We performed a comparison between Databricks and IBM SPSS Statistics 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."The technical support is good."
"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."
"I like cloud scalability and data access for any type of user."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"Databricks is a scalable solution. It is the largest advantage of the solution."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."
"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."
"The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it."
"SPSS is quite robust and quicker in terms of providing you the output."
"The most valuable features are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial."
"Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things."
"In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."
"It offers very good visualization."
"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."
"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."
"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."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."
"Pricing is one of the things that could be improved."
"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people. I found that everything was very confusing at first. Having clear documentation would help alleviate that."
"Better documentation on how to use macros."
"The product should provide more ways to import data and export results that are user-friendly for high-level executives."
"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."
"The design of the experience can be improved."
"It could provide even more in the way of automation as there are many opportunities."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while IBM SPSS Statistics is ranked 8th in Data Science Platforms with 36 reviews. Databricks is rated 8.2, while IBM SPSS Statistics is rated 8.0. 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 SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio, whereas IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler and SAS Analytics. See our Databricks vs. IBM SPSS Statistics report.
See our list of best Data Science Platforms vendors.
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.