postgres-operator VS ApacheKafka

yes, precisely. It's UI part that's broken, which cannot list snapshots. Issue is here, no fix since 2020, sadly: https://github.com/zalando/postgres-operator/issues/937

I personally like the Zalando operator better, you can add databases and users by updating the CRD, feature parity between the two on HA is pretty good

https://github.com/zalando/postgres-operator

one of the solutions which made it pretty simple for us to run postgresql in a ha environment (mostly in k8s, but works standalone as well) is zalandos patroni: https://github.com/zalando/patroni it's really solid and worked for us for a few years already.

or for k8s their operator: https://github.com/zalando/postgres-operator (docker image: https://github.com/zalando/spilo) we've also tried other operators which were easier to get started, but they failed miserably (crunchyrolls operator is basically based on the zalando one)

git clone https://github.com/zalando/postgres-operator.git cd postgres-operator

Zalando / Postgres-Operator

Somewhere between here and here i found out about that.

Here's an example of using the PostgreSQL Operator to deploy a high availability PostgreSQL cluster in Kubernetes. In this example, I'll be using the PostgreSQL Operator from Zalando [https://github.com/zalando/postgres-operator] to deploy a PostgreSQL cluster with two nodes.

Here's the operator for a postgres cluster: https://github.com/zalando/postgres-operator

And that repo you linked to has 1846 issues, 161 open. Which doesn't seem extraordinary based on my limited exposure to k8s.

Another example: https://github.com/zalando/postgres-operator/issues with 445 open issues. Why?

Maybe I'm wrong and this is all a good sign of progress, but my impression is that the entire k8s ecosystem is held together with reused duct tape.

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.