[name] asked: tell us about your experience working with Kafka and Airflow.

In projects with Golang, I used Kafka to organize high-load message exchange systems. My main experience is related to writing producers and consumers in Go, using the official library confluent-kafka-go and sarama.

Working with Kafka included:

• Configuring topics with the necessary level of replication and partitioning.
• Ensuring guaranteed message delivery with acknowledgments (acks).
• Handling errors and retrying message sending.

Airflow was used for orchestrating ETL processes and data processing pipelines. Specifically, I wrote DAGs in Python that scheduled tasks, including calling microservices, loading data from Kafka into databases, and monitoring statuses.

An example of a simple DAG for running a message processing task from Kafka:

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def process_kafka_messages():
    # message processing logic
    pass

default_args = {
    'start_date': datetime(2023, 1, 1),
    'retries': 1
}

dag = DAG('kafka_processing', default_args=default_args, schedule_interval='@hourly')

process_task = PythonOperator(
    task_id='process_kafka',
    python_callable=process_kafka_messages,
    dag=dag
)

Thus, Kafka ensured reliable data transfer, and Airflow managed and monitored the processing processes.