We performed a comparison between Amazon Kinesis 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."The most valuable feature of Amazon Kinesis is real-time data streaming."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"The management and analytics are valuable features."
"Everything is hosted and simple."
"What I like about Amazon Kinesis is that it's very effective for small businesses. It's a well-managed solution with excellent reporting. Amazon Kinesis is also easy to use, and even a novice developer can work with it, versus Apache Kafka, which requires expertise."
"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."
"I find almost all features valuable, especially the timing and fast pace movement."
"The solution works well in rather sizable environments."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"It's very simple to use Databricks Apache Spark."
"It is fast, it's scalable, and it does the job it needs to do."
"The integration with Python and the notebooks really helps."
"Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily."
"I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well."
"Databricks helps crunch petabytes of data in a very short period of time."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"We were charged high costs for the solution’s enhanced fan-out feature."
"Snapshot from the the from the the stream of the data analytic I have already on the cloud, do a snapshot to not to make great or to get the data out size of the web service. But to stop the process and restart a few weeks later when I have more data or more available of the client teams."
"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."
"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"I think the default settings are far too low."
"Lacks first in, first out queuing."
"There are no direct connectors — they are very limited."
"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."
"There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable."
"The integration of data could be a bit better."
"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."
"CI/CD needs additional leverage and support."
"I would like more integration with SQL for using data in different workspaces."
"There is room for improvement in visualization."
Amazon Kinesis is ranked 1st in Streaming Analytics with 24 reviews while Databricks is ranked 2nd in Streaming Analytics with 78 reviews. Amazon Kinesis is rated 8.0, while Databricks is rated 8.2. The top reviewer of Amazon Kinesis writes "Used for media streaming and live-streaming data". 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 Kinesis is most compared with Azure Stream Analytics, Amazon MSK, Confluent, Apache Flink and Spring Cloud Data Flow, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Microsoft Azure Machine Learning Studio. See our Amazon Kinesis 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.