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."It is fast, it's scalable, and it does the job it needs to do."
"I work in the data science field and I found Databricks to be very useful."
"Easy to use and requires minimal coding and customizations."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"The solution is very simple and stable."
"I like cloud scalability and data access for any type of user."
"The setup was straightforward."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"The most valuable is the ability to seamlessly connect operators without the need for extensive programming."
"Easy to use, stable, and powerful."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"It is a stable solution...It is a scalable solution."
"We leverage KNIME flexibility in order to query data from our database and manipulate them for any ad-hoc business case, before presenting results to stakeholders."
"We are able to automate several functions which were done manually. I can integrate several data sets quickly and easily, to support analytics."
"KNIME is quite scalable, which is one of the most important features that we found."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"The product should provide more advanced features in future releases."
"I would like it if Databricks made it easier to set up a project."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"Databricks has a lack of debuggers, and it would be good to see more components."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."
"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."
"There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger."
"KNIME could improve when it comes to large data markets."
"The documentation is lacking and it could be better."
"The solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
"It could input more data acquisitions from other sources and it is difficult to combine with Python."
"If they had a more structured training model it would be very helpful."
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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