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  5. Cube.js vs Superset

Cube.js vs Superset

OverviewComparisonAlternatives

Overview

Superset
Superset
Stacks420
Followers1.0K
Votes45
Cube
Cube
Stacks97
Followers258
Votes30

Cube.js vs Superset: What are the differences?

Introduction

Cube.js and Superset are both popular tools for building analytical applications and visualizations. While they have similar goals, there are key differences between the two platforms.

  1. Architecture: Cube.js is built as a modern, open-source analytics framework that allows developers to create interactive analytical applications. It provides a query orchestration and caching layer that connects to multiple data sources and allows for efficient querying and data exploration. On the other hand, Superset is a data exploration and visualization platform that provides a user-friendly interface for creating and sharing interactive dashboards. It focuses on providing a wide range of visualizations and data exploration capabilities.

  2. Data Modeling: Cube.js follows a multidimensional model for data modeling, allowing for the creation of complex analytical models with hierarchies, dimensions, and measures. It provides a powerful query language (MDX) that supports advanced analytical calculations and aggregations. Superset, on the other hand, follows a relational data modeling approach and supports SQL as its query language. While it can handle complex calculations using SQL queries, it lacks some of the advanced analytical capabilities offered by Cube.js.

  3. Development Experience: Cube.js is designed to be an embeddable analytics solution that can be integrated into existing applications, providing flexibility and customization options for developers. It provides an API and SDKs for various programming languages, allowing developers to build their own analytical applications using Cube.js as a backend. Superset, on the other hand, is more focused on providing a user-friendly interface for non-technical users to create and explore data visualizations. It includes a drag-and-drop dashboard builder and interactive SQL query editor, making it easier for users to create and share dashboards without much technical expertise.

  4. Extensibility: Cube.js provides a plugin system that allows developers to extend and customize its functionality. It supports custom connectors to connect to different data sources, as well as customizing the UI and adding new features. Superset also supports extensibility through its plugin architecture, allowing developers to add new visualizations, databases, and authentication providers. However, compared to Cube.js, it has a more limited set of customization options.

  5. Community and Ecosystem: Cube.js has a growing community and ecosystem, with an active open-source community contributing to its development and maintenance. It has a number of pre-built connectors, visualization libraries, and third-party tools that can be used with Cube.js. Superset also has an active community and ecosystem, with a wide range of connectors and visualizations available. However, it has been around for a longer time and has a larger user base compared to Cube.js.

  6. Deployment Options: Cube.js can be deployed as a standalone server or as a serverless function on cloud platforms like AWS Lambda, Google Cloud Functions, or Azure Functions. It provides scalability and performance optimizations out of the box. Superset can also be deployed as a standalone server or as a Docker container, allowing for flexibility in deployment options. However, it may require more setup and configuration compared to Cube.js.

In summary, Cube.js is a modern, open-source analytics framework designed for developers to build interactive analytical applications with flexible data modeling and customization options. Superset, on the other hand, is a user-friendly data exploration and visualization platform focused on providing a wide range of visualizations and easy dashboard creation for non-technical users.

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Detailed Comparison

Superset
Superset
Cube
Cube

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.

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

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;
* Pre-aggregation; * Caching; * Data modeling; * APIs; * Works with any relational database;
Statistics
Stacks
420
Stacks
97
Followers
1.0K
Followers
258
Votes
45
Votes
30
Pros & Cons
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
    It is difficult to install on the server
  • 3
    Ugly GUI
Pros
  • 8
    API
  • 6
    Caching
  • 6
    Open Source
  • 6
    Visualization agnostic
  • 4
    Rollups orchestration
Cons
  • 1
    No ability to update "cubes" in runtime
  • 1
    Incomplete documentation
  • 1
    Doesn't support filtering on left joins
  • 1
    Poor performance
  • 1
    Cannot use as a lib - only HTTP
Integrations
No integrations available
Amazon Redshift
Amazon Redshift
Google BigQuery
Google BigQuery
Microsoft SQL Server
Microsoft SQL Server
Snowflake
Snowflake
Presto
Presto
MySQL
MySQL
PostgreSQL
PostgreSQL
Microsoft Azure
Microsoft Azure
Oracle
Oracle
Amazon Athena
Amazon Athena

What are some alternatives to Superset, Cube?

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.

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.

Redash

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

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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