Google Cloud Datalab vs Starburst Enterprise comparison

Google and Starburst are both solutions in the Data Science Platforms category. Google is ranked #16 with an average rating of 9.0, while Starburst is ranked #14 with an average rating of 8.5. Google holds a 1.8% mindshare in Data Science Platforms, compared to Starburst’s 1.7% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

1,794 Views
1,130 Comparison Views

80% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

1,946 Views
1,460 Comparison Views

100% willing to recommend

Google Cloud Datalab

Starburst Enterprise

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Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and Starburst Enterprise are competing in the data analytics domain. Google Cloud Datalab impresses with its integration in Google Cloud services, while Starburst Enterprise stands out due to its powerful features and deployment options for complex analytics needs.

Features: Google Cloud Datalab offers integration with Google Cloud, focusing on machine learning and collaborative analysis. Starburst Enterprise provides the ability to query data across diverse sources using Presto, with robust performance in data-intensive environments. The main differences are in data source versatility and machine learning capabilities.

Ease of Deployment and Customer Service: Google Cloud Datalab has a seamless setup within Google Cloud, supported by documentation and integrated support tools. Starburst Enterprise provides flexible deployment options, including on-premises, cloud, and hybrid solutions, with dedicated customer service. The key distinction is the broader deployment range and personalized support of Starburst Enterprise.

Pricing and ROI: Google Cloud Datalab typically offers a lower setup cost, appealing to users invested in Google Cloud, with ROI through optimized costs. Starburst Enterprise may have higher initial costs but delivers ROI through its efficiency in complex queries and compatibility with existing infrastructures. The differentiation lies in initial investment versus long-term value.

To learn more, read our detailed Google Cloud Datalab vs. Starburst Enterprise Report (Updated: April 2026).

Buyer's Guide

Google Cloud Datalab vs. Starburst Enterprise

April 2026

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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

Google Cloud Datalab

Ranking in Data Science Platforms

16th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (15th)

Starburst Enterprise

Ranking in Data Science Platforms

14th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Streaming Analytics (17th)

Mindshare comparison

As of May 2026, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 1.8%, up from 0.9% compared to the previous year. The mindshare of Starburst Enterprise is 1.7%, down from 2.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Starburst Enterprise	1.7%
Google Cloud Datalab	1.8%
Other	96.5%

Data Science Platforms

Featured Reviews

LijomonJose

System Architect at UST Global EspaÃ±a

dashboards are good and data visualization is more meaningful for the end-user

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 forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

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KamleshPant

Senior Software Architect at USEReady

Connects to any data source from any region and offers unified access

There are no specific projects supported by Starburst regarding AI initiatives or machine learning projects. In the future, if we have all the data available, we can definitely capitalize on AI/ML and LLM capabilities to summarize data and gain insights. That's our future goal, but we haven't reached that point yet. There should be support for REST API data sources to access data from the web. We often have data coming in and communicate with data sources via REST API calls. I don't see that capability in Starburst currently; everything is through JDBC or ODBC. If Starburst could seamlessly access data using REST API capabilities, it would be a game-changer. The self-service data management features, like self-service materialized views, are great, but they can be a bit complex for basic users to understand.

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

"Google Cloud Datalab is very customizable."

"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."

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

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

"The APIs are valuable."

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

"It's very scalable, fast performing, and supports many catalogs."

"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."

Cons

"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 product must be made more user-friendly."

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

"The interface should be 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."

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

"There should be support for REST API data sources to access data from the web."

"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."

Pricing and Cost Advice

"The product is cheap."

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

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

"I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of using Starburst Enterprise can vary based on the amount of data you're processing and the type of machines you opt for, whether on AWS or another cloud platform."

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Construction Company

16%

Comms Service Provider

Computer Software Company

Financial Services Firm

35%

Computer Software Company

Comms Service Provider

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What needs improvement with Google Cloud Datalab?

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

What advice do you have for others considering Google Cloud Datalab?

Overall, I would rate it a nine out of ten. Google Cloud is very good. Once you go through the features of Google Cloud, it's a good idea to get a GCP certification so you have an idea of how it ca...

See all answers

What is your experience regarding pricing and costs for Starburst Enterprise?

I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of us...

See all answers

What needs improvement with Starburst Enterprise?

See all answers

What is your primary use case for Starburst Enterprise?

We use Starburst with one client who is exploring their ecosystem to remove data silos and enable data access across departments. It's a very big ecosystem, like a finance institute. They are curre...

See all answers

Comparisons

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Compared 7% of the time

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Compared 18% of the time

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Compared 14% of the time

Dremio vs Starburst Enterprise

Compared 8% of the time

Apache Flink vs Starburst Enterprise

Compared 6% of the time

Dataiku vs Starburst Enterprise

Compared 5% of the time

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Product Reports

Buyer's Guide

Google Cloud Datalab

May 2026

Download Google Cloud Datalab product report

Buyer's Guide

Data Science Platforms

April 2026

Download Starburst Enterprise product report

Overview

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

Starburst Enterprise optimizes data processing for businesses, offering a robust platform tailored for efficient data handling. Ideal for tech-savvy audiences, it powers seamless data analysis and management.

Starburst Enterprise provides an advanced infrastructure that simplifies querying massive data sets from a variety of sources. Its integration capabilities allow users to access and analyze data without extensive data movement, ensuring cost-effective operations and speedy insights. Businesses can leverage comprehensive data analytics strategies, significantly enhancing their decision-making processes while minimizing latency.

What are the key features of Starburst Enterprise?

Data Federation: Connects disparate data sources without consolidation.
High-Performance Query: Delivers fast, interactive SQL analytics even on diverse data sets.
Security and Access Control: Ensures data is protected with robust security protocols and user management systems.
Scalability: Capable of handling increasing workloads efficiently.

What benefits and ROI should users look for?

Cost Efficiency: Reduces data warehousing costs by querying data at its source.
Time Savings: Accelerates data access and query responses, improving productivity.
Scalability: Supports growing data needs effortlessly, reducing infrastructure investments.

In industries like finance and retail, Starburst Enterprise is implemented to streamline big data operations, enhance customer experiences, and facilitate better risk management. Its ability to integrate with existing infrastructures allows for seamless adoption into company operations, delivering substantial analytical advantages.

Starburst

Buyer's Guide

Google Cloud Datalab vs. Starburst Enterprise

April 2026

Free Report: Google Cloud Datalab vs. Starburst Enterprise

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