Confluent and Amazon Kinesis are competing in the stream processing domain. Confluent has an edge in specific areas, while Amazon Kinesis attracts users with its comprehensive features despite the cost.
Features: Confluent is known for seamless integration with Apache Kafka, strong event streaming capabilities, and scalability. Users mention the ease of real-time data processing. Amazon Kinesis stands out with an extensive feature set including real-time analytics, easy scaling, and broad AWS service integration. Kinesis' deep integration with AWS services provides a significant advantage.
Room for Improvement: Confluent users highlight the need for better documentation, improved customer support, and a more flexible pricing model. Amazon Kinesis users suggest enhancing tooling support, simplifying configuration, and providing better cost transparency. Confluent's documentation challenges are noted more frequently.
Ease of Deployment and Customer Service: Confluent is often preferred for straightforward deployment and responsive customer support. Amazon Kinesis, while more complex to deploy, benefits from strong AWS ecosystem support. Users find Confluent easier for initial setup, while Kinesis users value the AWS support network.
Pricing and ROI: Confluent is noted for a relatively high initial setup cost but provides predictable ROI with consistent performance. Amazon Kinesis is seen as costly due to AWS cloud pricing but offers scalability that justifies the investment for many. Pricing is a key differentiator, with Kinesis appealing to those leveraging full AWS integration.
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.
Confluent is an enterprise-ready, full-scale streaming platform that enhances Apache Kafka.
Confluent has integrated cutting-edge features that are designed to enhance these tasks:
Confluent is a more complete distribution of Kafka in that it enhances the integration possibilities of Kafka by introducing tools for managing and optimizing Kafka clusters while providing methods for making sure the streams are secure. Confluent supports publish-and-subscribe as well as the storing and processing of data within the streams. Kafka is easier to operate and build thanks to Confluent.
Confluent's software is available in three different varieties:
Confluent Advantage Features
Confluent has many valuable key features. Some of the most useful ones include:
Reviews from Real Users
Confluent stands out among its competitors for a number of reasons. Two major ones are its robust enterprise support and its open source option. PeerSpot users take note of the advantages of these features in their reviews:
Ravi B., a solutions architect at a tech services company, writes of the solution, “KSQL is a valuable feature, as is the Kafka Connect framework for connecting to the various source systems where you need not write the code. We get great support from Confluent because we're using the enterprise version and whenever there's a problem, they support us with fine-tuning and finding the root cause.”
Amit S., an IT consultant, notes, “The biggest benefit is that it is open source. You have the flexibility of opting or not opting for enterprise support, even though the tool itself is open source.” He adds, “The second benefit is it's very modern and built on Java and Scala. You can extend the features very well, and it doesn't take a lot of effort to do so.”
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