Apache Airflow

Open-source workflow orchestrator based on Python DAGs.

Reviewed by 7wData

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Publisher review

Apache Airflow is an open-source Python-native orchestration platform for authoring, scheduling, and monitoring data workflows using Directed Acyclic Graphs (DAGs). Launched in 2015, it remains the reference implementation for enterprise batch orchestration, powering mission-critical workflows at companies including OpenAI, Anthropic, and GitHub.

In 2026, Airflow is repositioning itself. Airflow 3.0, released in April 2025, marked the project's first major release in five years, introducing a React-based web UI, native DAG versioning, event-driven scheduling, and a Task SDK that reduces boilerplate. These changes reflect an effort to compete with newer platforms while maintaining backward compatibility. Survey data shows 32% of Airflow users now operate generative AI or MLOps workloads in production, signaling adoption in domains beyond traditional data engineering.

However, Airflow's foundational design—time-indexed execution, where every task anchors to a scheduled date—creates friction in modern data contexts. Workflows that respond to data-state changes or external events map awkwardly onto Airflow's scheduler-first model. Newer platforms like Dagster (asset-centric) and Prefect (cloud-native, dynamic) were purpose-built for these patterns and often offer superior developer experience.

Operational overhead remains substantial. Production deployments require a scheduler, webserver, metadata database, and executor cluster—often forcing adoption of Kubernetes for scaling. This complexity is a trade-off most teams accept in exchange for Airflow's unmatched integration ecosystem. Over 100 official provider packages ship operators for Snowflake, BigQuery, dbt, Databricks, Kafka, and dozens of other platforms. For teams with sprawling, time-based batch pipelines and diverse system dependencies, Airflow remains the path of least resistance.

Airflow is free to self-host; managed options (Astronomer, AWS MWAA, Google Cloud Composer) start around $100–$350/month and scale into enterprise tiers. The trade-off between self-hosted operational burden and managed cost is now a primary decision point for most organizations.

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How it works

  1. DAG-based workflow definition

    Define pipelines as Python code with explicit task dependencies, enabling version control, testing, and dynamic workflow generation.

  2. 100+ pre-built operator integrations

    Plug-and-play operators for Snowflake, BigQuery, dbt, Kafka, Spark, AWS, GCP, Databricks, Fivetran, Airbyte, and dozens of other platforms.

  3. Event-driven scheduling (Airflow 3.0)

    Trigger workflows based on external events rather than fixed time windows, enabling reactive pipelines alongside traditional batch scheduling.

  4. Native DAG versioning (Airflow 3.0)

    Track and roll back DAG changes directly within Airflow, eliminating the need for external versioning hacks or third-party tooling.

  5. Backfill and time-window support

    Retroactively fill historical data gaps and re-execute tasks for specific date ranges, critical for recovery and ad-hoc reprocessing.

  6. Web UI for monitoring and alerting

    React-based dashboard with DAG visibility, task logs, SLA tracking, manual triggering, and integration with Slack, email, and alerting systems.

  7. Distributed task execution with multiple executors

    Scale across multiple workers via CeleryExecutor (distributed queue), KubernetesExecutor (cloud-native), or managed executors in Astronomer and MWAA.

Strengths and trade-offs

Strengths

  • Unmatched integration ecosystem—100+ official operators cover virtually every enterprise system (Snowflake, BigQuery, dbt, Kafka, Spark, Databricks, etc.)
  • Proven production scale—battle-tested by OpenAI, Anthropic, GitHub, and thousands of enterprises for mission-critical workflows
  • Strong governance and observability—web UI, lineage tracking, SLA monitoring, audit logging, and integrations with alerting platforms enable enterprise compliance

Trade-offs

  • High operational overhead—requires scheduler, webserver, metadata database, and executor cluster; Kubernetes often mandatory for production, adding complexity and cost
  • Time-indexed architecture—fundamentally designed for batch scheduling; data-state-driven and event-reactive patterns map awkwardly compared to purpose-built competitors like Dagster and Prefect
  • Python-only constraint—all workflows must be Python; teams with polyglot infrastructure, real-time/streaming requirements, or non-Python codebases face fundamental limitations

Pricing context

Apache Airflow itself is free and open-source under the Apache License 2.0. Self-hosted deployments require infrastructure and operational investment (compute, database, queueing system). Managed options include Astronomer (starting ~$100/month for small deployments, scaling to $5,000+/month for enterprise multi-tenancy and SLA guarantees), AWS MWAA (~$350/month for mw1.small environment class, plus usage charges), and Google Cloud Composer. Most organizations choose managed Airflow to reduce operational burden; the free open-source version remains viable for teams with internal platform engineering capacity.

Getting started with Apache Airflow

  1. Set up Airflow instance

    Choose a managed service (Astronomer, AWS MWAA, Google Cloud Composer) to reduce setup complexity, or self-host using Docker. Managed options start around $100/month; self-hosting requires compute infrastructure and database setup.

  2. Create data connections

    Create connections in the Airflow web UI to authenticate with your data platforms (Snowflake, BigQuery, S3, Databricks, etc.). Use environment variables or Airflow's secrets backend to store credentials securely. Configure authentication for each platform you plan to use.

  3. Code your first workflow

    Write a Python file defining a workflow with explicit task dependencies using Airflow operators. Specify which tasks run in sequence or parallel, what data they touch, and where results land. Upload to your DAGs folder; the web UI auto-detects it.

  4. Trigger your first run

    Manually trigger the workflow from the web UI or command line. Watch task execution in real-time: check logs, verify data reaches its destination, and identify errors. Fix any issues in your code, then move to scheduling the workflow.

  5. Schedule and monitor

    Configure a schedule (daily, hourly, or event-driven in Airflow 3.0) to run the workflow automatically. Set up alerting via Slack or email for task failures. Use the web UI to monitor execution history and fine-tune SLA policies for your team.

Frequently Asked Questions

What is Apache Airflow?

Apache Airflow is an open-source Python-native orchestration platform for authoring, scheduling, and monitoring data workflows using Directed Acyclic Graphs (DAGs). Launched in 2015, it powers mission-critical workflows at companies including OpenAI, Anthropic, and GitHub, remaining the reference implementation for enterprise batch orchestration.

What are the main features of Airflow 3.0?

Airflow 3.0, released April 2025, introduced a React-based web UI, native DAG versioning, event-driven scheduling, and a Task SDK that reduces boilerplate. These changes enable workflows to respond to data-state changes and external events, competing with newer platforms while maintaining backward compatibility.

How many integrations does Airflow support?

Airflow ships over 100 official provider packages with operators for Snowflake, BigQuery, dbt, Databricks, Kafka, Spark, AWS, GCP, and dozens of other platforms. This unmatched integration ecosystem makes Airflow the path of least resistance for teams with sprawling, time-based batch pipelines and diverse system dependencies.

What are Airflow's main limitations?

Airflow's time-indexed architecture is fundamentally designed for batch scheduling; data-state-driven and event-reactive patterns map awkwardly compared to purpose-built competitors like Dagster and Prefect. Production deployments require substantial operational overhead: scheduler, webserver, metadata database, executor cluster, and often Kubernetes for scaling, adding complexity and cost.

How much does it cost to run Apache Airflow?

Apache Airflow itself is free and open-source under Apache License 2.0. Self-hosted deployments require infrastructure investment. Managed options start around $100–$350/month: Astronomer (~$100/month for small deployments), AWS MWAA (~$350/month), and Google Cloud Composer, scaling to enterprise tiers with SLA guarantees.

Should I use Airflow or Dagster?

Choose Airflow if you have sprawling, time-based batch pipelines and diverse system dependencies; its 100+ integrations are unmatched. Choose Dagster or Prefect if you need asset-centric, data-driven, or event-reactive workflows. Airflow's operational overhead is higher; newer platforms offer superior developer experience for modern data patterns.

Alternatives in this category

Integrations

Snowflake BigQuery Databricks dbt Kafka

How Apache Airflow compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

Apache Airflow

Pricing
Apache Airflow itself is free and open-source under the Apache License 2.0. Self-hosted deployments require infrastructure and operational investment (compute, database, queueing system). Managed options include Astronomer (starting ~$100/month for small deployments, scaling to $5,000+/month for enterprise multi-tenancy and SLA guarantees), AWS MWAA (~$350/month for mw1.small environment class, plus usage charges), and Google Cloud Composer. Most organizations choose managed Airflow to reduce operational burden; the free open-source version remains viable for teams with internal platform engineering capacity.
Target
Apache Airflow is an open-source Python-native orchestration platform for authoring, scheduling, and monitoring data workflows using Directed Acyclic Graphs (DAGs).
Deployment
self-hosted
Strength
Unmatched integration ecosystem—100+ official operators cover virtually every enterprise system (Snowflake, BigQuery, dbt, Kafka, Spark, Databricks, etc.)
Watch for
High operational overhead—requires scheduler, webserver, metadata database, and executor cluster; Kubernetes often mandatory for production, adding complexity and cost

Prefect

Pricing
Free tier ($0/month, 2 users). Starter $100/month. Team $400/month. Pro $500/month. Enterprise: contact sales.
Target
Python-heavy data engineering and MLOps teams, startups and mid-market companies building new pipelines without dedicated platform engineers.
Deployment
Hybrid: SaaS orchestration layer plus user-supplied compute infrastructure.
Strength
Dynamic workflows resolved at runtime support loops and conditional branching, unlike static DAG-based orchestrators.
Watch for
Cloud tier orchestrates only. Compute (Kubernetes, Docker, VMs) is separate and billed apart, making the $500/month Pro tier hard to justify at small scale.

Dagster

Pricing
Solo $10/month plus $0.040/credit. Starter $100/month plus $0.035/credit. Pro and Enterprise: contact sales.
Target
Data engineering teams on greenfield projects or dbt-heavy stacks needing asset-centric orchestration with built-in lineage.
Deployment
Open-source self-hosted, SaaS serverless, or hybrid (user code runs in customer environment).
Strength
Asset-centric architecture treats data assets as first-class objects, auto-tracking lineage and upstream/downstream dependencies during execution.
Watch for
Per-operation credit pricing escalates sharply for high-frequency jobs. A 5-minute schedule with 8 ops can exceed $1,100/month at Starter tier.

Astronomer Astro

Pricing
Developer from $0.35/hr, Team from $0.42/hr, workers from $0.13/hr (scale-to-zero). Business and Enterprise: Custom.
Target
Data teams and enterprises wanting managed Airflow without GCP or AWS native vendor lock-in.
Deployment
SaaS, with Private Cloud option for enterprise multi-environment setups.
Strength
Workers scale to zero when idle, billing only for active task execution, eliminating always-on infrastructure cost.
Watch for
Minimum entry around $100/month plus annual agreements required; users report opinionated deployment model creates lock-in versus self-hosted Airflow.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. airflow.apache.org — Core features overview, web UI, integrations, and Python-based DAG authoring
  2. www.astronomer.io — Airflow 3.0 release details (April 2025), user survey data (88% recommendation, 32% AI/ML adoption, 26% on Airflow 3), AI workload capabilities
  3. medium.com — Time-indexed architecture constraints, mismatch with modern data platforms, transition from task-centric to data-centric paradigms
  4. risingwave.com — Comparative analysis of Airflow, Dagster, and Prefect; architecture differences, use cases, scalability, and integration ecosystem strengths
  5. www.rudderstack.com — Core features, use cases (ETL, reporting, infrastructure automation, compliance workflows), deployment models, executor types, and limitations in real-time/streaming
  6. www.nextlytics.com — Airflow 3.0 feature details: React UI redesign, DAG versioning, event-driven scheduling, Task SDK, asset-centric design, security improvements
  7. branchboston.com — Design philosophy differences, developer experience trade-offs, when to choose each platform, practical iteration speed and execution scaling
  8. aws.amazon.com — AWS MWAA pricing tiers (mw1.small ~$350/month, mw1.medium ~$700/month, mw1.large ~$1,400/month)