StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Business Intelligence
  4. Business Intelligence
  5. Sisense vs Superset

Sisense vs Superset

OverviewComparisonAlternatives

Overview

Superset
Superset
Stacks420
Followers1.0K
Votes45
Sisense
Sisense
Stacks50
Followers66
Votes0

Sisense vs Superset: What are the differences?

<Write Introduction here>
  1. Deployment flexibility: Sisense offers both on-premise and cloud deployment options, providing flexibility for organizations with various infrastructure setups. In contrast, Superset mainly focuses on cloud deployment, making it ideal for organizations leveraging cloud-based solutions for their data analytics needs.

  2. Ease of use: Sisense is known for its user-friendly interface and intuitive drag-and-drop features, enabling non-technical users to create and analyze datasets easily. On the other hand, Superset is more developer-centric, requiring a higher level of technical expertise to utilize its full potential for data visualization and analysis.

  3. Data source connectivity: Sisense supports a wide range of data sources, including popular databases, cloud services, and business applications, making it suitable for organizations with diverse data storage solutions. In comparison, Superset has limited built-in connectors, relying on custom workarounds or community-provided plugins for integrating with various data sources.

  4. Advanced analytics capabilities: Sisense offers advanced analytics features like predictive modeling, machine learning integration, and AI-driven insights, empowering users to extract valuable business intelligence from their data. While Superset focuses more on traditional BI and data visualization capabilities, lacking some of the advanced analytics functionalities provided by Sisense.

  5. Scalability and performance: Sisense is designed to handle large volumes of data efficiently, with optimized performance and scalability for handling complex analytics workloads. In contrast, Superset may face limitations in handling massive datasets or real-time data processing, especially in high-demand environments requiring robust performance and scalability.

  6. Commercial support and maintenance: Sisense provides comprehensive commercial support and maintenance services, including technical assistance, training, and regular updates to ensure the platform's reliability and performance. On the other hand, Superset, being an open-source project, relies on community support and contributions, lacking dedicated commercial support options for organizations requiring professional assistance and maintenance.

In Summary, Sisense and Superset differ in deployment flexibility, ease of use, data source connectivity, advanced analytics capabilities, scalability and performance, and commercial support and maintenance options.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Superset
Superset
Sisense
Sisense

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.

It is making business intelligence (BI) analytics easy with its simple drag-and-drop and scalable end-to-end BI processes that help to prepare, analyze, and visualize multiple complex datasets quickly.

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;
Complex business queries without programming or SQL writing; Removes limitations to data size; Intuitive 'script-less' user interface; Consolidates, stores and accumulates data; Data mash-ups; Can be set up and used entirely in-house; Handles data at scale on a single commodity server.
Statistics
Stacks
420
Stacks
50
Followers
1.0K
Followers
66
Votes
45
Votes
0
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
No community feedback yet
Integrations
No integrations available
AtScale
AtScale
Vertica
Vertica
Dremio
Dremio
DataRobot
DataRobot
Trifacta
Trifacta
SAP HANA
SAP HANA

What are some alternatives to Superset, Sisense?

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.

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.

Related Comparisons

Postman
Swagger UI

Postman vs Swagger UI

Mapbox
Google Maps

Google Maps vs Mapbox

Mapbox
Leaflet

Leaflet vs Mapbox vs OpenLayers

Twilio SendGrid
Mailgun

Mailgun vs Mandrill vs SendGrid

Runscope
Postman

Paw vs Postman vs Runscope