druid-datasets VS ApacheKafka

Apache NiFi supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. Nifi is very useful when data needs to be loaded from different sources. In this case, I will nifi to access the Github API as it is very easy to make repeated calls to a Http endpoint and get data from multiple pages. You can see what I did by downloading NiFi yourself and then adding my template from the Druid Datasets repo: https://github.com/implydata/druid-datasets/blob/main/githubstars/github_stars.xml

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.