Our system needs to handle high-throughput and low-latency data streams from multiple sources, and provide real-time processing and analytics capabilities. We need to choose a suitable event-driven streaming platform that can handle this workload and integrate with other systems in our architecture.
We will use Apache Kafka as our event-driven streaming platform.
- Kafka has a proven track record of handling high-throughput data streams, with the ability to process millions of events per second.
- Kafka's distributed architecture allows it to scale horizontally and handle high levels of data fault-tolerance.
- Kafka has strong support for real-time processing and analytics through its support for stream processing frameworks such as Apache Storm and Apache Spark Streaming.
- Kafka's support for publish-subscribe messaging allows for easy integration with other systems in our architecture.
- Kafka has a large and active open-source community, providing a wealth of documentation and resources for development and troubleshooting.
- Kafka may have a steeper learning curve compared to other event-driven streaming platforms, and may require additional resources for setup and maintenance.
- Kafka's publish-subscribe messaging model may not be a perfect fit for all use cases, and may require additional work to integrate with systems that use different messaging models.
- Kafka's high-throughput and low-latency capabilities may put additional stress on other systems in our architecture that are not designed to handle high levels of traffic.
- Apache Storm, Apache Flink, and Apache Samza are alternative stream processing frameworks that have similar capabilities to Kafka.
- Apache Pulsar, RabbitMQ, and Google Cloud Pub/Sub are alternative messaging platforms that have different messaging models and may be better suited for specific use cases.
Date: 2021-09-01
Author: John Doe
- The system must be able to handle high-throughput data streams of millions of events per second.
- The system must be able to provide real-time processing and analytics capabilities.
- The system must be able to integrate easily with other systems in the architecture.
- The system must be able to scale horizontally and handle high levels of data fault-tolerance.