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."I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."
"It can send out large data amounts."
"The solution is an impressive tool for data migration and integration."
"I haven't heard about any major stability issues. At this time I feel like it's stable."
"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."
"Easy to use and requires minimal coding and customizations."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"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."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
"We have been able to appreciate the considerable reduction in prototyping time."
"I've never had any problems with stability."
"Clear view of the data at every step of ETL process enables changing the flow as needed."
"This solution is easy to use and it can be used to create any kind of model."
"Easy to use, stable, and powerful."
"Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present."
"CI/CD needs additional leverage and support."
"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 is room for improvement in visualization."
"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."
"Implementation of Databricks is still very code heavy."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"From the point of view of the interface, they can do a little bit better."
"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."
"It could be easier to use."
"System resource usage. Knime will occupy total system RAM size and other applications will hang."
"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 pricing needs improvement."
"There should be better documentation and the steps should be easier."
"The documentation needs a proper rework. "
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 Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku Data Science Studio and Amazon SageMaker. See our Databricks vs. KNIME report.
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