We performed a comparison between Databricks and KNIME 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 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."
"Ability to work collaboratively without having to worry about the infrastructure."
"The simplicity of development is the most valuable feature."
"The setup was straightforward."
"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."
"Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"Easy to use and requires minimal coding and customizations."
"We can deploy the solution in a cluster as well."
"The visual workflow tools for custom and complex tasks always beat raw coding languages with the agility, speed to deliver, and ease of subsequent changes."
"Automation is most valuable. It allows me to automatically download information from different sources, and once I create a workflow, I can apply it anytime I want. So, there is efficiency at the same time."
"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
"There are a lot of connectors available in KNIME."
"I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data."
"KNIME is easy to learn."
"KNIME is quite scalable, which is one of the most important features that we found."
"I would like it if Databricks made it easier to set up a project."
"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."
"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."
"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."
"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 solution has some scalability and integration limitations when consolidating legacy systems."
"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."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script."
"KNIME is not scalable."
"Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"From the point of view of the interface, they can do a little bit better."
"The data visualization part is the area most in need of improvement."
"One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools. The advertising and marketing efforts to reach out to companies and universities have not been very successful."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. Databricks is rated 8.2, while KNIME 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 KNIME writes "A low-code platform that reduces data mining time by linking script". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Amazon SageMaker. See our Databricks vs. KNIME report.
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