Apache Airflow Best Practices equips data professionals with the skills to master Airflow, from foundational concepts to advanced deployment strategies. This course is essential for those wanting to build scalable data pipelines, optimize workflows, and leverage Airflow in cloud environments.
Apache Airflow Best Practices
Apache Airflow Best Practices
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您将学到什么
Explore the new features and improvements in Apache Airflow 2.0
Design and build scalable data pipelines using DAGs
Implement ETL pipelines, ML workflows, and advanced orchestration strategies
您将获得的技能
- Devops Tools
- Data Pipelines
- Apache Airflow
- Cloud Deployment
- MLOps (Machine Learning Operations)
- Configuration Management
- Business Workflow Analysis
- Performance Tuning
- CI/CD
- Multi-Tenant Cloud Environments
- Workflow Management
- Continuous Deployment
- Python Programming
- System Monitoring
- Scalability
- DevOps
- 技能部分已折叠。显示 9 项技能，共 16 项。
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作业
13 项作业
授课语言：英语（English）
最近已更新！
December 2025
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该课程共有13个模块
In this section, we explore data orchestration fundamentals, Airflow 2.0 features, and best practices for building scalable pipeline solutions.
涵盖的内容
2个视频2篇阅读材料1个作业
In this section, we explore Apache Airflow's core concepts, including DAGs, task groups, and triggers, and how to implement them for efficient workflow automation and optimization.
涵盖的内容
1个视频4篇阅读材料1个作业
In this section, we explore Airflow components, their roles, and how to select and optimize executors for efficient workflow orchestration and scalability.
涵盖的内容
1个视频4篇阅读材料1个作业
In this section, we explore Airflow DAG authoring for API data extraction, focusing on task design with operators and workflow optimization for efficient data pipelines.
涵盖的内容
1个视频4篇阅读材料1个作业
In this section, we explore connecting Apache Airflow to external sources, designing DAGs with failure alerts, and managing secrets securely for efficient workflow automation.
涵盖的内容
1个视频4篇阅读材料1个作业
In this section, we cover creating Airflow UI plugins for custom workflow monitoring using Flask blueprints and metrics dashboards.
涵盖的内容
1个视频1篇阅读材料1个作业
In this section, we explore creating and distributing custom Airflow providers, focusing on structured packaging, testing, and reusable code for scalable workflow automation.
涵盖的内容
1个视频3篇阅读材料1个作业
In this section, we explore orchestrating machine learning workflows, focusing on DAG design, implementation, and MLOps practices for operational model deployment and performance analysis.
涵盖的内容
1个视频2篇阅读材料1个作业
In this section, we explore abstracting Airflow workflows to enable non-technical users to create and manage tasks. Key concepts include templated DAGs, workflow scheduling, and simplified orchestration for improved collaboration.
涵盖的内容
1个视频2篇阅读材料1个作业
In this section, we explore Airflow deployment strategies, DAG delivery patterns, and secure configuration management to optimize workflow efficiency and reliability.
涵盖的内容
1个视频4篇阅读材料1个作业
In this section, we explore monitoring strategies for Airflow systems and DAGs, focusing on core component health, DAG performance metrics, and alerting mechanisms for efficient workflow management.
涵盖的内容
1个视频2篇阅读材料1个作业
In this section, we explore strategies for implementing multi-tenancy in Airflow, focusing on isolation, operational requirements, and secure shared infrastructure management.
涵盖的内容
1个视频1篇阅读材料1个作业
In this section, we explore planning migration activities, implementing technical strategies, and executing pipeline changes with minimal downtime to ensure smooth Airflow transitions.
涵盖的内容
1个视频1篇阅读材料1个作业
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