Anonymous UserSenior Software Engineer at a tech services company
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"Everything is hosted and simple."
"Amazon Kinesis also provides us with plenty of flexibility."
"Amazon Kinesis has improved our 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."
"The most valuable feature is that it has a pretty robust way of capturing things."
"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."
"Great auto-scaling, auto-sharing, and auto-correction features."
"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 solution is better than average and some of the valuable features include efficiency and stability."
"Could include features that make it easier to scale."
"I think the default settings are far too low."
"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."
"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."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"Lacks first in, first out queuing."
"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."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"Under $1,000 per month."
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.
Amazon Kinesis is ranked 3rd in Streaming Analytics with 9 reviews while Apache Spark Streaming is ranked 10th in Streaming Analytics with 1 review. Amazon Kinesis is rated 8.4, while Apache Spark Streaming is rated 6.0. The top reviewer of Amazon Kinesis writes "Easily replay your streaming data with this reliable solution". On the other hand, the top reviewer of Apache Spark Streaming writes "Efficient, better then average, but overly developer-focused ". Amazon Kinesis is most compared with Apache Flink, Amazon MSK, Google Cloud Dataflow, Confluent and Spring Cloud Data Flow, whereas Apache Spark Streaming is most compared with Azure Stream Analytics, Spring Cloud Data Flow, Talend Data Streams, Databricks and IBM Streams.
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.