Compare Databricks vs. Domino Data Science Platform

Databricks is ranked 5th in Data Science Platforms with 6 reviews while Domino Data Science Platform is ranked 17th in Data Science Platforms with 1 review. Databricks is rated 8.6, while Domino Data Science Platform is rated 7.0. The top reviewer of Databricks writes "Good build-in optimization, easy to use with a good user interface". On the other hand, the top reviewer of Domino Data Science Platform writes "Good scalability and stability but the predictive analysis feature needs improvement". Databricks is most compared with Amazon SageMaker, Microsoft Azure Machine Learning Studio and Cloudera Data Science Workbench, whereas Domino Data Science Platform is most compared with Amazon SageMaker, Databricks and Dataiku Data Science Studio.
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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:

I work in the data science field and I found Databricks to be very useful.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.Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great.Automation with Databricks is very easy when using the API.We are completely satisfied with the ease of connecting to different sources of data or pocket files in the searchThe built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly.

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

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It would be very helpful if Databricks could integrate with platforms in addition to Azure.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.It should have more compatible and more advanced visualization and machine learning libraries.Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems.The integration features could be more interesting, more involved.The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment.

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The predictive analysis feature needs improvement.

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Pricing and Cost Advice
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.

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Also Known As
Databricks Unified Analytics, Databricks Unified Analytics PlatformDomino Data Lab Platform
Domino Data Lab
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Databricks creates a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. It utilizes Apache Spark to help clients with cloud-based big data processing. It puts Spark on “autopilot” to significantly reduce operational complexity and management cost. The Databricks I/O module (DBIO) improves the read and write performance of Apache Spark in the cloud. An increase in productivity is ensured through Databricks’ collaborative workplace.

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.

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Sample Customers
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, YeswareAllstate, Tesla, Dell, Moody's Analytics, SurveyMonkey, Eventbrite, Carnival
Top Industries
Software R&D Company41%
Comms Service Provider9%
Media Company9%
Insurance Company6%
Software R&D Company36%
Financial Services Firm12%
Real Estate/Law Firm7%
Manufacturing Company7%
Find out what your peers are saying about Alteryx, Knime, IBM and others in Data Science Platforms. Updated: December 2019.
391,932 professionals have used our research since 2012.
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