Data Studio vs Superset

Overview

Superset

Stacks420

Followers1.0K

Votes45

Data Studio

Stacks365

Followers304

Votes0

Data Studio vs Superset: What are the differences?

Introduction In this article, we will explore and compare the key differences between Data Studio and Superset, two popular data visualization and analysis tools.

User Interface and Ease of Use: Data Studio has a user-friendly and intuitive interface that allows users to create and customize reports and dashboards without the need for coding. On the other hand, Superset provides a more technical interface that requires some knowledge of SQL and coding to create visualizations and perform analysis.
Data Source Connectivity: Data Studio offers a wide range of connectors to various data sources, including Google Sheets, Google Analytics, and BigQuery, making it easy to connect and visualize data from different platforms. Superset also provides connectivity to different databases and data sources, but the setup and configuration process may require more technical expertise.
Customization and Flexibility: Data Studio provides a limited number of chart types and customization options compared to Superset. While Data Studio offers basic customization features like changing colors and fonts, Superset allows users to create custom charts and visualizations using Python and JavaScript.
Collaboration and Sharing: Data Studio allows users to collaborate and share reports and dashboards with others by granting access permissions and generating shareable links. Superset also supports collaboration and sharing, but it offers more advanced features like creating and managing user groups and roles, allowing for more granular control over access and permissions.
Advanced Analysis Capabilities: Superset offers more advanced data exploration and analysis capabilities compared to Data Studio. With Superset, users can write and execute complex SQL queries, create interactive filters and drill-downs, and perform ad-hoc analysis on the fly. Data Studio, on the other hand, focuses more on data visualization and reporting rather than advanced analysis.
Community and Support: Data Studio has a larger user community and is supported by Google, offering extensive documentation, tutorials, and forums for users to seek help and share knowledge. Superset, being an open-source project, also has an active community, but the level of support may depend on the community's engagement and contributions.

In summary, Data Studio offers a user-friendly interface and seamless integration with Google services, while Superset provides more customization options, advanced analysis capabilities, and flexibility at the cost of requiring more technical knowledge.

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CLI (Node.js)

Manual

Detailed Comparison

Superset	Data Studio
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.	Unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions. It’s easy and free.
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;	Easily access a wide variety of data. Data Studio’s built-in and partner connectors makes it possible to connect to virtually any kind of data; Turn your data into compelling stories of data visualization art. Quickly build interactive reports and dashboards with Data Studio’s web based reporting tools; Share your reports and dashboards with individuals, teams, or the world. Collaborate in real time. Embed your report on any web page
Statistics
Stacks 420	Stacks 365
Followers 1.0K	Followers 304
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 Analytics Google Cloud Storage Google BigQuery Google Search Console Google Ads AdRoll Google Sheets Google Campaign Manager

What are some alternatives to Superset, Data Studio?

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

Data Studio vs Superset: What are the differences?

Introduction In this article, we will explore and compare the key differences between Data Studio and Superset, two popular data visualization and analysis tools.

User Interface and Ease of Use: Data Studio has a user-friendly and intuitive interface that allows users to create and customize reports and dashboards without the need for coding. On the other hand, Superset provides a more technical interface that requires some knowledge of SQL and coding to create visualizations and perform analysis.
Data Source Connectivity: Data Studio offers a wide range of connectors to various data sources, including Google Sheets, Google Analytics, and BigQuery, making it easy to connect and visualize data from different platforms. Superset also provides connectivity to different databases and data sources, but the setup and configuration process may require more technical expertise.
Customization and Flexibility: Data Studio provides a limited number of chart types and customization options compared to Superset. While Data Studio offers basic customization features like changing colors and fonts, Superset allows users to create custom charts and visualizations using Python and JavaScript.
Collaboration and Sharing: Data Studio allows users to collaborate and share reports and dashboards with others by granting access permissions and generating shareable links. Superset also supports collaboration and sharing, but it offers more advanced features like creating and managing user groups and roles, allowing for more granular control over access and permissions.
Advanced Analysis Capabilities: Superset offers more advanced data exploration and analysis capabilities compared to Data Studio. With Superset, users can write and execute complex SQL queries, create interactive filters and drill-downs, and perform ad-hoc analysis on the fly. Data Studio, on the other hand, focuses more on data visualization and reporting rather than advanced analysis.
Community and Support: Data Studio has a larger user community and is supported by Google, offering extensive documentation, tutorials, and forums for users to seek help and share knowledge. Superset, being an open-source project, also has an active community, but the level of support may depend on the community's engagement and contributions.

Data Studio vs Superset

Overview