pipeless VS ApacheKafka

After spending some months working on the Pipeless open-source framework, today I bring something new and really cool: Pipeless Agents

pipeless init my-project --template empty # Using the empty template we avoid the interactive shell cd my-project wget -O - https://github.com/pipeless-ai/pipeless/archive/main.tar.gz | tar -xz --strip=2 "pipeless-main/examples/yolo"

wget -O - https://github.com/pipeless-ai/pipeless/archive/main.tar.gz | tar -xz --strip=2 "pipeless-main/examples/onnx-yolo"

Check the live demo in the website

The code is available in the Pipeless GitHub repository: https://github.com/pipeless-ai/pipeless/tree/main/examples/yolo

Recently, a new alternative called Pipeless has appeared. Pipeless is an open-source framework that focuses on providing a great development experience and out-of-the-box performance. It offers a really easy stream management allowing you to add, edit, and remove streams on the fly as well as processing multiple streams. Furthermore, it offers you the possibility to deploy the applications either to the cloud or directly to embedded or edge devices.

You can find the repo here: https://github.com/pipeless-ai/pipeless

Pipeless is an open-source computer vision framework that ships everything you need to build and deploy computer vision applications really fast.

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.