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Apache Airflow
Apache Airflow
Apache Airflow
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Apache Airflow
Original authorMaxime Beauchemin / Airbnb
DeveloperApache Software Foundation
Initial releaseJune 3, 2015; 10 years ago (2015-06-03)
Stable release3.0.2[1]Edit this on Wikidata (10 June 2025; 8 months ago (10 June 2025)) [±]
Written inPython
Operating systemLinux, macOS
TypeWorkflow management platform
LicenseApache License 2.0
Websiteairflow.apache.org
Repository

Apache Airflow is an open-source workflow management platform for data engineering pipelines. It started at Airbnb in October 2014[2] as a solution to manage the company's increasingly complex workflows. Creating Airflow allowed Airbnb to programmatically author and schedule their workflows and monitor them via the built-in Airflow user interface.[3][4] From the beginning, the project was made open source, becoming an Apache Incubator project in March 2016 and a top-level Apache Software Foundation project in January 2019.

Airflow is written in Python, and workflows are created via Python scripts. Airflow is designed under the principle of "configuration as code". While other "configuration as code" workflow platforms exist using markup languages like XML, using Python allows developers to import libraries and classes to help them create their workflows.

Overview

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Airflow uses directed acyclic graphs (DAGs) to manage workflow orchestration. Tasks and dependencies are defined in Python and then Airflow manages the scheduling and execution. DAGs can be run either on a defined schedule (e.g. hourly or daily) or based on external event triggers (e.g. a file appearing in Hive[5]). Previous DAG-based schedulers like Oozie and Azkaban tended to rely on multiple configuration files and file system trees to create a DAG, whereas in Airflow, DAGs can often be written in one Python file.[6]

Managed providers

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Three notable providers offer ancillary services around the core open source project.

  • Cloud Composer is a managed version of Airflow that runs on Google Cloud Platform (GCP) and integrates well with other GCP services.[7]
  • Amazon Web Services offers Managed Workflows for Apache Airflow starting from November 2020.[8]

References

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Apache Airflow

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Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. It allows users to programmatically author workflows as code in Python, representing them as directed acyclic graphs (DAGs) that define tasks and their dependencies. This extensible framework enables integration with various technologies and supports configurations ranging from single-machine setups to distributed systems. Originating at , Airflow was created in October 2014 by Maxime Beauchemin to manage complex data pipelines. It was open-sourced from its first commit and officially announced under Airbnb's GitHub repository in June 2015. In March 2016, the project joined the Apache Software Foundation's Incubator program, and it graduated to become an Apache Top-Level Project in January 2019. Since then, Airflow has seen significant releases, including version 3.0 in April 2025 and 3.1.3 in November 2025, which introduced major enhancements like improved scheduling and a refreshed . Key features of Airflow include its workflows-as-code approach, which leverages Python for dynamic DAG generation and Jinja templating for flexibility. The platform provides a web-based UI for visualizing, managing, and pipelines, along with rich scheduling capabilities, backfilling, and support for custom operators and hooks. Architecturally, Airflow consists of components like the scheduler, , metadata database, and webserver, enabling scalable orchestration of tasks such as ETL processes, operations, and business workflows. It is particularly suited for finite, scheduled batch jobs but complements other systems for event-driven or streaming use cases. Maintained by a global community of committers and contributors, Airflow fosters collaboration through resources like Slack channels, mailing lists, and contributor guidelines. Widely adopted for its version control-friendly design and extensibility, as of late 2024 it was used by over 77,000 organizations across industries and saw more than 31 million monthly downloads, powering at numerous organizations.

Introduction

Overview

Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows, where workflows are represented as directed acyclic graphs (DAGs). It allows users to programmatically author these workflows using Python code, providing a flexible framework to connect with various technologies and manage complex processes through a web-based user interface for visualization and debugging. Airflow is primarily used in to orchestrate ETL () pipelines, workflows, and general tasks, enabling efficient handling of at scale. For instance, it supports scheduling jobs like running Spark processes or transferring files in ML pipelines, making it a versatile tool for data-intensive operations. Key benefits of Airflow include its scalability to support configurations from single processes to distributed systems, extensibility through a rich ecosystem of plugins and integrations, and emphasis on code-based workflow definitions that facilitate , testing, and collaboration. Originally created by in October 2014 to manage internal data pipelines, it was open-sourced from its first commit and publicly announced in June 2015; it later transitioned to a top-level project in January 2019.

History

Apache Airflow originated in October 2014 when Maxime Beauchemin, a engineer at , developed it internally to overcome the limitations of cron-based scheduling for managing complex pipelines, which lacked robust dependency handling and monitoring capabilities. The was designed as a platform to author, schedule, and monitor workflows using Python code, addressing Airbnb's growing needs in a scalable manner. Airflow was open-sourced from its first commit and publicly announced in June 2015 via Airbnb's repository, quickly attracting community interest and contributions. By 2016, it had gained significant traction, with thousands of stars reflecting its appeal among data engineers for workflow orchestration. In March 2016, entered the Incubator to foster broader governance and community involvement under . It graduated to become a top-level project in January 2019, marking its maturity and independence from Airbnb's oversight. Key milestones include the release of 1.0 in June 2016, which introduced a stable and foundational features for production use. 2.0 followed in December 2020, delivering major enhancements such as a revamped , high-availability scheduler support, and the TaskFlow for simplified DAG authoring. The most recent major update, 3.0, was released on April 22, 2025, focusing on improved provider package management for better modularity and native async support through event-driven scheduling. Early development was driven primarily by , with substantial contributions from organizations like —which integrated Airflow into its Cloud Composer service—and , a key backer that has supported ongoing enhancements and community growth. By 2025, the project boasted over 3,000 contributors, underscoring its evolution into a collaborative open-source ecosystem.

Architecture

Core Components

Apache Airflow's core components in version 3.0 and later form a that enables secure and scalable of workflows defined as Directed Acyclic Graphs (DAGs). These components include the scheduler, DAG processor, , metadata database, server, triggerer, and worker processes, which collectively manage scheduling, parsing, execution, state tracking, asynchronous operations, and user interaction. The DAG processor is a standalone service responsible for parsing DAG files from the DAGs folder, serializing them, and storing the serialized versions in the metadata database. This separation improves performance and isolation by offloading parsing from the scheduler. The dag_processor_manager (part of the scheduler) logs lines for processed DAG files in the format: filename [num_dags] [num_errors] [processing_time]s. For example, the numbers "1 0 0.24s" mean: 1 is the number of DAGs successfully parsed from the file, 0 is the number of parsing errors encountered, and 0.24s is the time taken to process/parse the file in seconds. The scheduler serves as the central coordinator, using the serialized DAGs from the metadata database to monitor registered DAGs, resolve dependencies, and trigger task instances based on predefined schedules. It employs a heartbeat mechanism to periodically assess the state of DAG runs and tasks via the database, ensuring timely progression and handling events like failures or retries, while optimizing resource usage in production environments. The executor is the mechanism that handles the actual execution of tasks submitted by the scheduler, determining how and where the computational work occurs. 3.x supports various executor types, including the LocalExecutor for single-machine parallel execution using multiprocessing, the CeleryExecutor for distributing tasks across multiple workers via a message broker like or , the KubernetesExecutor for containerized execution, and the new Edge Executor for event-driven workflows. Parallelism is configurable through parameters such as parallelism and max_active_tasks_per_dag, which set global and per-DAG limits to prevent resource overload. At the heart of state management is the metadata database, a relational database that persistently stores essential information including serialized DAG definitions, task instance states, execution history, variables, connections, and XComs. Commonly implemented with or for their reliability and compliance, it features a structured with key tables such as dag_run for tracking workflow executions and task_instance for individual task metadata like start times, durations, and outcomes. The scheduler, server, and other core services synchronize via this database, but workers are restricted from direct access for enhanced . The server replaces the previous webserver, delivering the user-facing interface and endpoints using the framework with a modern React-based UI for visualizing DAG structures, monitoring run statuses, and manually triggering workflows. It exposes an enhanced REST v2 for programmatic interactions, allowing external tools to query metadata or submit tasks, while interfacing primarily with the database and handling worker communications through the Task Execution . This design enhances security, scalability, and maintainability in multi-user environments. The triggerer is a new component in Airflow 3.0 that manages asynchronous operations, such as deferrable operators and event-driven scheduling, by running Python functions outside the main task execution flow to improve responsiveness and resource efficiency. In distributed configurations, worker processes execute task code on behalf of the , operating as independent processes or containers that pull tasks from a queue and report results, logs, and status updates back to the server via the Task Execution , which then persists them to the metadata database and notifies the scheduler. For example, under CeleryExecutor, workers are managed by and can scale horizontally across machines, with task failures isolated to prevent cascading issues. This decoupled design supports high-throughput workloads by enhancing security and separating execution from monitoring duties.

Execution Model

Apache Airflow's execution model in version 3.x governs the runtime processing of workflows through a structured task lifecycle, where tasks transition through distinct states to ensure orderly and reliable execution. A task begins in the none state when its dependencies are unmet, moves to scheduled once dependencies are satisfied and it is ready for execution, enters queued upon assignment to an executor awaiting a worker slot, and reaches running while actively executing on a worker. Upon completion, it achieves success if no errors occur or failed if an error arises; in cases of failure with remaining attempts, it enters up_for_retry for rescheduling, while skipped applies to tasks bypassed via branching logic. New trigger rules like ALL_DONE_MIN_ONE_SUCCESS have been added for more flexible dependency management. Cross-task communication occurs via XComs, a lightweight mechanism for passing small, serializable data as key-value pairs between tasks, identified by keys like task_id and dag_id, with pushes and pulls handled through task instance methods and requiring task_ids for pulls. This maintains state without direct inter-task , with improved in deserialization. Dependency resolution relies on a topological sort of the graph to establish execution order, ensuring downstream tasks only proceed after upstream tasks complete successfully, with relationships defined via operators like >> for downstream dependencies. This sort dynamically determines the sequence, supporting triggers where upstream completion signals downstream readiness. For resilience, tasks support configurable retries on failure, specified via the retries parameter (e.g., up to three attempts), with an optional retry_exponential_backoff flag enabling progressive delay increases between retries using the tenacity library to mitigate transient issues, alongside a base retry_delay timedelta. Alerting integrates through notification callbacks, such as on_failure, using the BaseNotifier class to dispatch messages via provider hooks for channels like email (SMTP) or Slack, with new deadline alerts for proactive monitoring; note that SLAs have been removed. Parallelism is managed through executor-specific slot-based concurrency, where available slots limit simultaneous task runs to prevent overload, with the KubernetesExecutor enabling scalable, containerized execution by launching isolated pods per task for enhanced isolation and in distributed environments. Priority weights are capped by pool slots. Event-driven execution is facilitated by sensors, specialized operators that poll or wait for external conditions before succeeding and unblocking downstream tasks, such as the FileSensor monitoring for file arrival with configurable poke intervals or reschedule modes to balance resource use and responsiveness. The triggerer supports deferrable sensors for asynchronous waiting.

Core Concepts

Directed Acyclic Graphs (DAGs)

In Apache Airflow, a (DAG) serves as the foundational structure for defining workflows, encapsulating the schedule, tasks, dependencies, callbacks, and parameters of an entire process. It models tasks as nodes connected by directed edges representing dependencies, ensuring the graph remains acyclic to prevent infinite loops and guarantee finite execution. This design allows for complex, parallelizable workflows while maintaining a clear execution order based on . DAGs are authored in Python using the DAG class from the Airflow library, which requires essential parameters such as dag_id (a for the DAG), start_date (the date from which the DAG becomes active), and schedule (defining the periodicity, such as @daily or a cron expression like 0 0 * * *). As of Apache Airflow 3.0 (released April 2025), the schedule parameter unifies previous schedule_interval and timetable options. A basic DAG setup can be as simple as the following example:

python

from airflow.sdk import DAG from datetime import datetime with DAG( dag_id='my_dag', start_date=datetime(2021, 1, 1), schedule='@daily', catchup=False ): pass # Tasks would be defined here

from airflow.sdk import DAG from datetime import datetime with DAG( dag_id='my_dag', start_date=datetime(2021, 1, 1), schedule='@daily', catchup=False ): pass # Tasks would be defined here

This context manager approach organizes tasks within the DAG definition, enabling 's scheduler to parse and instantiate the workflow. For modern authoring in 3.0+, the @dag decorator from airflow.sdk provides a concise alternative to the DAG class for defining workflows directly as functions. Dependencies between tasks are modeled using the bitwise right-shift operator >> for sequential downstream relationships (e.g., task1 >> task2 ensures task2 runs only after task1 completes) and the left-shift operator << for upstream branching (e.g., task3 << [task1, task2] waits for both predecessors). For more dynamic structures, DAGs can be generated programmatically using loops or factory functions, such as iterating over a list of datasets to create parameterized tasks, which is useful for scalable, data-driven pipelines. Task implementation typically involves Airflow operators, as detailed in the Operators and Tasks section. To ensure robust workflows, best practices include designing tasks for idempotency, where re-execution yields the same result without side effects; versioning DAGs with Git for change tracking and rollback; and avoiding tight coupling by minimizing direct data passing between tasks in favor of external storage like XComs or databases. Additionally, handling data intervals—defined by the execution date and schedule—facilitates backfills by aligning task logic with logical rather than actual run times, preventing overlaps in historical data processing. Key limitations of DAGs include the prohibition of cycles, which would render the graph unschedulable, and the fixed structure determined at parse time, meaning runtime modifications to dependencies are not supported without re-parsing the DAG file.

Operators and Tasks

In Apache Airflow, tasks represent the fundamental units of execution within a directed acyclic graph (DAG), encapsulating specific actions or operations to be performed. Each task is an instance of an operator, which serves as a template defining the behavior of that unit of work. The abstract base class for all operators is BaseOperator, which provides core functionality such as dependency management and execution context. When defining a task via an operator, essential parameters include task_id for unique identification within the DAG, owner to specify the responsible user or team, and retries to configure the number of automatic retry attempts upon failure. These parameters ensure tasks are traceable, accountable, and resilient in workflow execution. For instance, a task might be instantiated with retries=3 to handle transient errors without manual intervention. Airflow provides several core operators through the standard providers package for common operations. The BashOperator (imported from airflow.providers.standard.operators.bash) executes shell commands on the host machine, taking a bash_command parameter to specify the script or command, such as running a data processing script via /bin/bash -c "echo 'Hello World'". The PythonOperator (imported from airflow.providers.standard.operators.python) invokes a Python callable function, accepting python_callable and optional op_args for passing arguments, enabling integration of custom Python logic like data transformations. However, for simple Python functions without Jinja templating, the @task decorator from airflow.sdk is recommended as a modern alternative in Airflow 3.0+. The SqlOperator performs SQL queries against a database using a sql parameter, supporting templating for dynamic queries, and relies on underlying hooks for connectivity. These operators allow developers to define straightforward tasks without extensive coding. Hooks provide reusable interfaces for connecting to external systems, abstracting authentication and connection details to keep pipelines secure and maintainable. They are typically used within operators to interact with services like databases or cloud storage. For example, PostgresHook facilitates connections to PostgreSQL databases using stored connection IDs, enabling operators to execute queries without embedding credentials in code. Similarly, S3Hook handles interactions with Amazon S3, such as uploading or downloading files, by leveraging predefined connections for access keys and endpoints. This design centralizes connection management in Airflow's metadata database. Developers can create custom operators by subclassing BaseOperator and implementing the execute method to define the task's logic, along with a custom __init__ for operator-specific parameters. This extensibility supports tailored integrations not covered by built-in options. A simple example is a HelloOperator that prints a greeting:

python

from airflow.sdk import DAG, BaseOperator from datetime import datetime class HelloOperator(BaseOperator): def __init__(self, name: str, **kwargs) -> None: super().__init__(**kwargs) self.name = name def execute(self, context): message = f"Hello {self.name}" print(message) return message with DAG(dag_id="hello_dag", start_date=datetime(2023, 1, 1)) as dag: hello_task = HelloOperator(task_id="hello", name="World")

from airflow.sdk import DAG, BaseOperator from datetime import datetime class HelloOperator(BaseOperator): def __init__(self, name: str, **kwargs) -> None: super().__init__(**kwargs) self.name = name def execute(self, context): message = f"Hello {self.name}" print(message) return message with DAG(dag_id="hello_dag", start_date=datetime(2023, 1, 1)) as dag: hello_task = HelloOperator(task_id="hello", name="World")

This operator can be instantiated in a DAG like any built-in one, demonstrating how custom logic integrates seamlessly. For an HTTP request scenario, one might extend it further to use libraries like requests in the execute method. Task groups enable logical organization of related tasks within a DAG, improving readability in the user interface without modifying dependencies or execution order. Defined using the @taskgroup decorator or TaskGroup class, they visually cluster tasks—such as a sequence of data ingestion steps—allowing complex workflows to be structured hierarchically while preserving the underlying graph structure. This feature is particularly useful for maintaining clarity in large-scale DAGs.

Features

Scheduling and Orchestration

Apache Airflow's scheduling mechanism relies on timetables to define when workflows execute, supporting both expressions for precise timing, such as "0 1 * * *" to run daily at , and timedelta objects for relative intervals, like timedelta(hours=1) for hourly runs. These timetables generate data intervals that represent the logical period of data processed in each DAG run, with data_interval_start marking the beginning of the interval (equivalent to the legacy execution_date) and data_interval_end indicating its close, ensuring workflows align with the intended data windows rather than actual execution timestamps. For instance, a daily schedule processes data from the prior day's start to end, promoting consistency in data pipeline logic. To handle historical data processing, Airflow provides backfilling capabilities, where the catchup=True parameter in a DAG definition automatically schedules and executes runs for all missed intervals from the start_date up to the current time upon activation, ideal for ensuring completeness in time-series workflows. In Airflow 3.0 (released April 2025), backfills are executed within the scheduler itself, providing improved control, scalability, and diagnostics compared to previous versions. If catchup is disabled (the default), only the most recent interval runs, avoiding overload on resource-intensive pipelines. Manual backfills can be initiated via the CLI command airflow dags backfill with specified start and end dates, allowing targeted re-execution of past periods, such as reprocessing a month's worth of ETL jobs, while options like --max-active-runs limit concurrency to prevent system strain. Workflow triggering in Airflow extends beyond time-based scheduling to include manual, external, and event-driven options for flexible . Manual triggers can be executed via the CLI with airflow dags trigger <dag_id>, optionally passing configuration or a logical date, or through external API calls to the REST endpoint /api/v1/dags/{dag_id}/dagRuns when the server is active. Since Airflow 2.4, dataset-based triggers enable event-driven execution, where DAGs depend on updates to defined assets like files (e.g., s3://[bucket](/page/Bucket)/data.csv), firing only after upstream tasks successfully update all required datasets since the last run, thus decoupling schedules from rigid timelines. Orchestration patterns in facilitate complex coordination, with the BranchPythonOperator allowing conditional branching by executing a Python callable that returns one or more task IDs to proceed, enabling dynamic paths based on runtime conditions like checks. For , sub-DAGs (via SubDagOperator) were historically used to encapsulate reusable task groups but were deprecated in Airflow 2.0 and removed in Airflow 3.0 in favor of TaskGroups, which provide hierarchical organization within a single DAG without the overhead of separate DAG instances, improving visualization and dependency management in the UI. Time zone handling in Airflow ensures global consistency by storing all datetimes in UTC internally and in the metadata database, with the default timezone set to UTC in airflow.cfg but configurable to any IANA timezone, such as Europe/Paris, to align schedules with regional needs while respecting daylight saving transitions in cron expressions. Deadline Alerts, introduced in Airflow 3.0 to replace the former Service Level Agreements (SLAs), allow defining expected maximum execution times via the deadline parameter on tasks or DAGs (e.g., deadline=timedelta(hours=2) relative to a reference like the DAG run's logical date). If the deadline is exceeded, Airflow triggers a callback function immediately (checked periodically by the scheduler, default every 5 seconds), sending alerts such as to addresses, without waiting for task completion. This provides enhanced flexibility over SLAs, which were removed in 3.0; migrations can use DeadlineReference.DAGRUN_LOGICAL_DATE for equivalent behavior.

Monitoring and UI

Apache Airflow provides a web-based (UI) that serves as the primary tool for monitoring, managing, and , offering visualizations and interactive elements to track DAG executions and task states. The UI includes a DAG List View that displays all available DAGs along with their status, schedule intervals, and tags, while the DAG Details Page provides deeper insights through the Grid View—a heatmap of task statuses over time—and the Graph View, which illustrates task dependencies and workflow structure. Additionally, the Asset Graph View visualizes data asset lineage across DAGs. These features enable users to quickly assess pipeline health and navigate complex workflows without relying on command-line tools. Task monitoring within the UI is facilitated by the Task Instance View, which displays detailed logs, including system output and error messages, alongside mini Gantt-style timelines in the Task Instances tab to show task durations and overlaps. (RBAC), introduced in Airflow 2.0 and enhanced in 2.2, governs UI interactions, with permissions determining visibility of elements like the Admin tab for . The scheduler's role in triggering DAG runs populates these views with for ongoing observation. In Airflow 3.0 and later versions released in 2025, the UI underwent a significant refresh, incorporating modern React-based components for improved rendering and responsiveness. Airflow's logging system captures task-level output with configurable rotation to manage storage, using the default FileTaskHandler to write logs to the local while tasks execute on workers. Logs can be remotely integrated with the stack via handlers or cloud services like Amazon CloudWatch through provider packages, allowing centralized aggregation and search. This setup ensures logs remain accessible via the UI even after task completion, supporting post-execution analysis. For metrics and alerting, Airflow emits built-in counters and gauges—such as dag_runs for execution counts and task_duration for performance tracking—to StatsD or OpenTelemetry backends, which can be scraped by using a StatsD exporter for visualization in tools like . Configurable alerts on failures, such as task retries or deadline misses, are set via DAG definitions and delivered through email, Slack, or other hooks, enhancing proactive monitoring without external dependencies. Debugging is supported through UI elements like the Task Instance Details page, which exposes metadata such as start times and try numbers, and the XCom Viewer, allowing inspection of cross-task communication values pushed during runs. Rendered Templates in the UI display evaluated templated fields for tasks, aiding in verification of dynamic configurations. These tools streamline issue resolution by providing contextual data directly in the interface. UI security features include authentication via LDAP, , or other Flask-AppBuilder backends, ensuring secure access to sensitive workflow views. Audit logs track user actions, such as DAG modifications or task clearances, accessible under the Admin tab with filtering and search capabilities for compliance and forensics. RBAC further enforces granular permissions, preventing unauthorized interactions.

Ecosystem

Providers and Integrations

Apache Airflow providers are modular, standalone packages that extend the platform's core by supplying operators, hooks, sensors, and transfer operators for seamless integration with external systems and services. These packages encapsulate the necessary components to interact with specific technologies, allowing users to build workflows that incorporate third-party tools without altering the Airflow core. Providers are designed to be installed independently, promoting modularity and ease of maintenance. Prominent providers cover major cloud ecosystems, including apache-airflow-providers-amazon for AWS services such as S3, EMR, and ; apache-airflow-providers-google for GCP offerings like , , and ; apache-airflow-providers-microsoft for Azure resources including Blob Storage and ; and apache-airflow-providers-snowflake for Snowflake data warehouse integration. Apache ecosystem providers, such as apache-airflow-providers-apache-beam for distributed processing and apache-airflow-providers-apache-kafka for pipelines, further enable integration with open-source tools. Over 80 such providers exist, all maintained under the official Apache Airflow project. Providers are installed via pip commands, such as pip install apache-airflow-providers-amazon, often alongside Airflow using extras like pip install 'apache-airflow[amazon]' for bundled dependencies. To prevent compatibility issues, installations should reference official constraint files that align provider versions with the target Airflow release. Official providers are community-managed and released through the apache-airflow-providers namespace on PyPI, distinguishing them from user-developed extensions. A representative integration example is an ETL pipeline transferring data from AWS S3 to BigQuery: the S3KeySensor from the Amazon provider monitors for new files in an S3 bucket, triggering a BigQueryInsertJobOperator from the provider to execute SQL inserts or loads into BigQuery tables. Similarly, the Snowflake provider enables execution of SQL queries on Snowflake databases, including triggering predefined Snowflake tasks asynchronously by executing the EXECUTE TASK <task_name>; command via operators such as SQLExecuteQueryOperator (from the common SQL provider) with a Snowflake connection. Such workflows leverage provider-specific hooks for authentication and data handling, ensuring secure connections across services. Since Airflow 2.0, providers have been fully decoupled from the core distribution, enabling independent versioning and release cycles that follow Semantic Versioning (SemVer) guidelines. This separation, introduced to accelerate development of integrations, includes backward compatibility policies where major provider versions maintain support for prior Airflow releases within defined ranges. The last backport providers for Airflow 1.10 were released in March 2021, after which all updates target Airflow 2.x and later.

Extensibility and Plugins

Apache Airflow provides a robust plugin architecture that enables users to extend its functionality by integrating custom components into the core system without modifying the source code. Plugins are implemented as Python modules placed in the $AIRFLOW_HOME/plugins directory, where they are automatically discovered and loaded by Airflow's built-in plugin manager during startup. This manager, enhanced in Airflow 2.0 and later, supports entry points for various extensions, including custom operators, hooks, and macros, through the AirflowPlugin class, which defines attributes like operators, hooks, and macros to register these components. For instance, a plugin can expose custom operators by listing them in the operators attribute, allowing seamless integration into DAGs as if they were native. Users can further extend Airflow by developing custom , which determine how tasks are executed, to support hybrid environments such as combining local processing with containerized workloads. Custom are created by subclassing BaseExecutor and implementing key methods like execute_async for asynchronous task submission and sync for state synchronization, then configuring them via the executor setting in airflow.cfg or per-DAG/task. Since Airflow 2.10.0, multiple can be specified (e.g., LocalExecutor,KubernetesExecutor) to enable hybrid setups, where tasks route to appropriate backends based on configuration, facilitating custom integrations for scalable, isolated executions. While the scheduler itself is a core component focused on DAG monitoring and task triggering, extensions can influence scheduling behavior through plugins or custom timetables, though full custom schedulers are not directly supported and typically require architectural adjustments. Airflow leverages Jinja templating for dynamic parameterization within DAGs, allowing templates in operator fields to incorporate runtime values, variables, and macros for flexible workflow definitions. Built-in macros, accessible via the macros namespace (e.g., {{ ds }} for execution date or {{ ti }} for task instance), provide utilities like date formatting and timedelta calculations to generate context-aware parameters. Users can define custom macros globally through plugins by adding them to the macros attribute of AirflowPlugin or locally within a DAG using the user_defined_macros parameter, enabling tailored functions such as environment-specific path resolution in task arguments. Best practices for developing extensions emphasize rigorous testing and modular packaging to ensure reliability and maintainability. Testing custom components, such as operators or plugins, is facilitated by tools like pytest, often augmented with pytest-airflow for mocking contexts and validating DAG imports without a full environment; for example, using DagBag to check for import errors or simulating task executions to verify states like SUCCESS. Plugins and custom code should be packaged as Python extras or provider-like packages using entry points (e.g., apache_airflow_provider), allowing distribution via PyPI and easy installation with pip install -e .[extras], which promotes reusability akin to providers while avoiding direct filesystem modifications. Despite these capabilities, Airflow's extensibility has limitations, particularly around plugin loading and compatibility. Plugins are loaded only at startup, necessitating restarts of the webserver and scheduler for changes to take effect, which can disrupt production environments. Additionally, custom extensions may conflict with core updates, as Airflow 3.0+ introduced breaking changes like replacing Flask with , requiring legacy plugins to use compatibility layers or face deprecation issues.

Community and Adoption

Development and Governance

Apache Airflow is governed under the Apache Software Foundation's consensus-driven model, with oversight provided by the Project Management Committee (PMC), a group of elected members who guide the project's technical direction, vote on releases, and nominate new committers. The PMC, established in December 2018 and chaired by Bolke de Bruin, maintains a dynamic roster of committers—individuals granted write access to the codebase—who actively contribute to development and review. Project issues and improvements are tracked using JIRA at issues.apache.org/jira/projects/AIRFLOW, while discussions occur on dedicated mailing lists, including the dev list for proposals and technical debates and the users list for support queries. Contributions follow a structured leveraging the mirror of the official repository. Developers the repository, implement changes in feature branches, and submit pull requests for review by committers, ensuring adherence to coding standards. Code style is enforced through pre-commit hooks configured in .pre-commit-config., which run automated checks for formatting, linting, and during the commit . The project maintains a quarterly for minor releases (e.g., 3.1, 3.2) and annual major releases, as seen with the April 2025 launch of version 3.0, allowing for iterative improvements while supporting long-term stability. Key initiatives emphasize enhancing core capabilities and ecosystem growth, including task isolation via the Task Execution API, scheduler-managed backfills and asset-based scheduling for improved orchestration, a modern React-based , and the movement of core operators to the apache-airflow-providers-standard package for broader integrations. Official documentation, hosted at airflow.apache.org, includes comprehensive on usage, while the contributor guide in the contributing-docs directory provides newcomers with setup instructions, overviews, and best practices for effective participation. Development is supported by funding from corporate sponsors, including as the lead commercial backer providing engineering resources and Google Cloud, a Platinum sponsor of the Foundation, which funds contributions to and related projects. These partnerships enable sustained investment in the provider ecosystem and community events like the Airflow Summit.

Use Cases and Impact

Apache Airflow is widely employed for orchestrating extract-transform-load (ETL) and extract-load-transform (ELT) pipelines at scale, enabling organizations to manage complex data workflows programmatically. For instance, , the original creator of Airflow, utilizes it to process daily data volumes exceeding petabytes, automating dependencies across hundreds of tasks to support analytics and features. These applications highlight Airflow's strength in handling batch-oriented in production environments. Beyond ETL, Airflow facilitates operations () by scheduling model training, validation, and deployment workflows, allowing data scientists to define pipelines in Python while integrating with tools like and . In contexts, it supports and () by automating infrastructure provisioning, testing, and monitoring tasks, often as part of broader architectures. Industry adoption is robust, with Airflow powering data teams at over 77,000 organizations as of November 2024, including a significant portion of companies, driven by its open-source maturity and community contributions exceeding 3,000 developers as of 2025. Airflow's impact lies in establishing workflow-as-code as a standard paradigm, shifting from manual scripting to declarative, version-controlled pipelines that enhance and . This approach has influenced subsequent tools like and Dagster, which build on Airflow's (DAG) model while addressing scalability pain points such as executor management. However, Airflow's operational complexity— including cluster scaling and dependency resolution—has spurred the rise of to alleviate infrastructure burdens. Offerings like Astro provide fully hosted environments with built-in scaling, while Google Cloud and Amazon Managed Workflows for Apache Airflow (MWAA) integrate natively with their respective clouds for seamless deployment and monitoring. Looking ahead, is evolving toward deeper integration with serverless architectures and AI-driven , enabling event-based triggers and automated optimization in 2025 and beyond. Surveys indicate over 85% of users anticipate expanded use in revenue-generating applications, fueled by enhancements in real-time processing and hybrid support. This trajectory positions as a foundational layer for AI-augmented data ecosystems, reducing manual oversight while maintaining its core extensibility for custom integrations.

References

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