What is Apache Airflow? A guide for data engineers

Apache Airflow has become a go-to solution for managing complex, code-based data workflows, but using it effectively requires a clear understanding of how its architecture, task model, and ecosystem fit together. Whether you're refining your DAG structure, evaluating how Airflow compares to other orchestrators, or looking to scale your deployment, this guide breaks down what you need to know.

We'll explore Airflow's architecture, core abstractions, deployment patterns, recent upgrades, and trade-offs, plus how it integrates with tools like RudderStack to support a modern, end-to-end data pipeline.

Main takeaways:

• Apache Airflow is a Python-based orchestrator for defining, scheduling, and monitoring complex workflows
• It uses DAGs to manage task dependencies and supports retries, logging, and SLA tracking
• Airflow is best suited for batch and time-based workflows, not real-time processing
• Its modular architecture and rich ecosystem make it highly scalable and extensible
• Airflow pairs well with tools like RudderStack to enable end-to-end data pipeline orchestration

What is Apache Airflow?

Apache Airflow is an open-source platform for orchestrating data workflows. It allows data teams to define, schedule, and monitor pipelines as code using Python. Instead of relying on manual scripts or scattered automation, Airflow lets you manage complex workflows as Directed Acyclic Graphs (DAGs), a structure where each task represents a discrete step and dependencies control execution order.

Airflow was created at Airbnb in 2014 and has since become a foundational part of many modern data stacks. Its Python-native syntax, strong ecosystem of integrations, and modular architecture make it well-suited for everything from simple ETL jobs to enterprise-scale pipelines across cloud platforms, data warehouses, and APIs.

By defining workflows as code, Airflow encourages version control, testing, and modular development. It supports retries, SLA enforcement, logging, and alerting out of the box, making it especially effective for recurring, batch-oriented processes like data ingestion, reporting, and machine learning model training.

How does Apache Airflow work?

Airflow is built on a modular architecture that separates orchestration, execution, and monitoring into distinct components. These parts interact through a central metadata database, enabling real-time coordination and scalable task execution.

Key system components

• Scheduler: Continuously monitors DAG definitions and triggers task instances based on their schedule and dependencies.
• Executor: Executes the tasks by delegating them to worker processes. Executors can run locally or distribute tasks across multiple machines.
• Webserver: Provides a user-friendly UI where engineers can inspect DAGs, trigger runs manually, monitor task states, and view logs.
• Metadata Database: Stores the state of DAG runs, task instances, variables, and system-level metadata.
• DAG folder: A designated directory containing Python files that define DAGs and their associated tasks.

When a DAG is triggered, either on a schedule or manually, Airflow creates a DAG Run, which generates Task Instances. Each task instance runs independently and can be retried or logged as needed. This design supports parallel execution, robust error handling, and visibility across the entire pipeline lifecycle.

Core abstractions and workflow logic

Airflow workflows are constructed using several key abstractions:

• Operators: Predefined building blocks for tasks. Common examples include:
  • BashOperator (runs shell commands)
  • PythonOperator (executes Python functions)
  • EmailOperator (sends alerts)
• Sensors: Specialized operators that wait for an external condition (e.g., file availability or data presence). They can operate in "poke" or "reschedule" mode to optimize resource use.
• TaskFlow API: A decorator-based approach to defining Python functions as tasks, improving readability and dependency resolution. It leverages XComs to pass values between tasks automatically.

Configuration and runtime flexibility

Airflow also includes several mechanisms for customizing workflows:

• XComs (cross-communications): Allow tasks to pass small pieces of metadata to one another (e.g., timestamps, record IDs).
• Variables: Store global key-value pairs accessible to any DAG or task—often used for environment-specific configuration.
• Params: Enable dynamic runtime inputs for DAGs, useful for parameterizing tasks in manual or API-triggered runs.

Together, these features help engineers create scalable, maintainable workflows that are easy to adapt, monitor, and extend.

Recent improvements in Airflow 2.x and 3.x

Apache Airflow has seen major upgrades in recent releases that make it more stable, performant, and cloud-friendly.

Key highlights in Airflow 2.x:

• DAG parsing and scheduler decoupling: Parsing logic is now handled separately from scheduling, reducing lag and improving overall throughput.
• Smart sensor optimization: Sensors can now run more efficiently by consolidating checks, lowering resource usage.
• Stable REST API: Teams can manage DAGs and tasks programmatically with a documented, production-ready API.
• Improved multi-DAG execution: Running multiple workflows concurrently is now more efficient.
• Task log caching: Faster log access improves the UI experience and speeds up debugging.

New features in Airflow 3.x:

• DAG versioning: Allows teams to track and audit changes to DAGs over time, whih is critical for governance and rollback.
• Event-based scheduling: Adds flexibility by enabling workflows to be triggered by external events, not just time-based intervals.
• Secrets backend enhancements: Improved native support for tools like AWS Secrets Manager, GCP Secret Manager, and Vault.
• Modern Python support: Up-to-date compatibility with Python 3.10+ and cleaner dependency management.
• Plugin system improvements: Simplifies extending Airflow through providers and custom plugins.
• Ecosystem growth: Airflow now integrates more tightly with tools like OpenLineage, DataHub, and Great Expectations.

Why do teams use Apache Airflow?

Apache Airflow is one of the most widely adopted tools for orchestrating modern data workflows. Its flexible architecture, Python-native syntax, and broad ecosystem support make it a powerful solution for managing everything from daily ETL jobs to enterprise-wide governance workflows.

Core benefits

Airflow's strengths lie in its modular design and extensibility:

• Dynamic workflow orchestration: Airflow allows users to define and schedule complex, multi-step workflows using Directed Acyclic Graphs (DAGs). Dependencies are explicit, making task sequencing and failure handling predictable and repeatable.
• Python-based DSL: DAGs are defined entirely in Python, making workflows highly customizable and version-controllable. This also enables engineers to incorporate logic, variables, and libraries seamlessly.
• Built-in observability and alerting: Airflow includes SLA monitoring, automatic retries, detailed logging, and third-party integrations for alerts via email, Slack, PagerDuty, or Prometheus/Grafana, giving teams real-time visibility into workflow health.
• Scalability: Airflow can scale horizontally to meet increasing data volume or task concurrency demands. It supports distributed execution across workers, allowing organizations to parallelize workflows across clusters.
• Reusability and maintainability: Tasks and DAGs are modular by nature. Teams can build reusable templates and functions, reducing technical debt and simplifying updates.
• Ecosystem integrations: With support for hundreds of built-in operators and community-contributed providers, Airflow connects easily to tools like dbt, Snowflake, BigQuery, AWS, GCP, and more.
• Strong community: As a mature open-source project, Airflow benefits from a vibrant contributor base, regular updates, and deep documentation.

Common use cases

Teams use Airflow to orchestrate a variety of business-critical pipelines across data, analytics, and infrastructure:

• ETL and ELT workflows: Airflow automates the movement and transformation of data across systems, commonly ingesting from tools like Salesforce, HubSpot, or ad platforms, then preparing and loading data into warehouses like Snowflake or Redshift.
• Infrastructure and cloud automation: Use Airflow to provision cloud resources, trigger serverless functions, or manage environments. It's often used alongside Terraform or cloud SDKs to coordinate infrastructure-as-code workflows.
• Automated reporting and delivery: Airflow can generate and distribute reports on a set cadence, such as daily sales summaries or weekly performance dashboards, across formats like CSV, Excel, or PDF, and deliver them via email, Slack, or S3.
• Pipeline monitoring and alerting: Built-in SLA tracking and alerting integrations (email, Slack, PagerDuty) enable teams to flag failed tasks, delayed DAGs, or data anomalies. Airflow's retry and failure callbacks provide strong error recovery.
• Compliance and governance: Airflow orchestrates workflows that enforce data lineage tracking, privacy validation, and compliance checks. It's commonly used to automate audits for GDPR, HIPAA, or SOX requirements.

From startups building their first data stack to large enterprises running thousands of DAGs, Airflow adapts to evolving needs, making it a cornerstone of many production-grade data platforms.

Limitations and trade-offs of using Airflow

While Apache Airflow is a powerful orchestration tool, it's not the perfect fit for every use case. Understanding its limitations helps teams make informed architecture decisions and avoid misalignment with real-time or low-latency requirements.

Key limitations include:

• Batch-first design: Airflow was built for scheduled workflows. It doesn't natively support streaming or real-time event-driven use cases without significant customization or workarounds.
• Sensor inefficiencies: Sensors can block worker resources if not configured properly (e.g., running in "poke" mode instead of "reschedule" mode). This can lead to scalability and performance bottlenecks.
• Operational overhead: Running Airflow in production often requires dedicated DevOps support. Maintaining executor infrastructure, scaling schedulers, and managing dependencies can be complex.
• Learning curve: Building reliable DAGs requires a solid understanding of Airflow's abstractions—especially around retries, dependencies, and XComs. Debugging can also be non-trivial in large workflows.
• Limited native support for CI/CD and testing: Although DAGs are code, Airflow does not offer strong out-of-the-box support for automated testing, local mocking, or versioned DAG management (prior to Airflow 3.x).

Despite these trade-offs, many teams find that Airflow excels when workflows are well-defined, task sequences are clear, and orchestration requirements are time-based rather than reactive.

How Airflow compares to other orchestrators

As workflow orchestration evolves, newer tools have emerged to address specific pain points in areas like dynamic DAG generation, data lineage, and developer experience. Here's how Airflow stacks up against popular alternatives:

Airflow vs. Prefect

• Strengths of Prefect: Simpler syntax, dynamic DAG creation, and a cloud-native experience out of the box.
• When to choose Prefect: Ideal for teams prioritizing quick onboarding, dynamic workflows, or those without dedicated infrastructure support.
• Airflow's edge: Greater ecosystem maturity, community support, and extensibility for enterprise-grade needs.

Airflow vs. Dagster

• Strengths of Dagster: Built-in support for type checking, asset tracking, data lineage, and testing.
• When to choose Dagster: Best for teams focused on data quality, reproducibility, and strong developer tooling.
• Airflow's edge: More widely adopted, with a broader base of integrations and operators.

Airflow vs. Luigi

• Strengths of Luigi: Simplicity and ease of local use for small ETL tasks.
• When to choose Luigi: For small, straightforward pipelines that don't require cloud-scale orchestration.
• Airflow's edge: Scales far beyond Luigi in terms of observability, extensibility, and community support.

Airflow remains the most customizable and widely supported solution for batch-oriented orchestration. But for highly dynamic or event-driven pipelines, tools like Prefect or Dagster may offer a more streamlined experience.

How to deploy and scale Apache Airflow

Airflow supports a variety of deployment patterns, making it adaptable to different team sizes and workloads. Here's how to think about deploying and scaling it effectively.

1. Choose your deployment model

Airflow can be deployed in several ways depending on your team's needs and infrastructure maturity:

• Local development: Use SequentialExecutor or astro dev for testing DAGs in isolated environments.
• Docker and Docker Compose: Ideal for small teams who want to run Airflow components in containers with minimal setup.
• Kubernetes: Provides elasticity, task isolation, and scalability—best suited for production environments.
• Managed services: Platforms like Astronomer, AWS MWAA, and Google Cloud Composer reduce operational overhead by managing infrastructure, scaling, and monitoring.

Recommendation: Start with Docker or LocalExecutor in development. Move to Kubernetes or a managed service as workflows and team complexity grow.

2. Select the right executor

Choosing the right executor is essential for performance and maintainability.

• SequentialExecutor: Single-threaded; only one task runs at a time. Best for testing.
• LocalExecutor: Enables parallel task execution on a single machine. Suitable for small pipelines.
• CeleryExecutor: Distributes tasks across a pool of worker nodes using Redis or RabbitMQ. Ideal for horizontally scaling in production.
• KubernetesExecutor: Launches each task in a dedicated pod. Offers dynamic scaling and resource isolation in Kubernetes-native environments.

3. Isolate and scale Airflow components

For production environments, separating core components improves reliability and scalability:

• Webserver: Should run independently to avoid UI bottlenecks.
• Scheduler: Can be scaled horizontally (Airflow 2.x+ supports multiple schedulers).
• Workers: Should be able to autoscale based on task load and concurrency requirements.

Use a message broker (like Redis or RabbitMQ) and a robust metadata database (like PostgreSQL) to ensure performance under load.

4. Enable autoscaling and fault tolerance

As workloads grow, Airflow should respond dynamically:

• Use Horizontal Pod Autoscalers (HPA) with the KubernetesExecutor to scale based on CPU/memory usage or queue depth.
• With CeleryExecutor, tune autoscaling using autoscale and min_concurrency flags.
• Implement retry policies and SLA miss alerts to detect and recover from failures.

5. Monitor and optimize

Observability is critical for performance tuning and debugging:

• Use Prometheus and Grafana or third-party tools like Datadog to track metrics such as task duration, DAG run frequency, scheduler lag, and worker availability.
• Leverage Airflow's built-in logging and alerting features to stay ahead of issues.
• Set up dashboards to monitor key orchestration KPIs across environments.

When combined with RudderStack's real-time ingestion and transformation, Airflow gives teams end-to-end control, handling scheduled orchestration downstream from clean, governed data pipelines.

Orchestrate smarter pipelines with RudderStack and Airflow

Apache Airflow gives data engineers the orchestration power to automate and manage complex workflows at scale. With its Python-based DAGs and modular design, it's become a go-to tool for building robust batch pipelines, especially when workflows are clear, dependencies are predictable, and flexibility is essential.

But orchestration is only part of the picture. To deliver reliable insights and personalized experiences, you also need consistent, high-quality data flowing into your warehouse in real time. That's where RudderStack fits in, providing the infrastructure to collect, transform, and route customer data seamlessly across your stack.

By integrating RudderStack Event Stream with Airflow, your team gains full control over data collection and activation while automating orchestration with confidence.

Ready to modernize your data stack from ingestion to activation? Request a demo to see how RudderStack complements your Airflow workflows.