What methods are used to implement multi-stage data or process processing?

Multistage data or process processing is usually implemented in the following ways:

• Pipelines — a sequence of steps where each stage processes data and passes the result further. Often used in CI/CD, ETL processes, data processing.

• Workflow orchestration — using tools like Apache Airflow, Jenkins, Argo Workflows, which manage dependencies and task scheduling.

• Microservices and event-driven architecture — each service is responsible for its stage, interaction occurs through events or messages (e.g., Kafka, RabbitMQ).

• Use of containers and Kubernetes — for isolation and scaling of individual processing stages.

• Batch processing and stream processing — for batch or stream data processing with separation into stages.

An example of a simple pipeline in bash:

cat data.txt | grep "error" | sort | uniq > errors_sorted.txt

Here, data passes through several stages: filtering, sorting, removing duplicates.