Sigma Computing vs Superset

Overview

Superset

Stacks420

Followers1.0K

Votes45

Sigma Computing

Stacks21

Followers27

Votes0

Sigma Computing vs Superset: What are the differences?

<Write Introduction here>

Data Sources: Sigma Computing has the ability to connect to a wider range of data sources including cloud-based and on-premise databases, while Superset is more limited in its data source connections.
Ease of Use: Sigma Computing focuses on providing a user-friendly interface with a drag-and-drop functionality for creating complex data visualizations, whereas Superset requires some technical expertise and knowledge of SQL for creating visualizations.
Collaboration Features: Sigma Computing offers collaboration features such as real-time sharing and commenting on dashboards, while Superset lacks these collaborative capabilities.
Pricing Model: Sigma Computing offers a subscription-based pricing model with tiered pricing based on usage and features, while Superset is open-source and free to use but may require additional resources for implementation and maintenance.
Security Features: Sigma Computing provides more robust security features such as granular access controls and data governance, while Superset has fewer security features and may require additional security configurations.
Support and Documentation: Sigma Computing offers dedicated customer support and comprehensive documentation for users, whereas Superset relies on community forums and user-generated content for support and information.

In Summary, Sigma Computing and Superset differ in data sources, ease of use, collaboration features, pricing model, security features, and support and documentation.

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Detailed Comparison

Superset	Sigma Computing
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 modern analytics built for the cloud. Trusted by data-first companies, it provides live access to cloud data warehouses using an intuitive spreadsheet interface that empowers business experts to ask more questions without writing a single line of code. With the full power of SQL, the cloud, and a familiar interface, business users have the freedom to analyze data in real time without limits.
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;	Ad Hoc Reports; Benchmarking; Dashboard; Key Performance Indicators ;Performance Metrics; Predictive Analytics; Visual Analytics; Embedded Dashboards; SQL runner; Self-service Analytics; Visual Data Modeling
Statistics
Stacks 420	Stacks 21
Followers 1.0K	Followers 27
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	Google BigQuery PostgreSQL Amazon Redshift Snowflake

What are some alternatives to Superset, Sigma Computing?

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: 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

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

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

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

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 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

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

Related Comparisons

Sigma Computing vs Superset: What are the differences?

<Write Introduction here>

Data Sources: Sigma Computing has the ability to connect to a wider range of data sources including cloud-based and on-premise databases, while Superset is more limited in its data source connections.
Ease of Use: Sigma Computing focuses on providing a user-friendly interface with a drag-and-drop functionality for creating complex data visualizations, whereas Superset requires some technical expertise and knowledge of SQL for creating visualizations.
Collaboration Features: Sigma Computing offers collaboration features such as real-time sharing and commenting on dashboards, while Superset lacks these collaborative capabilities.
Pricing Model: Sigma Computing offers a subscription-based pricing model with tiered pricing based on usage and features, while Superset is open-source and free to use but may require additional resources for implementation and maintenance.
Security Features: Sigma Computing provides more robust security features such as granular access controls and data governance, while Superset has fewer security features and may require additional security configurations.
Support and Documentation: Sigma Computing offers dedicated customer support and comprehensive documentation for users, whereas Superset relies on community forums and user-generated content for support and information.

In Summary, Sigma Computing and Superset differ in data sources, ease of use, collaboration features, pricing model, security features, and support and documentation.

Sigma Computing vs Superset

Overview