Managing SLAs in Apache Airflow DAGs

In modern data engineering, meeting data delivery deadlines is critical for business operations. This course guides you through the foundational concepts of Service Level Agreements (SLAs) within Apache Airflow, showing you how to prevent pipeline delays before they impact your stakeholders. You will transition from understanding basic scheduling to actively monitoring and enforcing strict timing constraints on your workflows. Through clear explanations and practical code examples, you will learn how to configure alerts and handle SLA misses effectively. What you'll learn: - Understand core SLA concepts and terminology in data pipeline management - Configure SLA parameters for individual tasks and entire DAGs - Implement SLA miss callback functions to automate alerts and notifications - Apply modern Airflow best practices to optimize task execution times - Troubleshoot common reasons for SLA failures in production environments This course starts with fundamental definitions of SLAs and pipeline latency before moving into hands-on configuration patterns and alerting strategies. It is designed for beginner data engineers and system administrators who want to build more resilient data workflows. No prior experience with SLA configuration is required, though a basic familiarity with Python and Airflow DAGs is helpful. Start mastering your data pipeline deadlines today.