We performed a comparison between Databricks and IBM SPSS Modeler 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 most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"The initial setup is pretty easy."
"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."
"It will scale up to anything we need."
"It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"
"The supervised models are valuable. It is also very organized and easy to use."
"The quality is very good."
"It works fine. I have not had any stability issues; it is always up."
"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."
"Automated modelling, classification, or clustering are very useful."
"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."
"Scalability is an area with certain shortcomings. The solution's scalability needs improvement."
"Implementation of Databricks is still very code heavy."
"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"There should be better integration with other platforms."
"Can be improved by including drag-and-drop features."
"Anyone who doesn't know SQL may find the product difficult to work with."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."
"When you are not using the product, such as during the pandemic where we had worldwide lockdowns, you still have to pay for the licensing."
"Dimension reduction should be classified separately."
"The standard package (personal) is not supported for database connection."
"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."
"The integration with sources and visualisation needs some improvement. The scalability needs improvement."
"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."
"It would be good if IBM added help resources to the interface."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews. Databricks is rated 8.2, while IBM SPSS Modeler 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 Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio, whereas IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, IBM SPSS Statistics, RapidMiner and Dataiku Data Science Studio. See our Databricks vs. IBM SPSS Modeler report.
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