Superset
Stacks420
Followers1.0K
Votes45

Redash vs Superset: What are the differences?

Key Differences between Redash and Superset

Redash and Superset are both popular open-source data visualization tools that provide the capabilities to create and share interactive dashboards and reports. Although they have similar goals, they have significant differences in terms of features and functionalities. The key differences between Redash and Superset can be summarized as follows:

  1. Data Source Support: Redash supports a wide range of data sources, including popular SQL databases, NoSQL databases, and APIs, making it suitable for diverse data environments. On the other hand, Superset primarily focuses on SQL-based databases and may require additional configurations for other data sources.

  2. Ease of Use: Redash is known for its intuitive user interface and ease of use. It provides a simple and user-friendly environment for data exploration, report creation, and dashboard building. Superset, while also providing a user-friendly interface, may require more technical expertise to fully utilize its advanced features and functionalities.

  3. Visualization Options: Superset offers a rich set of visualization options, including charts, graphs, maps, and more. It provides a comprehensive library of visualizations to choose from and offers extensive customization options. Redash, while also providing standard visualizations, may have a relatively smaller set of visualization options compared to Superset.

  4. Collaboration and Sharing: Redash provides robust collaboration and sharing features, allowing users to collaborate on data analysis and share dashboards, queries, and reports with others. It allows for easy collaboration and sharing within teams and across organizations. Superset also offers collaboration and sharing capabilities, but it may require additional configurations and setups for efficient collaboration.

  5. Security and Access Control: Superset offers advanced security features and access control mechanisms. It provides role-based access control, allowing administrators to define granular access levels for different users and groups. Redash also provides access control features but may have a relatively simpler security model compared to Superset.

  6. Architecture and Scalability: Redash follows a microservice architecture that supports horizontal scalability. It offers better scalability options, allowing for higher performance and handling larger datasets. Superset follows a monolithic architecture, which may limit its scalability compared to Redash.

In summary, Redash and Superset differ in terms of data source support, ease of use, visualization options, collaboration and sharing capabilities, security and access control, and architecture and scalability. Each tool has its strengths and focuses, and the choice between them depends on specific requirements and preferences.

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Detailed Comparison

Redash
Redash
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.

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
Stacks
338
Stacks
420
Followers
502
Followers
1.0K
Votes
12
Votes
45
Pros & Cons
Pros
  • 9
    Open Source
  • 3
    SQL Friendly
Cons
  • 1
    All results are loaded into RAM before displaying
  • 1
    Memory Leaks
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
    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
No integrations available

What are some alternatives to Redash, Superset?

Metabase

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.

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.

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