Introduction
Apache Airflow is a battle-tested open-source platform for authoring, scheduling, and monitoring workflows as directed acyclic graphs (DAGs), making it a cornerstone of data and ML infrastructure. Airflow allows teams to define dynamic workflows as Python code, manage dependencies explicitly, and execute tasks in a controlled, observable, and repeatable fashion. Airflow is used to orchestrate pipelines that span across batch jobs, external APIs, internal data platforms, and machine learning systems—ensuring workflows run on time, with clear failure handling and lineage tracking.
Key benefits of using Airflow include:
Code-as-Workflow: Workflows are defined as Python DAGs, enabling version control, modularity, and full integration with Python libraries and Cake-specific SDKs.
Flexible Scheduling and Triggering: Supports complex scheduling patterns, event-based triggering, and backfilling—ideal for both periodic and reactive jobs.
Operator and Sensor Ecosystem: Provides a rich ecosystem of operators and sensors for databases, cloud storage, APIs, file systems, and more—accelerating development of production-grade pipelines.
Built-In Observability and Retry Logic: Offers a powerful UI for visualizing DAGs, monitoring task status, retrying failures, and inspecting logs and metadata.
Scalable and Extensible: Easily deployable on Kubernetes, Celery, or ECS with support for horizontal scaling, custom plugins, and cross-DAG dependencies.
Airflow is used to coordinate workflows such as daily data ingestion, DBT transformations, model retraining and evaluation, RAG pipeline refreshes, and cross-team data integrations. It serves as a backbone for reproducible, traceable processes that power analytics, ML, and operations across the company. By adopting Airflow, you can ensure its workflows are well-structured, transparent, and production-ready—empowering teams to automate with confidence and scale data operations reliably.