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В кластер входят документы, описывающие функционал, структуру и применение Apache Airflow для управления потоками данных, и не входят документы по иным инструментам оркестрации.
Общие признаки: оркестрация конвейеров данных, Python-инструментарий, управление DAG, автоматизация ETL
Группа выше: Конвейеры и обработка больших данных
Смысл: The text serves as a comprehensive practical guide to Apache Airflow, demonstrating how to replace rigid, expensive GUI-based ETL tools with a flexible, code-driven orchestration system to manage complex data pipelines efficiently.
A comprehensive technical guide on using Apache Airflow to build scalable, code-driven ETL pipelines, featuring real-world examples, Docker setup, and custom operator development.
Смысл: The text introduces Apache Airflow as a powerful Python-based tool for orchestrating complex batch data processing pipelines, highlighting its flexibility and scalability through real-world usage examples.
An overview of Apache Airflow's core concepts—DAGs, Operators, and Scheduling—demonstrating how Python-based orchestration improves the flexibility and scalability of ETL pipelines.
Смысл: The text explains the fundamental concepts of Apache Airflow DAGs, focusing on their structure as directed acyclic graphs and the evolution of scheduling mechanisms from simple cron intervals to highly customizable Python-based Timetables.
An in-depth technical guide on defining DAGs in Apache Airflow and implementing flexible scheduling using traditional methods and custom Python Timetables.