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.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
Returns depend on the application you deploy and the amount of benefits you are getting, which depends on how many applications you are deploying, what are the sorts of applications, and what are the requirements.
We receive prompt support from AWS solution architects or TAMs.
I was getting prompt responses, and it was nicely handled regarding the support.
I would rate them eight if 10 was the best and one was the worst.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
I would rate the scalability of Amazon Kinesis as a nine.
According to me, it is quite scalable in terms of all the data it can handle and stream.
I would rate the stability of Amazon Kinesis as high, giving it a 10.
There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
Regarding additional improvements, I would say probably around error handling, where when we encounter errors specific to our response structures and everything, or the tables or anything of that nature, it would be better if we were prompted with better error handling mechanisms.
If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it.
Observability and monitoring are areas that could be enhanced.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
There was back and forth with the team regarding the cost, but overall, I feel it would have been easier to handle if there were a mechanism that let us know when the credits were being fully utilized or when the limits were getting crossed.
Amazon Kinesis integrates easily with the AWS environment.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
These features are important due to scalability and resiliency.
This helped a lot with regard to log monitoring and many other streaming use cases where we were able to get all the data in one place and stream it at a quicker rate.
The Kafka Streams API helps with real-time data transformations and aggregations.
| Product | Mindshare (%) |
|---|---|
| Amazon Kinesis | 4.5% |
| Apache Kafka on Confluent Cloud | 0.7% |
| Other | 94.8% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 10 |
| Large Enterprise | 10 |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 3 |
| Large Enterprise | 8 |
Amazon Kinesis provides real-time data streaming with seamless AWS integration, ideal for analytics, data transformation, and external customer feeds. It offers cost-effective data management with high throughput and low latency, supporting multiple programming languages.
Amazon Kinesis enables organizations to manage real-time data streams efficiently. Its integration with AWS ensures seamless setup and operation, while features like auto-scaling and fault tolerance make it reliable for diverse data sources such as IoT devices and server logs. The platform's ability to handle large-scale event-driven systems and dynamic workloads makes it suitable for complex streaming architectures. Despite some challenges with costs and setup complexity, Kinesis remains a popular choice for its efficient data management and processing capabilities.
What are the key features of Amazon Kinesis?In industries such as IoT, finance, and entertainment, Amazon Kinesis facilitates the real-time ingestion and processing of data streams. It connects seamlessly to data lakes and warehouses, enabling businesses to harness data-driven insights without performance loss. This capability is essential for managing dynamic workloads and large-scale event systems. By supporting tools like KDS, Firehose, and Video Streams, Kinesis empowers organizations to respond quickly to changing data environments, enhancing operational effectiveness across different sectors.
Apache Kafka on Confluent Cloud provides real-time data streaming with seamless integration, enhanced scalability, and efficient data processing, recognized for its real-time architecture, ease of use, and reliable multi-cloud operations while effectively managing large data volumes.
Apache Kafka on Confluent Cloud is designed to handle large-scale data operations across different cloud environments. It supports real-time data streaming, crucial for applications in transaction processing, change data capture, microservices, and enterprise data movement. Users benefit from features like schema registry and error handling, which ensure efficient and reliable operations. While the platform offers extensive connector support and reduced maintenance, there are areas requiring improvement, including better data analysis features, PyTRAN CDC integration, and cost-effective access to premium connectors. Migrating with Kubernetes and managing message states are areas for development as well. Despite these challenges, it remains a robust option for organizations seeking to distribute data effectively for analytics and real-time systems across industries like retail and finance.
What are the key features of Apache Kafka on Confluent Cloud?In industries like retail and finance, Apache Kafka on Confluent Cloud is implemented to manage real-time location tracking, event-driven systems, and enterprise-level data distribution. It aids in operations that require robust data streaming, such as CDC, log processing, and analytics data distribution, providing a significant edge in data management and operational efficiency.
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