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 8.3, while Google is ranked #16 with an average rating of 7.8. Databricks holds a 18.2% mindshare in Data Science Platforms, compared to Google’s 0.9% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Databricks

Read 89 Databricks reviews

14,742 Views
10,545 Comparison Views

96% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

711 Views
612 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: April 2025).

Buyer's Guide

Databricks vs. Google Cloud Datalab

April 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 (7th), Streaming Analytics (1st)

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 (18th)

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Databricks is 18.2%, down from 19.1% compared to the previous year. The mindshare of Google Cloud Datalab is 0.9%, down from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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.

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Quotes from Members

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

Pros

"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."

"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."

"Databricks integrates well with other solutions."

"Databricks offers various courses that I can use, whether it's PySpark, Scala, or R."

"Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client."

"Automation with Databricks is very easy when using the API."

"The setup was straightforward."

"The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient."

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."

"The APIs are valuable."

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

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

"Google Cloud Datalab is very customizable."

Cons

"It would be great if Databricks could integrate all the cloud platforms."

"Databricks' technical support takes a while to respond and could be improved."

"While Databricks is generally a robust solution, I have noticed a limitation with debugging in the Delta Live Table, which could be improved."

"The tool should improve its integration with other products."

"Would be helpful to have additional licensing options."

"They release patches that sometimes break our code. These patches are supposed to fix issues, but sometimes they cause disruptions."

"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."

"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."

More Databricks cons

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

"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."

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

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

"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."

Pricing and Cost Advice

"The pricing depends on the usage itself."

"I rate the price of Databricks as eight out of ten."

"I would rate the tool’s pricing an eight out of ten."

"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."

"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."

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

"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."

"The billing of Databricks can be difficult and should improve."

More Databricks pricing and cost advice

"The product is cheap."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Financial Services Firm

21%

University

12%

Computer Software Company

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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 ...

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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...

See all answers

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...

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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

Dataiku vs Databricks

Compared 11% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

More Databricks Competitors

IBM SPSS Statistics vs Google Cloud Datalab

Compared 46% of the time

Cloudera Data Science Workbench vs Google Cloud Datalab

Compared 24% of the time

More Google Cloud Datalab Competitors

Product Reports

Buyer's Guide

Databricks

April 2025

Download Databricks product report

Buyer's Guide

Google Cloud Datalab

April 2025

Download Google Cloud Datalab product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Overview

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

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

April 2025

Free Report: Databricks vs. Google Cloud Datalab

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