Google Cloud Datalab Reviews

Name: Google Cloud Datalab
Brand: Google
Rating: 3.9 (6 reviews)

3.9 out of 5

6 reviews
80% willing to recommend

What is Google Cloud Datalab?

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.

Get the Google Cloud Datalab Buyer's Guide and find out what your peers are saying about Google Cloud Datalab, Databricks, Tableau and more!

Google Cloud Datalab is the #16 ranked solution in top Data Science Platforms and #19 ranked solution in top Data Visualization solutions. PeerSpot users give Google Cloud Datalab an average rating of 7.8 out of 10. Google Cloud Datalab is most commonly compared to Databricks: Google Cloud Datalab vs Databricks. Google Cloud Datalab is popular among the large enterprise segment, accounting for 72% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 22% of all views.

Helped 859,687 peers since 2012

Featured Google Cloud Datalab reviews

Nilesh Gode

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

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

TuPhan

Data Engineer at a tech consulting company with 201-500 employees

Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience. The short session timeout is disruptive, especially across different time zones, and improving session management would contribute to a smoother development process. Enhancements in the integration with BigQuery are essential, focusing on addressing performance and usability issues, especially with large datasets. On a broader scale, enriching the UI settings within the Google Cloud Console for Datalab is crucial. This includes implementing features for unified settings in environments and facilitating easier navigation and management for end-users.

Read full review

LijomonJose

System Architect at UST Global EspaÃ±a

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.

Read full review

Google Cloud Datalab mindshare

Product category:

As of July 2025, the mindshare of Google Cloud Datalab in the Data Visualization category stands at 0.7%, up from 0.4% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Visualization

PeerResearch reports based on Google Cloud Datalab reviews

Type	Title	Date
Category	Data Visualization	Jul 2, 2025	Download
Product	Reviews, tips, and advice from real users	Jul 2, 2025	Download
Comparison	Google Cloud Datalab vs Tableau	Jul 2, 2025	Download
Comparison	Google Cloud Datalab vs Splunk Cloud Platform	Jul 2, 2025	Download
Comparison	Google Cloud Datalab vs Splunk Enterprise Platform	Jul 2, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	N/A	96%	91 interviews Add to research
Tableau	4.2	22.9%	89%	306 interviews Add to research

Valuable Features

Google Cloud Datalab offers valuable features like a free call-up feature, robust infrastructure, and environment. It's seamless in setting up development libraries, customizable, with valuable APIs and dashboards. Users benefit from efficient data visualization and meaningful insights. AI features like auto-completion and data management are included, though they require careful configuration. The platform supports easy data handling in MLOps, aiding in designing data pipelines and incorporating various components, while also enhancing the customization of architecture.

"For me, it has been a stable product."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."

Room for Improvement

Google Cloud Datalab requires a more user-friendly interface and easier security setup. Connectivity issues, integration challenges with BigQuery, and a disruptive short session timeout affect usability. Improving the interface for easier navigation and management is crucial. Users face difficulties with data control during transitions due to geographic and time zone complexities. The system needs more efficient data processing and should simplify accessing big data without complex SQL queries. Enhanced experience in varied network conditions is also necessary.

"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."

Pricing

"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."

Popular Use Cases

Primary uses of Google Cloud Datalab include managing personnel and provisioning machine learning models into Vertex AI. It aids in data science projects, analyzing datasets, conducting Exploratory Data Analysis, and identifying machine-learning model features. Workloads are transferred from AWS and Univision, enhancing data quality and ensuring data processing aligns with customer needs. It's utilized for analytics, research, and daily data processing, executing batch jobs and automating data flow with scripts through Dataflow.

Service and Support

Customer service and support of Google Cloud Datalab receive mixed feedback. Some users appreciate the quick response and prioritization of issues, while others encounter frustration with AI-based support that lacks direct human interaction. Challenges in communication and specialized expertise are noted. Collaboration with different vendors like Microsoft and AWS highlights varying levels of support quality. As partners, they experience improved access to administrators, although initial usability issues were significant due to AI limitations.

Deployment

Users found Google Cloud Datalab's initial setup mostly easy, with some describing it as straightforward. Some faced challenges, especially when technical issues required restarting or reconfiguring, which was time-consuming. Dependency on cloud experts was noted when lacking expertise. Documentation and templates facilitated ease of use, and setup speed varied based on workflow efficiency. Guidance through documentation enabled many to complete the setup independently, despite it differing from other platforms.

Scalability

Google Cloud Datalab exhibits scalability features suitable for widespread use across varied regions. Its intuitive interface, rooted in open-source technology, appeals to global users in data-related fields. Usage patterns show regular access by significant numbers within organizations. Scaling can reach a considerable number of nodes but may require additional infrastructure for extensive scaling. Configurations involving microservices are necessary for optimal performance when handling large-scale operations.

Stability

Users have found Google Cloud Datalab to be stable for regular use, praising its simplicity. They rate its stability eight out of ten, appreciating the elimination of the need for extensive technical knowledge. While some note potential limitations with larger datasets affecting its performance, it is generally manageable as is. Current usage shows satisfaction without significant issues, ensuring positive sentiment concerning its ongoing efficacy. Benefits include the ease of management and technical accessibility.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Google Cloud Datalab Buyer's Guide for additional reliable information.