We performed a comparison between Databricks and IBM Watson Studio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."There are good features for turning off clusters."
"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."
"The solution is an impressive tool for data migration and integration."
"We have the ability to scale, collaborate and do machine learning."
"It's easy to increase performance as required."
"The main features of the solution are efficiency."
"I like cloud scalability and data access for any type of user."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"The system's ability to take a look at data, segment it and then use that data very differently."
"It has a lot of data connectors, which is extremely helpful."
"It is a very stable and reliable solution."
"The solution is very easy to use."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"The scalability of IBM Watson Studio is great."
"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"The product should provide more advanced features in future releases."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"The connectivity with various BI tools could be improved, specifically the performance and real time integration."
"The initial setup is difficult."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"I would like it if Databricks made it easier to set up a project."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."
"The initial setup was complex."
"I think maybe the support is an area where it lacks."
"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"The decision making in their decision making feature is less good than other options."
"We would like to see it more web-based with more functionality."
"The main challenge lies in visibility and ease of use."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews. Databricks is rated 8.2, while IBM Watson Studio is rated 8.2. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas IBM Watson Studio is most compared with Azure OpenAI, Microsoft Azure Machine Learning Studio, Google Vertex AI, Amazon Comprehend and Anaconda. See our Databricks vs. IBM Watson Studio report.
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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.