pgxtutorial VS ApacheKafka

# Clone my repository with any of the following commands: $ gh repo clone henvic/pgxtutorial $ git clone https://github.com/henvic/pgxtutorial.git $ git clone [email protected]:henvic/pgxtutorial.git # then: $ cd pgxtutorial # Create a database $ psql -c "CREATE DATABASE pgxtutorial;" # Set the environment variable PGDATABASE $ export PGDATABASE=pgxtutorial # Run migrations $ tern migrate -m ./migrations # Run all tests passing INTEGRATION_TESTDB explicitly $ INTEGRATION_TESTDB=true go test -v ./... # Execute application $ go run ./cmd/pgxtutorial 2021/11/22 07:21:21 HTTP server listening at localhost:8080 2021/11/22 07:21:21 gRPC server listening at 127.0.0.1:8082

oh, I also have defined database.PGX (https://github.com/henvic/pgxtutorial/blob/main/internal/dat...), which is a little more scarier. I'll get back about that too, but this one is perhaps even more useless, and there might be a better way to solve the issue I want to try to avoid with it.

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.