Try our new research platform with insights from 80,000+ expert users

Databricks vs Domino Data Science Platform comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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
91
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
2
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
 

Featured Reviews

ShubhamSharma7 - PeerSpot reviewer
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.
AS
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…

Quotes from Members

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

Pros

"Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client."
"The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale."
"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."
"Databricks' most valuable feature is the data transformation through PySpark."
"The simplicity of development is the most valuable feature."
"We have the ability to scale, collaborate and do machine learning."
"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"Databricks is definitely a very stable product and reliable."
"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."
"The scalability of the solution is good; I'd rate it four out of five."
 

Cons

"There is room for improvement in visualization."
"It should have more compatible and more advanced visualization and machine learning libraries."
"CI/CD needs additional leverage and support."
"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."
"A lot of people are required to manage this solution."
"Databricks has a lack of debuggers, and it would be good to see more components."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."
"The deployment of large language models (LLMs) could be improved."
"The predictive analysis feature needs improvement."
 

Pricing and Cost Advice

"The cost is around $600,000 for 50 users."
"The billing of Databricks can be difficult and should improve."
"We only pay for the Azure compute behind the solution."
"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."
"The price of Databricks is reasonable compared to other solutions."
"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."
"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."
"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
Information not available
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
865,164 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
Financial Services Firm
39%
Manufacturing Company
10%
Insurance Company
8%
Computer Software Company
6%
 

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

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
Domino Data Lab Platform
 

Interactive Demo

Demo not available
 

Overview

 

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
Find out what your peers are saying about Databricks vs. Domino Data Science Platform and other solutions. Updated: July 2025.
865,164 professionals have used our research since 2012.