cluster-operator VS ApacheKafka

Currently we run the SFS using podManagementPolicy: ordered, and have a list of 3 availability zones to start in, which starts pod 0 first in say us-east-1a, then pod 1 in us-east-1b, pod 2 in us-east-1c. This works fine however we've hit some scenarios where if there's multiple node restarts in multiple zones where rabbit pods are sitting the cluster can become unavailable and won't restart. As per this issue the fix is to run podManagementPolicy: parallel which will not require the lower number pods to be fully Ready before starting the higher.

RabbitMQ uses distributed deployment mechanism. You can set up instances in a highly available manner. Just like Memphis, RabbitMQ is cloud available. This allows you to run a highly available cluster on top of infrastructure such as kubernetes in the cloud.

kubectl apply -f https://github.com/rabbitmq/cluster-operator/releases/latest/download/cluster-operator.yml

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.