We performed a comparison between Amazon MSK and Databricks based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It is a stable product."
"The most valuable feature of Amazon MSK is the integration."
"MSK has a private network that's an out-of-box feature."
"Overall, it is very cost-effective based on the workflow."
"Amazon MSK has significantly improved our organization by building seamless integration between systems."
"Amazon MSK has good integration because our team has been undergoing significant changes. Coupling it with MSK within AWS is helpful. We don't have to set up additionals or monitor external environments. This"
"It offers good stability."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
"The main features of the solution are efficiency."
"The processing capacity is tremendous in the database."
"The ability to stream data and the windowing feature are valuable."
"We can scale the product."
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"Easy to use and requires minimal coding and customizations."
"The simplicity of development is the most valuable feature."
"The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET."
"The configuration seems a little complex and the documentation on the product is not available."
"It does not autoscale. Because if you do keep it manually when you add a note to the cluster and then you register it, then it is scalable, but the fact that you have to go and do it, I think, makes it, again, a bit of some operational overhead when managing the cluster."
"It should be more flexible, integration-wise."
"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."
"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."
"The integration and query capabilities can be improved."
"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."
"It's not easy to use, and they need a better UI."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"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."
"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."
"In the next release, I would like to see more optimization features."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
Amazon MSK is ranked 6th in Streaming Analytics with 7 reviews while Databricks is ranked 2nd in Streaming Analytics with 78 reviews. Amazon MSK is rated 7.2, while Databricks is rated 8.2. The top reviewer of Amazon MSK writes "Streamlines our processes, and we don't need to configure any VPCs; it's automatic". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Amazon MSK is most compared with Confluent, Azure Stream Analytics, Amazon Kinesis, Google Cloud Dataflow and PubSub+ Event Broker, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio. See our Amazon MSK vs. Databricks report.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics 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.