BeanstalkD VS ApacheKafka

There's beanstalkd, it has a few Python libraries and it works out of the box with ActiveJob via Backburner.

https://beanstalkd.github.io/

Messaging/Queueing Systems (Amazon SQS, RabbitMQ, Beanstalkd)

Not when a queue is involved. IME trying to replicate something like beanstalkd (https://beanstalkd.github.io/) in postgres is asking for trouble for anything but trivial workloads.

If you're measuring throughput in jobs/s, use a real work queue.

Before Kafka was a standard, I created a go library for beanstalkd that act like an RPC.

Then there are queue libraries like beanstalkd, RabbitMQ or built-in features like queues from Laravel. These will probably get you quicker to your goal then trying the process managing route.

The only task queue I loved was beanstalkd -- it's beautifully written and highly performant. Starting it takes seconds and it's been running for a decade:

https://beanstalkd.github.io/

It's not rust (it's C), but beanstalkd is a pretty incredible work queue that processes millions of jobs a day (10K+/s at peak) for my company. I know there are a few rust drivers available.

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.