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."
"It offers good stability."
"Amazon MSK has significantly improved our organization by building seamless integration between systems."
"Overall, it is very cost-effective based on the workflow."
"The most valuable feature of Amazon MSK is the integration."
"It is a stable product."
"It's great technology."
"The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark."
"Databricks integrates well with other solutions."
"I like cloud scalability and data access for any type of user."
"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."
"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."
"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."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"It should be more flexible, integration-wise."
"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 would be really helpful if Amazon MSK could provide a single installation that covers all the servers."
"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."
"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"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."
"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."
"It should have more compatible and more advanced visualization and machine learning libraries."
"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."
"Implementation of Databricks is still very code heavy."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
Amazon MSK is ranked 6th in Streaming Analytics with 6 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 "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 Spring Cloud Data Flow, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio and Dremio. See our Amazon MSK vs. Databricks report.
See our list of best Streaming Analytics vendors.
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