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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
88
Ranking in other categories
Cloud Data Warehouse (7th), 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 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 Domino Data Science Platform is 2.5%, down from 2.7% 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' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."
"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"It is fast, it's scalable, and it does the job it needs to do."
"Automation with Databricks is very easy when using the API."
"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."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."
"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

"Databricks can improve by making the documentation better."
"It would be great if Databricks could integrate all the cloud platforms."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"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."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"The predictive analysis feature needs improvement."
"The deployment of large language models (LLMs) could be improved."
 

Pricing and Cost Advice

"I'm not involved in the financing, but I can say that the solution seemed reasonably priced compared to the competitors. Similar products are usually in the same price range. With five being affordable and one being expensive, I would rate Databricks a four out of five."
"I would rate Databricks' pricing seven out of ten."
"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."
"Databricks are not costly when compared with other solutions' prices."
"Databricks uses a price-per-use model, where you can use as much compute as you need."
"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."
"Price-wise, I would rate Databricks a three out of five."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
11%
Manufacturing Company
9%
Healthcare Company
6%
Financial Services Firm
35%
Manufacturing Company
11%
Insurance Company
10%
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: March 2025.
845,040 professionals have used our research since 2012.