Apache Airflow
/tldr: The standard platform for programmatic authoring, scheduling, and monitoring of data workflows.
1. Core Concepts: DAGs and Operators
Airflow represents all workflows as Directed Acyclic Graphs (DAGs), where each node in the graph is a specific unit of work (Task).
DAG (Directed Acyclic Graph)
The workflow blueprint, defined in Python. It specifies the tasks and their dependencies but contains no logic itself.
- **Directed:** Flow is unidirectional (no loops).
- **Acyclic:** No task can depend on an upstream task that has already executed in the same run.
Operator & Task
An **Operator** is a pre-built template for a kind of work (e.g., execute SQL, run a Python function, wait for a file).
A **Task** is an *instantiation* of an Operator within a DAG (e.g., the specific instance of the SqlOperator used to run SELECT * FROM sales).
2. Cross-Communication (XComs)
Airflow tasks are designed to be independent (stateless). XComs (Cross-Communication) is the mechanism used to pass small amounts of metadata between tasks.
XComs Explained
- **Mechanism:** A key/value store that pushes data (usually metadata like file paths, row counts, status codes) to the Airflow Metadata Database.
- **Use Case:** A task (A) might pull data, get the final file path (
s3://data/processed/file.csv), push this path to an XCom, and then a downstream task (B) reads the XCom to know which file to load into the database. - **Limitation:** Due to database constraints, XComs are intended for **small data only** (e.g., strings, small dictionaries, or lists). Do not use them to pass large dataframes or files.
3. The Modern Approach (TaskFlow API)
Introduced in Airflow 2.0, the TaskFlow API simplifies DAG authoring by allowing standard Python functions to be easily defined as tasks, significantly improving code readability.
TaskFlow Benefits
- **Simplified Tasks:** The
@taskdecorator turns any Python function into an Airflow Task (a PythonOperator instance). - **Automatic XComs:** When one decorated function returns a value, and that value is passed as an argument to another decorated function, Airflow automatically manages the XCom push/pull behind the scenes.
- **Implicit Dependencies:** Dependencies can be defined naturally by passing the function objects around, making the code feel like standard Python orchestration.
Traditional Airflow required explicit task_id >> next_task_id syntax and manual XCom handling. TaskFlow makes it much cleaner.
4. Managed Services (Astronomer)
While Apache Airflow is open source, managing a highly available, scalable instance is complex. Services like Astronomer and AWS MWAA/GCP Composer handle the infrastructure complexity.
Astronomer (Astro)
- **Managed Control Plane:** Provides a fully managed platform (Astro) for deploying and scaling Airflow instances (deployments).
- **Enterprise Features:** Adds value with enhanced features like:
- Centralized Logging & Monitoring
- Advanced CI/CD Pipelines for DAG testing and deployment
- Security and User Management (RBAC)
- **Key Benefit:** Allows data engineering teams to focus purely on DAG authoring rather than platform maintenance.
Airflow is the central nervous system for scheduling all data movement and transformations.