Databricks vs Domino Data Science Platform comparison

Databricks and Domino Data Lab are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.3, while Domino Data Lab is ranked #15 with an average rating of 8.0. Databricks holds a 15.3% mindshare in Data Science Platforms, compared to Domino Data Lab’s 2.7% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Domino Data Lab users who would recommend it.

Databricks

Read 91 Databricks reviews

23,965 Views
10,784 Comparison Views

96% willing to recommend

Domino Data Science Platform

Read 2 Domino Data Science Platform reviews

1,514 Views
1,484 Comparison Views

100% willing to recommend

Databricks

Domino Data Science Platform

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Databricks and Domino Data Science Platform are competing products aimed at enhancing data science capabilities for enterprises. Databricks has an edge in scalability and integration with big data ecosystems, while Domino excels in data science management features.

Features: Databricks offers a Spark-based platform, strong data processing capabilities, and automated machine learning. Domino provides complete model management, collaboration tools, and support for various data science workflows. Domino's focus on model lifecycle management provides an advantage for end-to-end projects.

Ease of Deployment and Customer Service: Databricks offers cloud-native deployment and strong customer support, focusing on integration challenges. Domino allows flexible deployment, including on-premises options, and offers personalized customer service for smooth transitions. Organizational needs will dictate the choice between scalability and tailored setup.

Pricing and ROI: Databricks uses variable pricing based on usage, which can lead to cost efficiency and high ROI for large-scale implementations. Domino, although potentially higher in initial cost, offers substantial ROI through operational efficiency in collaborative projects. Financial decisions depend on budget and expected returns from each platform's offerings.

To learn more, read our detailed Databricks vs. Domino Data Science Platform Report (Updated: July 2025).

Buyer's Guide

Databricks vs. Domino Data Science Platform

July 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 (8th), Streaming Analytics (1st)

Domino Data Science Platform

Ranking in Data Science Platforms

15th

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Data Science Platforms category, the mindshare of Databricks is 15.3%, down from 19.8% compared to the previous year. The mindshare of Domino Data Science Platform is 2.7%, down from 2.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Databricks	15.3%
Domino Data Science Platform	2.7%
Other	82.0%

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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AHMEDSAHRAOUI

Machine Learning Engineer at Unemployed

Accelerated machine learning model development with seamless deployment

We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they…

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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

"The load distribution capabilities are good, and you can perform data processing tasks very quickly."

"It is fast, it's scalable, and it does the job it needs to do."

"The ability to stream data and the windowing feature are valuable."

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

"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"

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

"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."

"Databricks serves as a single platform for conducting the entire end-to-end lifecycle of machine learning models or AI ops."

More Databricks pros

"The scalability of the solution is good; I'd rate it four out of five."

"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."

Cons

"In the future, I would like to see Data Lake support. That is something that I'm looking forward to."

"I believe that this product could be improved by becoming more user-friendly."

"The product could be improved regarding the delay when switching to higher-performing virtual machines compared to other platforms."

"The product needs samples and templates to help invite users to see results and understand what the product can do."

"The initial setup of Databricks could be complex."

"I have seen better user interfaces, so that is something that can be improved."

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

"Costs can quickly add up if you don't plan for it."

More Databricks cons

"The predictive analysis feature needs improvement."

"The deployment of large language models (LLMs) could be improved."

Pricing and Cost Advice

"I would rate Databricks' pricing seven out of ten."

"The basic version of this solution is now open-source, so there are no license costs involved. However, there is a charge for any advanced functionality and this can be quite expensive."

"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."

"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."

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

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

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

"The price of Databricks is reasonable compared to other solutions."

More Databricks pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Financial Services Firm

39%

Manufacturing Company

10%

Insurance Company

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 ...

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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 needs improvement with Domino Data Science Platform?

The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that cannot handle big deployments, which is not suitable for LLMs.

See all answers

What is your primary use case for Domino Data Science Platform?

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What advice do you have for others considering Domino Data Science Platform?

It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying solely on data scientists might not be sufficient. I'd rate the solution eight out...

See all answers

Comparisons

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Dremio vs Databricks

Compared 6% of the time

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Compared 5% of the time

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Compared 24% of the time

Amazon SageMaker vs Domino Data Science Platform

Compared 22% of the time

Alteryx vs Domino Data Science Platform

Compared 8% of the time

KNIME Business Hub vs Domino Data Science Platform

Compared 7% of the time

Anaconda Business vs Domino Data Science Platform

Compared 5% of the time

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Product Reports

Buyer's Guide

Databricks

August 2025

Download Databricks product report

Buyer's Guide

Data Science Platforms

August 2025

Download Domino Data Science Platform product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Domino Data Lab Platform

Interactive Demo

Demo not available

Databricks

Domino Data Lab

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

Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.

Domino Data Lab

Sample Customers

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

Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life

Buyer's Guide

Databricks vs. Domino Data Science Platform

July 2025

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