Apache Airflow® Use Cases: A Comprehensive Guide with Real-World Examples

Apache Airflow is a powerful, open-source workflow orchestration tool designed to streamline complex data engineering tasks and beyond. At its core, Airflow excels in scheduling, dependency management, and task monitoring. This makes it an indispensable asset for data teams seeking to automate and optimize their data pipelines while ensuring data integrity and operational efficiency.

In this article, we will delve into a diverse range of use cases that highlight the versatility and power of Apache Airflow® in orchestrating the most demanding and intricate workflows.

Whether you are a data engineer, data analyst, data scientist, software developer or infrastructure engineer, the use cases explored here will provide you with a comprehensive understanding of Airflow’s potential. You’ll also see examples of Airflow’s versatility extending across multiple industries including finance, healthcare, retail, manufacturing, gaming, and more.

What is Airflow used for?

Airflow has become the de-facto standard for expressing data flows as code with data professionals using it to author, schedule, and manage complex workflows and data pipelines, along with the underlying infrastructure that runs them—whether in the cloud or on-premise.

Airflow’s key features include:

• Pipeline scheduling: Automates workflow execution based on defined schedules.
• Dependency management: Ensures tasks run in the correct order.
• Advanced task monitoring: Provides visibility into workflow health and performance.
• Scalability: Handles workflows at any scale, from small to enterprise-level.

These features make Airflow a go-to choice for orchestrating complex, large-scale workflows with the following key benefits:

• Versatility: Integrates with a wide range of tools and cloud services, and allows you to run custom scripts.
• Reliability: Built-in monitoring, alerting, and retry mechanisms ensure workflows run consistently.
• Extensibility: Supports custom plugins for specific needs.
• Transparency: Provides full visibility into pipeline progress and timelines through an intuitive UI monitoring.

So what can you use Airflow for? Data teams rely on Airflow for a wide range of use cases — automating ETL and ELT processes, orchestrating MLOps and generative AI, powering operational analytics and data driven applications through to infrastructure lifecycle management.

Mapping Airflow features to use cases

Airflow’s versatility comes from a broad set of features and capabilities that are purpose-built to serve a diverse range of use cases.

Data integration for ETL/ELT

Apache Airflow is an exceptional fit for data integration processes like ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). Its dynamic pipeline capabilities allow data engineers to seamlessly extract data from various sources, transform it according to business rules, and load it to their target destination. Airflow’s robust scheduling and dependency management—allowing users to set clear upstream and downstream task relationships—ensure that data pipelines run smoothly and on time, reducing manual intervention and minimizing errors.

With its hundreds of specialized operators, you can use Airflow to orchestrate pipelines that integrate data from SaaS, PaaS, and IaaS services, Git repositories, and the web, in addition to legacy on-prem apps, databases, and file systems. Integration with tools like dbt, Ray and Databricks makes it easy for data and analytics engineers to transform data, before then having Airflow populate outputs into data warehouses and data lakes as well as feed batches of data to their fraud-detection alerts, clickstream analytics, and other time-critical services.

With Airflow, data professionals can easily monitor the status of ETL/ELT jobs, receive alerts for failures, and quickly troubleshoot issues. By leveraging Airflow for ETL/ELT processes, organizations can achieve faster, more reliable data integration, leading to better data quality and more informed decision-making.

Generative AI and MLOps

Apache Airflow is a powerful tool for managing and automating generative AI (GenAI) and machine learning operations (MLOps), making it a great fit for data and ML engineers as well as with software developers focussed on creating next-gen AI based applications.

Airflow’s dynamic pipelines and robust scheduling capabilities enable the orchestration of complex ML/AI workflows, from data preprocessing to model deployment, retrieval-augmented generation (RAG), and monitoring. Airflow integrates seamlessly with popular GenAI and ML tools and frameworks for experiment tracking and model serving. This allows engineers to automate the end-to-end model lifecycle, including data preparation, model training, evaluation, and deployment.

Airflow supports complex workflows like retrieval-augmented generation, where the model retrieves relevant information from large datasets to enhance the quality and relevance of generated outputs. This capability is essential for creating more accurate and contextually appropriate responses.

RAG reference architecture for conversational AI. More examples are available from Astronomer’s GenAI cookbook

With Airflow, ML/AI engineers can implement CI/CD best practices for the pipelines training their models. Automated pipelines can trigger model retraining based on new data or code changes, ensuring that models stay up-to-date and perform optimally.

By leveraging Airflow for MLOps orAirflow for Generative AI, organizations can accelerate the development and deployment of ML models, improve collaboration across engineering teams, and ensure the reliability and accuracy of their ML/AI-driven applications. This results in more efficient use of resources, faster time-to-market, and better business outcomes from AI investments.

Operational analytics

Operational analytics refers to the practice of delivering data-driven insights by feeding data into dashboards, apps, or portals that are used to monitor and optimize business operations.

Apache Airflow is an ideal solution for orchestrating the complex workflows required for operational analytics. Its ability to schedule, automate, and manage pipelines ensures that relevant data from diverse sources—whether cloud-based, on-premise, or at the network edge—reaches users and decision-makers in a timely manner.

With prebuilt integrations for tools like Snowflake, Databricks, Google BigQuery, and AWS Redshift, Airflow simplifies the development, deployment, and maintenance of analytic workflows. Furthermore, teams can monitor data quality, identify issues, and refine metrics and KPIs as business needs evolve, keeping insights accurate and actionable.

By using Airflow for operational analytics organizations deliver scalable, sustainable analytics across distributed environments.

Infrastructure management

Infrastructure management involves the automated provisioning, configuration, and maintenance of IT infrastructure to ensure optimal performance and scalability. Apache Airflow is a highly effective tool for managing infrastructure due to its ability to orchestrate complex workflows and automate repetitive tasks.

Airflow’s robust scheduling and dependency management capabilities enable infrastructure engineers to automate the deployment and scaling of resources across various environments, whether running in the cloud, on-premise data centers or edge computing locations. Its integration with tools like Kubernetes allows for seamless management of infrastructure components, ensuring consistency and reducing manual errors. The setup and teardown feature in Airflow is specifically designed for infrastructure provisioning.

With Airflow, engineers can create automated workflows to handle tasks such as server provisioning, software updates, and backup operations. This not only saves time but also ensures that infrastructure is consistently configured and maintained according to best practices. Airflow’s monitoring and alerting features enable proactive management of infrastructure, allowing engineers to detect and address issues before they impact operations.

By leveraging Airflow for infrastructure management, organizations can achieve greater efficiency, scalability, and reliability in their IT operations. Airflow’s ability to automate and orchestrate complex infrastructure workflows helps reduce downtime, improve resource utilization, and enhance overall operational performance.

Apache Airflow Use Cases Across Industries

The use cases discussed above are common across every industry vertical—from startups to enterprises and governments. The following section highlights five specific examples that illustrate the versatility of Airflow.

Financial Services

In the financial services industry, Apache Airflow offers robust orchestration for critical data workflows, making it a powerful tool for streamlining operations.

Airflow automates complex processes like:

• Regulatory reporting, ensuring compliance with laws like Dodd-Frank and GDPR, while reducing the risk of errors.
• Enhancing fraud detection by orchestrating complex workflows curating the data to train and retrain machine learning models to flag anomalies.
• Enabling 360-degree customer view by aggregating data from multiple sources, providing deep insights for personalized services.

Its scalability supports risk management and investment portfolio optimization, driving data-driven decision-making across financial institutions.

Retail and eCommerce

In retail and e-commerce, Apache Airflow drives operational efficiency by orchestrating data workflows that enhance customer experiences and streamline backend operations.

Key use cases include:

• Automating inventory management by integrating data from various sources, ensuring stock levels are optimized automatically.
• Powering recommendation engines and personalized marketing by orchestrating data pipelines that analyze customer behavior across multiple touchpoints.
• Improving supply chain management by coordinating data flows between vendors, logistics, and sales, ensuring accurate demand forecasting and timely deliveries.

Healthcare

In healthcare, Apache Airflow plays a crucial role in managing complex data workflows for improving patient care and operational efficiency.

Common use cases include:

• Automating the extraction and processing of medical data from EHR systems, while ensuring regulatory compliance by tracking data lineage.
• Orchestrating training of machine learning models to analyze medical imagery to aid the diagnostic process.
• Automatically gathering insights from patient data, enhancing care delivery and outcomes while reducing manual workloads for healthcare providers.

Manufacturing

In the manufacturing industry, Apache Airflow optimizes data orchestration for monitoring production, improving quality control, and streamlining supply chain operations.

Example use cases include:

• Batch or micro-batch processing of data collected from IoT sensors, ensuring efficient production and maintenance scheduling.
• Combining data from ERP systems and predictive maintenance tools. By collecting and analyzing data from specialized systems through their APIs, Airflow helps prevent equipment downtime, enhances operational efficiency, and drives innovation through automation and advanced analytics.

Gaming

In the gaming industry, Apache Airflow provides a competitive edge by powering advanced analytics, user personalization, and in-game optimizations.

Typical use cases include:

• Integrating data from various sources, including user behavior and game telemetry, allowing gaming companies to deliver targeted experiences and optimize game performance.
• Automating the processing of large volumes of data to support A/B testing of web applications and enhance fraud detection through machine learning integration.

Deep Dive: Illustrative Airflow Use Cases

There are a wealth of resources to help you get started with Apache Airflow for your specific use case. Here are just three to give you a flavor of what is available to you.

ELT with Apache Airflow and Databricks

This reference architecture and GitHub repo shows how to integrate Apache Airflow with Databricks for ELT workflows, using the Airflow Databricks provider. It includes a practical example that extracts data from CSV files in S3, loads it into a Databricks Delta Lake table and finally runs transformations on the data through Databricks jobs created and scheduled by Airflow.

By leveraging Airflow’s scheduling and orchestration, and Databricks’ compute power, you get the best of both worlds, handling large-scale data transformations requiring distributed computing while benefiting from Airflow’s complex workflow managing capabilities.

Generative AI cookbook for Apache Airflow

As state-of-the-art in AI advances, the stack of technologies needed to build an enterprise grade GenAI application is complex and rapidly evolving. Understanding how and where data orchestration integrates into the stack was the primary driver behind developing theGenerative AI cookbook for Apache Airflow.

The cookbook demonstrates how Airflow is the foundation for the reliable delivery of AI applications through six common GenAI use cases:

• Support automation
• E-commerce product discovery
• Product insight from customer reviews
• Customer churn risk analysis
• Legal document summarization and categorization
• Dynamic cluster provisioning for image creation

Each reference architecture is designed using a full stack of GenAI technologies — from embedding and inference models to vector databases, distributed compute platforms, and cloud services.

Simplify data transformations with dbt in Apache Airflow

dbt Core excels at transforming data using SQL, and remains one of the most popular tools used to create transformation pipelines. Astronomer has built Cosmos, an advanced provider package, allowing you to use Airflow’s robust orchestration and scheduling capabilities to run dbt Core.

Bringing together dbt and Airflow on the Astro managed service, you can unify your data workflows, reducing complexity and providing clear insights that improve team productivity. Through Cosmos (built by Astronomer), you gain complete visibility into your dbt tasks within Airflow, making it easier to detect and troubleshoot issues. Additionally, Astro’s advanced deployment capabilities allow you to deploy dbt code independently from Airflow DAGs, simplifying your CI/CD processes and aligning them with the needs of modern data teams.

Getting Started with Airflow

Astro is a fully managed modern data orchestration platform powered by Apache Airflow. Astro augments Airflow with enterprise-grade features to enhance developer productivity, optimize operational efficiency at scale, meet production environment requirements, and more.

Astro enables companies to place Airflow at the core of their data operations, ensuring the reliable delivery of mission-critical data pipelines and products across every use case and industry.

Watch a demo to learn more about Astro.

Frequently Asked Questions about Airflow Use Cases

Airflow and Specific Use Cases

What are the most common use cases for Airflow?

The most common use cases for Apache Airflow include:

• ETL/ELT pipelines: Automating data extraction, transformation, and loading.

• Machine learning and GenAI workflows: Orchestrating model training, evaluation, deployment, and preparing data pipelines for retrieval-augmented generation (RAG), and model governance.

• Operational analytics: Feeding data into dashboards or monitoring tools along with managing reporting, alerting, and audit workflows.

• Data warehousing: Scheduling tasks that update and manage data lakes/warehouses.

• Infrastructure management: Automating provisioning and resource management.

How can Airflow be used for data integration (ETL/ELT) processes?

Apache Airflow is ideal for automating and orchestrating ETL/ELT processes. It schedules and manages workflows that extract data from various sources, loading it into data warehouses and data lakes (e.g., Snowflake, BigQuery, Databricks) and transforming it using tools like dbt or custom scripts.

Airflow’s task dependencies ensure that steps execute in the correct order, while its UI provides visibility into pipeline performance. It also integrates with many popular data tools, making it easier to manage large-scale data integration processes efficiently.

How can Airflow be used for MLOps and Generative AI?

Apache Airflow plays a crucial role in MLOps and Generative AI by automating the end-to-end machine learning lifecycle. It orchestrates workflows like data preprocessing, model training, hyperparameter tuning, and deployment. Airflow’s flexibility allows you to use it with popular ML tools (e.g., TensorFlow, PyTorch) and ML/AI offerings of cloud platforms.

For Generative AI, Airflow can automate tasks such as creating vector embeddings for RAG applications, model retraining and deploying updated models in production, ensuring that systems stay current with new data. This results in smoother MLOps pipelines, enabling faster iteration and model scalability.

How can Airflow be used for operational analytics?

Apache Airflow is frequently used for operational analytics by leveraging it to automate data pipelines that feed insights into dashboards, reports, or apps. It integrates with various data sources (cloud services, databases, APIs) and offers advanced scheduling options to ensure that data is up-to-date and actionable. Airflow’s scheduling and monitoring features create the conditions for data workflows to run smoothly, providing the timely insights needed to optimize business operations, such as customer experience, inventory management, and data-driven decision-making.

By automating these processes, Airflow reduces manual intervention and ensures reliable, scalable analytics.

How can Airflow be used for infrastructure management?

Apache Airflow can be used for infrastructure management by automating tasks like provisioning, scaling, and monitoring cloud or on-premise resources. It integrates with tools like Kubernetes, allowing teams to automate infrastructure-as-code workflows and ensure that systems remain optimized and resilient. By scheduling and orchestrating infrastructure-related tasks, Airflow reduces manual effort, improves consistency, and enhances the scalability and reliability of infrastructure operations.

Which industries benefit the most from using Apache Airflow?

Airflow is used across every industry vertical—from startups to enterprises and governments. You can run Airflow anywhere: in the cloud or on-premise.

Astro

What is Astro? How does it differ from open-source Airflow?

Astro is a managed service for Apache Airflow offered by Astronomer. It provides additional features like easier deployment, enhanced scalability, along with deeper monitoring and observability compared to open-source Airflow. Astro simplifies the setup and maintenance of Airflow environments, offering enterprise-grade support, security, and reliability. It integrates seamlessly with various data platforms, making it easier for teams to automate and monitor workflows at scale.

In contrast, open-source Airflow requires self-hosting, configuration, and management, putting more responsibility on users to maintain their infrastructure.

Learn more about the differences between Astro and Airflow.

What are the benefits of using Astro for my Airflow deployments?

Using Astro for your Airflow deployments offers several benefits:

• Simplified setup: Quick deployment of Airflow environments without manual configuration.

• Scalability: Automatically scales resources based on workflow demand.

• Enhanced monitoring: Provides better visibility and alerting for your workflows.

• Security and compliance: Built-in security features and enterprise-grade support.

• Integration: Seamlessly connects with popular data tools and platforms.

Astro allows teams to focus on workflow development without worrying about infrastructure management, making it ideal for enterprises looking for a managed solution.

How can I get started with Astro?

You can try Astro for free here.