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."Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."
"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."
"The solution's technical support is flawless."
"Everything is hosted and simple."
"The most valuable feature of Amazon Kinesis is real-time data streaming."
"One of the best features of Amazon Kinesis is the multi-partition."
"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."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"Its lightweight and fast processing are valuable."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."
"There are good features for turning off clusters."
"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."
"The integration with Python and the notebooks really helps."
"We can scale the product."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"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."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"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."
"Lacks first in, first out queuing."
"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."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"We were charged high costs for the solution’s enhanced fan-out feature."
"I would love an integration in my desktop IDE. For now, I have to code on their webpage."
"CI/CD needs additional leverage and support."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"Scalability is an area with certain shortcomings. The solution's scalability needs improvement."
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
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.
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