Apache Airflow – Complete Guidance for Beginners

Apache Airflow has grown from an internal tool at Airbnb into the de facto standard for workflow orchestration. As of November 2024 the project was downloaded more than 31 million times per month, compared with fewer than one million downloads just four years earlierastronomer.io. Over 77 000 organizations use Airflowastronomer.io and more than 90 % of surveyed engineers describe the platform as critical to their data operationsastronomer.io. Large enterprises run Airflow at scale: 53.8 % of companies with more than 50 000 employees depend on Airflow for mission‑critical workloads and one in five operate twenty or more production instancesastronomer.io. This guide explains what Airflow is, how to get started, its architecture, key features and advantages, practical use cases and best practices, and suggests a video for visual learners.

What Is Apache Airflow?

Apache Airflow is an open‑source platform for programmatically developing, scheduling and monitoring batch‑oriented workflowsairflow.apache.org. Rather than clicking through a UI, you author Directed Acyclic Graphs (DAGs) in Python code; each DAG defines tasks (work units) and their dependencies so that Airflow knows the order of executionairflow.apache.org. Airflow’s “workflows as code” approach offers several advantages:

• Dynamic pipelines – Because workflows are defined in code, you can generate and parameterise DAGs dynamicallyairflow.apache.org. This makes it easy to create hundreds of similar DAGs from templates.

• Extensibility – Airflow ships with a wide range of built‑in operators and sensors and can be extended with custom onesairflow.apache.org. Hooks provide high‑level interfaces to connect with databases, cloud services and APIsaltexsoft.com.

• Flexibility – Jinja templating lets you parameterise tasks and reuse scripts, while Python makes it easy to integrate any library or logicairflow.apache.org.

• Version control and testing – Because DAGs are just Python files, they can be stored in Git, enabling collaborative development, testing and code reviewsairflow.apache.org.

• Open source and Python‑native – Airflow uses Python, one of the most popular programming languagesaltexsoft.com. The open‑source licence and an active community of thousands of contributors ensure rapid innovationaltexsoft.com.

Getting Started: Installation and Setup

Airflow can run on your laptop or scale to a distributed cluster. The following high‑level steps summarise how to install Airflow locally; consult the official documentation for details.

1. Prerequisites – Install Python 3.8+ and choose a database (PostgreSQL or MySQL for production; SQLite is fine for testing)xenonstack.com.

2. Set Airflow home – Optionally set an environment variable to specify where Airflow will store its configuration and logs:

   export AIRFLOW_HOME=~/airflow
   

3. Install Airflow – Use pip with the appropriate constraints file to install Airflow and its dependenciesxenonstack.com. For example:

   pip install "apache-airflow==2.7.0" --constraint https://raw.githubusercontent.com/apache/airflow/constraints-2.7.0/constraints-3.8.txt
   

4. Initialise the database – Airflow stores metadata (DAG runs, task states, users) in a database. Initialise it with:

   airflow db init
   

   This command creates the necessary tables and default configurationxenonstack.com.

5. Create an admin user – Create a user with the appropriate role using the CLIxenonstack.com:

   airflow users create \
     --username admin \
     --firstname YourName \
     --lastname Surname \
     --role Admin \
     --email you@example.com
   

6. Start Airflow components – Launch the web server and scheduler:

   airflow webserver --port 8080 &
   airflow scheduler &
   

   The UI will be available at http://localhost:8080 where you can view, trigger and monitor DAGs.

7. Define DAGs and operators – Create Python files in the dags/ folder to define workflows using built‑in operators (e.g., PythonOperator, BashOperator)xenonstack.com. Use the @task decorator to turn ordinary functions into Airflow tasksastronomer.io.

Core Concepts and Architecture

Airflow’s architecture centres on a central metadata database and several interacting services:

• Scheduler – Reads DAG definitions, determines when tasks should run and submits runnable tasks to an executorairflow.apache.org.

• DAG Processor – Parses and serialises DAG files into the databaseairflow.apache.org.

• Webserver – Provides a UI to view DAGs, trigger runs and inspect logsairflow.apache.org.

• Metadata database – Stores the state of DAGs, task instances and usersairflow.apache.org.

• DAG files folder – A directory containing Python scripts that define DAGsairflow.apache.org.

Optional components include executors and workers for distributed execution, triggerers for deferred tasks and plugins to extend functionalityairflow.apache.org. The figure below shows a simplified architecture.

Features and Advantages of Airflow

Airflow’s success is due to a combination of flexibility, scalability and an active community. Key features include:

Feature	Explanation & benefits	Sources
Workflows as Code	DAGs and tasks are defined in Python, enabling dynamic generation, parameterisation and version control. This “code first” approach makes workflows modular, testable and easy to reviewairflow.apache.orgairflow.apache.org.	Airflow docs
Extensible Connectors & Hooks	A large ecosystem of built‑in operators, sensors and hooks allows Airflow to interact with databases, cloud services and APIs. Hooks simplify integration with platforms like MySQL, PostgreSQL, AWS, Google Cloud and Slack; custom operators and hooks can be written when no pre‑built option existsaltexsoft.com.	AltexSoft
Advanced Scheduling & Dependency Management	Airflow supports cron‑like schedules and dataset‑driven scheduling where DAGs run when upstream data is available. Tasks have explicit dependencies, and the scheduler can backfill historical runs or retry failed tasksastronomer.iomedium.com.	Astronomer, Medium
Scalability and Concurrency	Airflow scales from a single laptop to clusters of workers using Celery or Kubernetes executors. DAGs can run hundreds of tasks in parallel, and multiple schedulers can operate simultaneously for high availabilityastronomer.ioaltexsoft.com.	Astronomer, AltexSoft
Observability & UI	The web‑based UI lets you view DAG graphs, task statuses and logs and provides buttons to trigger, pause or retry DAGs. Built‑in alerting sends notifications on failures or successesairflow.apache.orgmedium.com.	Airflow docs, Medium
Reliability & Resilience	Features like automatic retries, rescheduling and callback functions ensure that pipelines recover from transient failures and run to completionmedium.com.	Medium
Python‑Native & Open Source	Airflow uses Python, making it accessible to a wide pool of developers and data scientistsaltexsoft.com. Its open‑source nature encourages community contributions and rapid innovationaltexsoft.com.	AltexSoft
REST API & Programmatic Control	Since version 2.0, Airflow offers a REST API for triggering workflows, managing users and integrating with external systemsaltexsoft.com.	AltexSoft
Community & Ecosystem	Thousands of contributors maintain Airflow and publish tutorials, plugins and provider packages. Resources like the Astronomer Registry and community Slack support newcomersaltexsoft.comastronomer.io.	AltexSoft, Astronomer

Advantages Summarised

• Language and talent – Python is one of the most widely used languages in data science, so Airflow’s Python‑native design lowers the learning curve and increases developer productivityaltexsoft.com.

• Everything as code – Workflows, dependencies and configuration are defined in code, giving you full control and flexibilityaltexsoft.com.

• Horizontal scalability – Airflow supports task concurrency and multiple schedulers, enabling high throughput and reliable processingaltexsoft.com.

• Simple integrations – A rich library of hooks and provider packages lets you quickly connect to popular databases, cloud services and toolsaltexsoft.com.

• Programmatic access – The REST API allows external systems to trigger workflows or manage users and adds on‑demand execution capabilitiesaltexsoft.com.

• Vibrant community – Airflow is backed by a large, active community that contributes new features, operators and documentationaltexsoft.com.

Major Use Cases and Examples

ETL/ELT and Analytics Pipelines

Airflow is widely used to extract, transform and load data. More than 90 % of respondents to Airflow’s 2023 survey said they use Airflow for ETL/ELT workloadsairflow.apache.org. Airflow’s tool‑agnostic design, dynamic task mapping and object storage abstraction make it easy to integrate with sources like Amazon S3 or Google Cloud Storage and transform data at scaleairflow.apache.orgairflow.apache.org. A simple industry example from the Airflow documentation extracts climate data from a CSV and real‑time weather data from an API, merges them, and loads the results into a dashboardairflow.apache.org. Airflow handles scheduling, retries and logging for every step.

Business Operations and Data‑Driven Products

Organizations build customer‑facing products and run analytics dashboards using Airflow. It can power personalised recommendation engines, update data in dashboards or prepare data for large language model (LLM) applicationsairflow.apache.org. Airflow’s tool‑agnostic and extensible nature lets teams switch data warehouses or BI tools without rewriting pipelinesairflow.apache.org. Features like dynamic task mapping, datasets and notifications ensure pipelines adjust to changing customer lists and alert engineers when issues ariseairflow.apache.orgairflow.apache.org.

Infrastructure and DevOps Management

Because Airflow can call any API, it is also used to manage infrastructure. You can orchestrate the provisioning of Kubernetes clusters, Spark jobs or other cloud resourcesairflow.apache.org. Starting with Airflow 2.7, setup/teardown tasks allow you to spin up infrastructure before a workflow runs and automatically clean it up afterwards, even if tasks failairflow.apache.org. This is invaluable for cost‑efficient compute clusters in MLOps or big data workloadsairflow.apache.org.

MLOps and Generative AI

Airflow orchestrates the machine‑learning life cycle, from data ingestion and feature engineering to model training, evaluation and deploymentairflow.apache.org. It is tool‑agnostic: you can integrate any ML framework or vector database. A retrieval‑augmented generation (RAG) example from the documentation ingests news articles, stores embeddings in Weaviate and generates trading adviceairflow.apache.org. Airflow provides monitoring, alerting and automatic retries, making it a reliable backbone for LLMOps workflowsairflow.apache.org.

Adoption and Industry Trends

The 2025 State of Airflow report highlights Airflow’s momentum:

• Explosive adoption – Monthly downloads jumped from less than one million in 2020 to over 31 million in November 2024astronomer.io. Airflow has over 3 000 contributors and more than 29 000 pull requestsastronomer.io.

• Enterprise usage – At least 77 000 organizations use Airflowastronomer.io. Among enterprises with >50 k employees, 53.8 % run mission‑critical workloads on Airflow and more than 20 % operate twenty or more Airflow instancesastronomer.io.

• Mission‑critical status – Over 90 % of data professionals consider Airflow critical to their operationsastronomer.io; 85 % plan to build revenue‑generating products on Airflow within a yearastronomer.io.

• Multi‑cloud integration – Users split their workloads across Snowflake, Databricks and BigQuery with near‑equal adoptionastronomer.io, reinforcing Airflow’s role as the orchestration layer that unifies heterogeneous data stacks.

• AI adoption – Around 30.6 % of experienced users run MLOps pipelines and 13.3 % run generative‑AI pipelines on Airflowastronomer.io.

• User demographics – Two‑thirds of companies have more than six Airflow usersbigdatawire.com and 55 % of respondents interact with Airflow dailybigdatawire.com; 93 % would recommend itairflow.apache.org.

These statistics show that Airflow has matured into a foundational component of the modern data stack, powering analytics, machine learning and operational workloads at scale.

Limitations and Challenges

Airflow excels at orchestrating batch‑oriented, finite workflows but has limitations:

• Not designed for streaming – Airflow triggers batch jobs on a schedule or by event; it isn’t suited for continuous event streamsairflow.apache.org. Tools like Apache Kafka handle real‑time ingestion; Airflow can periodically process that data in batchesairflow.apache.org.

• No built‑in DAG versioning – Airflow doesn’t yet track historical DAG versions, so deleting tasks removes their metadata. Users must manage DAG versions in Git and assign new DAG IDs when making major changesaltexsoft.com.

• Documentation and learning curve – Some users find official documentation abridged; onboarding requires understanding scheduling logic, configuration and Python scriptingaltexsoft.com. Novices may face a steep learning curvealtexsoft.com.

• Requires Python skills – Airflow adheres to “workflow as code”, so non‑developers may need training to author DAGsaltexsoft.com.

Best Practices for Beginners

To get the most out of Airflow, follow these guidelinesastronomer.io:

1. Start simple – Begin with straightforward DAGs before tackling complex workflowsastronomer.io.

2. Leverage pre‑built operators and sensors – Use the extensive library of operators and hooks to interact with databases, cloud storage, email, Slack, etc.astronomer.io. If there’s no operator for your use case, convert a Python function into a task using the @task decoratorastronomer.io.

3. Optimise scheduling with datasets – Airflow’s dataset API lets you trigger DAGs when upstream data is updated, enabling event‑driven pipelines instead of rigid schedulesastronomer.io.

4. Manage inter‑task communication – Use XComs sparingly to pass small data between tasks; for larger payloads, implement a custom XCom backendastronomer.io or store data externally (e.g., object storage).

5. Use version control and CI/CD – Store DAGs in Git, enforce code reviews and automate deployment via containers. Tag releases so you can roll back if neededairflow.apache.org.

6. Parameterise and template your workflows – Use Jinja templating to define dynamic inputs such as dates or file paths, enabling DAG reuse with different parametersaltexsoft.com.

7. Implement error handling and monitoring – Configure retries, timeouts and alerting; monitor DAGs via the UI and set up notifications (email or Slack) to detect failuresmedium.com. Use external observability tools or managed services (e.g., Astronomer’s Astro) for enterprise monitoringastronomer.io.

8. Document and test – Provide clear documentation for each DAG, including purpose, inputs and outputs. Write unit and integration tests to validate pipeline behaviouraltexsoft.com.

Suggested Video for Visual Learners

If you prefer learning through video, a free 1.5‑hour YouTube tutorial, often listed as “Apache Airflow Tutorial for Beginners” by LimeGuru, provides a concise yet thorough introduction. The course covers the fundamentals of Airflow, demonstrates how to execute pipelines using operators, explains how to schedule and monitor DAGs and includes a live demonstration of the Kubernetes Pod Operatorclasscentral.com. Search for the course title on YouTube or see the Class Central listing to watch the video and follow along.

Conclusion

Apache Airflow has become a cornerstone of modern data engineering, enabling organisations to orchestrate data pipelines, machine‑learning workflows and infrastructure operations. Its Python‑native, code‑first approach empowers teams to version, test and collaborate on workflows, while its scalability and extensibility make it suitable for small startups and large enterprises alike. With tens of thousands of organisations relying on Airflow and a vibrant community pushing the platform forward, learning Airflow offers newcomers a valuable skill set that spans ETL, MLOps, AI, and DevOps domains.