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."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."
"It offers good stability."
"The most valuable feature of Amazon MSK is the integration."
"It is a stable product."
"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."
"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"I work in the data science field and I found Databricks to be very useful."
"It's very simple to use Databricks Apache Spark."
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."
"The configuration seems a little complex and the documentation on the product is not available."
"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."
"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 would be really helpful if Amazon MSK could provide a single installation that covers all the servers."
"It should be more flexible, integration-wise."
"A lot of people are required to manage this solution."
"It should have more compatible and more advanced visualization and machine learning libraries."
"There should be better integration with other platforms."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."
"Databricks can improve by making the documentation better."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"There is room for improvement in visualization."
Amazon MSK is ranked 6th in Streaming Analytics with 6 reviews while Databricks is ranked 1st 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 "Efficient real-time transaction tracking but time-consuming installation". 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, Amazon Kinesis, Azure Stream Analytics, Google Cloud Dataflow and IBM Streams, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio. 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.