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 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."
"The management and analytics are valuable features."
"The scalability is pretty good."
"The solution works well in rather sizable environments."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"The solution's technical support is flawless."
"Great auto-scaling, auto-sharing, and auto-correction features."
"One of the best features of Amazon Kinesis is the multi-partition."
"The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark."
"It is fast, it's scalable, and it does the job it needs to do."
"We can scale the product."
"I haven't heard about any major stability issues. At this time I feel like it's stable."
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."
"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."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"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."
"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"Amazon Kinesis should improve its limits."
"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."
"I think the default settings are far too low."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"Costs can quickly add up if you don't plan for it."
"There are no direct connectors — they are very limited."
"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."
"Databricks could improve in some of its functionality."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
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.
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