crewAI VS ApacheKafka

It may also be interesting to create an team of AI agents (the reporter, the analyst & decision provider) to help in decision making, for example by using crewAI.

CrewAI is a new multi-agent framework built on top of LangChain to simplify LLM-based Agent development. In this blog post, I'll guide you through the essentials of using CrewAI to streamline complex workflows with Python. From setting up AI agents to managing tasks, you'll learn how CrewAI's versatility can enhance efficiency in various domains. Whether you're new to AI or an intermediate Python user, get ready to unlock the potential of CrewAI in your projects.

actually agent frameworks are becoming very popular now

https://github.com/joaomdmoura/crewAI

Senden an Kafka (d. h. Senden der Daten an Apache Kafka)

Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.

Apache Kafka is a distributed streaming platform capable of handling high throughput of data, while ReductStore is a databases for unstructured data optimized for storing and querying along time.

*Push data *(original source image, GPS, timestamp) in a common place (Apache Kafka,...)

RabbitMQ comes with administrative tools to manage user permissions and broker security and is perfect for low latency message delivery and complex routing. In comparison, Apache Kafka architecture provides secure event streams with Transport Layer Security(TLS) and is best suited for big data use cases requiring the best throughput.

Apache Kafka is a distributed streaming platform to share data between applications and services in real-time.

also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.