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 19.1% mindshare in Data Science Platforms, compared to Google’s 1.0% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Databricks

Read 85 Databricks reviews

17,588 Views
11,896 Comparison Views

96% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

850 Views
763 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: January 2025).

Buyer's Guide

Databricks vs. Google Cloud Datalab

January 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

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 (19th)

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Databricks is 19.1%, up from 18.5% compared to the previous year. The mindshare of Google Cloud Datalab is 1.0%, down from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

Integrating engineering and learning, but cost challenges arise with cluster management

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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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

"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

"Databricks helps crunch petabytes of data in a very short period of time."

"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."

"It helps integrate data science and machine learning capabilities."

"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."

"The solution is very easy to use."

"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."

More Databricks pros

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

"The APIs are valuable."

"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."

Cons

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

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

"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."

"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."

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

"I would love an integration in my desktop IDE. For now, I have to code on their webpage."

"There is room for improvement in visualization."

"The solution has some scalability and integration limitations when consolidating legacy systems."

More Databricks cons

"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."

"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."

Pricing and Cost Advice

"The solution is based on a licensing model."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."

"We're charged on what the data throughput is and also what the compute time is."

"We only pay for the Azure compute behind the solution."

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

"The solution is a good value for batch processing and huge workloads."

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

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

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Financial Services Firm

19%

Computer Software Company

14%

University

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...

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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...

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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

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Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 7% of the time

More Databricks Competitors

IBM SPSS Statistics vs Google Cloud Datalab

Compared 34% of the time

Cloudera Data Science Workbench vs Google Cloud Datalab

Compared 20% of the time

IBM SPSS Modeler vs Google Cloud Datalab

Compared 11% of the time

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Product Reports

Buyer's Guide

Databricks

January 2025

Download Databricks product report

Buyer's Guide

Google Cloud Datalab

January 2025

Download Google Cloud Datalab product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Learn More

Databricks

Google

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.

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.

Sample Customers

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

Information Not Available

Buyer's Guide

Databricks vs. Google Cloud Datalab

January 2025

Free Report: Databricks vs. Google Cloud Datalab

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