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."Great auto-scaling, auto-sharing, and auto-correction features."
"The most valuable feature is that it has a pretty robust way of capturing things."
"Amazon Kinesis also provides us with plenty of flexibility."
"One of the best features of Amazon Kinesis is the multi-partition."
"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."
"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."
"The most valuable feature of Amazon Kinesis is real-time data streaming."
"From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."
"I like cloud scalability and data access for any type of user."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"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."
"The processing capacity is tremendous in the database."
"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."
"The ability to stream data and the windowing feature are valuable."
"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 is a scalable solution. It is the largest advantage of the solution."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Could include features that make it easier to scale."
"Lacks first in, first out queuing."
"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."
"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."
"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."
"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."
"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."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"It should have more compatible and more advanced visualization and machine learning libraries."
"The integration of data could be a bit better."
"I would like more integration with SQL for using data in different workspaces."
"There are no direct connectors — they are very limited."
"Doesn't provide a lot of credits or trial options."
"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."
"The pricing of Databricks could be cheaper."
Amazon Kinesis is ranked 2nd in Streaming Analytics with 21 reviews while Databricks is ranked 1st 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, Apache Flink, Amazon MSK, Confluent and Apache Pulsar, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, 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.
