Apache Superset vs Google Cloud Datalab comparison

Apache and Google are both solutions in the Data Visualization category. Apache is ranked #2 with an average rating of 8.3, while Google is ranked #19 with an average rating of 7.8. Apache holds a 10.2% mindshare in DV, compared to Google’s 0.7% mindshare. Additionally, 100% of Apache users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Apache Superset

Read 12 Apache Superset reviews

3,309 Views
3,309 Comparison Views

100% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

740 Views
163 Comparison Views

80% willing to recommend

Apache Superset

Google Cloud Datalab

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 1, 2025

Google Cloud Datalab and Apache Superset compete in the data analysis tools category. Superset has the upper hand due to its robust feature set and visualization capabilities.

Features: Google Cloud Datalab provides seamless integration with Google Cloud services, Jupyter-style notebooks for data exploration, and advanced machine learning tools. Apache Superset offers a wide range of data visualization options, a SQL editor, and customizable dashboards.

Room for Improvement: Google Cloud Datalab could benefit from expanded visualization capabilities, improved integration beyond Google Cloud, and more flexible machine learning integrations. Apache Superset requires enhancements in deployment ease, better integration with external data sources, and advanced machine learning functionalities.

Ease of Deployment and Customer Service: Google Cloud Datalab has a straightforward deployment, especially for Google Cloud users, and reliable customer service from Google's infrastructure. Apache Superset involves a more complex setup but is supported by extensive community documentation and flexibility for diverse environments.

Pricing and ROI: Google Cloud Datalab is cost-effective for businesses already using Google Cloud, with lower setup costs and good ROI. Apache Superset, being open-source, presents an attractive upfront cost but may incur additional expenses for customization and maintenance, necessitating investment in expertise for advanced use.

To learn more, read our detailed Apache Superset vs. Google Cloud Datalab Report (Updated: July 2025).

Buyer's Guide

Apache Superset vs. Google Cloud Datalab

July 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Apache Superset

Ranking in Data Visualization

2nd

Average Rating

8.2

Reviews Sentiment

4.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Google Cloud Datalab

Ranking in Data Visualization

19th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Science Platforms (18th)

Mindshare comparison

As of August 2025, in the Data Visualization category, the mindshare of Apache Superset is 10.2%, down from 10.4% compared to the previous year. The mindshare of Google Cloud Datalab is 0.7%, up from 0.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Visualization

Featured Reviews

Md Ariful Islam

Application Engineer at Rakuten Symphony

Provides robust search capabilities with potential for additional features

The initial deployment of Apache Superset was easy. Its integration is very straightforward. I found that when I chose Elasticsearch, its installation, settings, and integration with my application were also very easy; I could implement everything in just two or three days. It took me only one day to deploy it for the first time because I encountered complexities with the version differences, between version one and version two of Muktopaath. When I upgraded my version, the previous version did not support my new server installation, making it necessary for me to find and implement my previous versions' plugins. Although I could resolve it, I found it easy, aside from the version problem that took additional time.

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Nilesh Gode

Project Manager - Data Science at Mettler-Toledo International Inc.

Easy to setup, stable and easy to design data pipelines

The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"What I appreciate the most about Apache Superset is that it's free and easy to set up."

"Apache Superset allows us to set up alerts; for instance, if the failure rate exceeds 1.5%, we receive an alert to address the issue with our integration partners."

"The benefits I have seen from using Apache Superset for the business team are that they have been able to look at the numbers easily, log into the portal, and look at different dashboards, which are at different granularities."

"It is a good visual solution tool in an open-source category."

"What I appreciate most about Apache Superset is its text search feature, including searching with small and capital letters."

"Being able to quickly build standard dashboards around relational data in Apache Superset provides tremendous visibility for everyone on the team."

"The solution supports a rich set of charts and enables users to create their own dashboards."

"The no-code interface is the most valuable as it allows us to operate without constant support from the data engineering team, fostering a self-service environment."

More Apache Superset pros

"For me, it has been a stable product."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

"Google Cloud Datalab is very customizable."

"The APIs are valuable."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"All of the features of this product are quite good."

Cons

"Dynamic dashboarding could improve to enable smooth navigation when transitioning from a higher to a lower view, allowing for easy accessibility."

"The platform's reporting feature needs enhancement."

"With Apache Superset, we had some problems with the permissions when we had too many users."

"Dark mode would be the main thing I would like; it does not really work because the chart text cannot be white in a dark mode setup, so it is not feasible."

"When comparing Apache Superset with Elasticsearch and Solar Search, it lacks some features that come with Elasticsearch, such as Kibana."

"Apache Superset could be improved by enhancing its interactivity and engagement capabilities."

"Building a full-fledged product or software as a service might be cumbersome due to performance limitations."

"Automation in terms of APIs for creating roles, and giving privileges to the user can be improved."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"The product must be made more user-friendly."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"The interface should be more user-friendly."

Pricing and Cost Advice

"The price of Apache Superset is less than some of its competitors."

"Apache Superset is an open-source solution."

"Apache Superset has a three-year licensing model."

"Apache Superset is open-source and free."

"It is affordable for us because we have a limited number of users."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"The product is cheap."

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

14%

Government

Healthcare Company

Financial Services Firm

30%

University

Government

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Apache Superset?

It is a good visual solution tool in an open-source category.

See all answers

What is your experience regarding pricing and costs for Apache Superset?

Apache Superset is an open-source solution.

See all answers

What needs improvement with Apache Superset?

Apache Superset needs more rich charting capabilities. Second, it could benefit from more real-time capabilities, such as real-time streams over WebSockets. Also, permissions-wise, it should be les...

See all answers

What do you like most about Google Cloud Datalab?

Google Cloud Datalab is very customizable.

See all answers

What needs improvement with Google Cloud Datalab?

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcin...

See all answers

What is your primary use case for Google Cloud Datalab?

It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...

See all answers

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Compared 7% of the time

Splunk Enterprise Platform vs Apache Superset

Compared 6% of the time

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Compared 32% of the time

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Compared 24% of the time

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Compared 13% of the time

Oracle Analytics Cloud vs Google Cloud Datalab

Compared 12% of the time

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Product Reports

Buyer's Guide

Apache Superset

August 2025

Download Apache Superset product report

Buyer's Guide

Google Cloud Datalab

August 2025

Download Google Cloud Datalab product report

Overview

Apache Superset provides seamless integration for data visualization and dashboard creation without the need for developer assistance. Its intuitive, no-code environment supports users to embed, query, and share data insights efficiently.

Apache Superset offers a robust platform for data visualization through easy dashboard configuration and data integration. It facilitates query writing and reuses KPIs to ensure data consistency across dashboards. Users can embed dashboards within applications effortlessly and leverage a wide range of chart options for sophisticated data representation. The self-service nature empowers teams to maintain data integrity and optimize processes swiftly. However, it seeks enhancement in documentation and dynamic dashboard navigation, with a need for more interactive features to rival industry-leading tools. Permissions management and interactivity need enhancement, especially in larger user environments.

What are the key features of Apache Superset?

Dashboard Configuration: Simplifies the process of setting up dashboards with minimal coding.
No-Code Environment: Enables dashboard creation without developer involvement.
Cross-Filters and Role Mapping: Allows dynamic filtering and user role mapping for tailored data access.
Embedded Analytics: Facilitates embedding of dashboards into applications.
Wide Chart Library: Offers diverse visualization options for comprehensive data display.

Why consider Apache Superset for your organization?

Data Consistency: Ensures unified data usage across dashboards.
Enhanced Insights: Provides insightful visualizations for better decision-making.
Process Optimization: Streamlines data processes for operational efficiency.
Self-Service Model: Empowers teams to manage data independently without IT intervention.

Industries utilize Apache Superset to create and integrate dashboards for data analysis and visualization. It is widely used in genomics to analyze data, monitor service performance in telecom, and manage metrics and KPIs. Companies leverage its capabilities for profitability insights, agent productivity assessments, and historical data trend analysis.

Apache

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

Google

Buyer's Guide

Apache Superset vs. Google Cloud Datalab

July 2025

Free Report: Apache Superset vs. Google Cloud Datalab

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