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."Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds, thousands of processes in every source with very low latency."
"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."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"The most valuable feature of Amazon Kinesis is real-time data streaming."
"The solution's technical support is flawless."
"The integration capabilities of the product are good."
"I have worked in companies that build tools in-house. They face scaling challenges."
"Everything is hosted and simple."
"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."
"I like cloud scalability and data access for any type of user."
"It can send out large data amounts."
"The ease of use and its accessibility are valuable."
"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"Could include features that make it easier to scale."
"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"We were charged high costs for the solution’s enhanced fan-out feature."
"The price is not much cheaper. So, there is room for improvement in the pricing."
"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."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"The integration of data could be a bit better."
"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."
"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."
"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."
"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."
"We'd like a more visual dashboard for analysis It needs better UI."
"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."
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, Confluent, Amazon MSK, Apache Flink and Apache Pulsar, 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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