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 is that it has a pretty robust way of capturing things."
"The management and analytics are valuable features."
"The scalability is pretty good."
"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."
"I find almost all features valuable, especially the timing and fast pace movement."
"The solution's technical support is flawless."
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"Great auto-scaling, auto-sharing, and auto-correction features."
"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."
"Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user."
"Databricks integrates well with other solutions."
"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."
"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."
"Databricks helps crunch petabytes of data in a very short period of time."
"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"Lacks first in, first out queuing."
"Amazon Kinesis should improve its limits."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"I think the default settings are far too low."
"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."
"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"It would be beneficial if Amazon Kinesis provided document based support on the internet to be able to read the data from the Kinesis site."
"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."
"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"There should be better integration with other platforms."
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
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 Apache Pulsar, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio and Dremio. 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.