Databricks vs Google Cloud Datalab comparison

Databricks and Google are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.4, while Google is ranked #19 with an average rating of 7.8. Databricks holds a 12.3% mindshare in Data Science Platforms, compared to Google’s 1.1% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Databricks

Read 91 Databricks reviews

20,775 Views
8,933 Comparison Views

96% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

749 Views
584 Comparison Views

80% willing to recommend

Databricks

Google Cloud Datalab

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Executive SummaryUpdated on Dec 5, 2024

Databricks and Google Cloud Datalab compete in big data analytics. Databricks holds an advantage in pricing and support, while Google Cloud Datalab is noted for its expansive feature set.

Features: Databricks offers seamless integration with Apache Spark, advanced analytics tools, and effective collaboration options. Google Cloud Datalab integrates smoothly with Google Cloud services, simplifies machine learning model development, and provides strong data visualization capabilities.

Room for Improvement: Databricks could enhance user interface aesthetics, offer more extensive API documentation, and improve cross-platform compatibility. Google Cloud Datalab could benefit from optimized resource allocation, reducing initial setup complexities, and enhancing node configuration processes.

Ease of Deployment and Customer Service: Databricks offers flexible deployment and responsive customer service with easy integration on existing platforms. Google Cloud Datalab benefits from scalable deployment within Google Cloud's infrastructure, providing a streamlined setup.

Pricing and ROI: Databricks is cost-effective with a favorable ROI due to efficient resource use. Google Cloud Datalab has higher setup costs but delivers substantial ROI through integration with Google's ecosystem.

To learn more, read our detailed Databricks vs. Google Cloud Datalab Report (Updated: September 2025).

Buyer's Guide

Databricks vs. Google Cloud Datalab

September 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

Databricks

Ranking in Data Science Platforms

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (9th), Streaming Analytics (1st)

Google Cloud Datalab

Ranking in Data Science Platforms

19th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (18th)

Mindshare comparison

As of November 2025, in the Data Science Platforms category, the mindshare of Databricks is 12.3%, down from 19.0% compared to the previous year. The mindshare of Google Cloud Datalab is 1.1%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	12.3%
Google Cloud Datalab	1.1%
Other	86.6%

Data Science Platforms

Featured Reviews

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

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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

"We can scale the product."

"It offers AI functionalities that assist with code management and machine learning processes."

"Databricks has helped us have a good presence in data."

"I like cloud scalability and data access for any type of user."

"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."

"The time travel feature is the solution's most valuable aspect."

"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."

"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."

More Databricks pros

"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."

"Google Cloud Datalab is very customizable."

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

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

"The APIs are valuable."

Cons

"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."

"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."

"It should have more compatible and more advanced visualization and machine learning libraries."

"The product cannot be integrated with a popular coding IDE."

"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."

"My experience with the pricing and licensing model is that it remains relatively expensive. Though it's less expensive than AWS, we still need a more cost-effective solution."

"There is room for improvement in visualization."

"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."

More Databricks cons

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

"The interface should be more user-friendly."

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

"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."

"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."

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

Pricing and Cost Advice

"The price is okay. It's competitive."

"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."

"It is an expensive tool. The licensing model is a pay-as-you-go one."

"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."

"Databricks uses a price-per-use model, where you can use as much compute as you need."

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"The solution requires a subscription."

"Databricks are not costly when compared with other solutions' prices."

More Databricks pricing and cost advice

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

"The product is cheap."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

Manufacturing Company

Healthcare Company

Financial Services Firm

28%

University

Government

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	25
Midsize Enterprise	12
Large Enterprise	56

No data available

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

See all answers

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

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Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

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

Comparisons

Microsoft Power BI vs Databricks

Compared 9% of the time

Dataiku vs Databricks

Compared 7% of the time

Informatica PowerCenter vs Databricks

Compared 6% of the time

Dremio vs Databricks

Compared 5% of the time

Tableau Enterprise vs Databricks

Compared 4% of the time

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Anaconda Business vs Google Cloud Datalab

Compared 37% of the time

IBM SPSS Statistics vs Google Cloud Datalab

Compared 16% of the time

Oracle Analytics Cloud vs Google Cloud Datalab

Compared 12% of the time

KNIME Business Hub vs Google Cloud Datalab

Compared 11% of the time

SAS Enterprise Miner vs Google Cloud Datalab

Compared 9% of the time

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Product Reports

Buyer's Guide

Databricks

October 2025

Download Databricks product report

Buyer's Guide

Google Cloud Datalab

October 2025

Download Google Cloud Datalab product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Overview

Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

Databricks

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

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Information Not Available

Buyer's Guide

Databricks vs. Google Cloud Datalab

September 2025

Free Report: Databricks vs. Google Cloud Datalab

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