This session will detail Allegro’s, a leading e-commerce company in Poland, journey with Apache Airflow. It will chart our evolution from a custom, on-premises Airflow-as-a-Service solution through a significant expansion to over 300 Cloud Composer instances in Google Cloud, culminating in Airflow becoming the core of our data processing. We orchestrate over 64,000 regular tasks spanning over 6,000 active DAGs on more than 200 Airflow instances. From feeding business-supporting dashboards, to managing main data marts, and handling ML pipelines, and more. We will share our practical experiences, lessons learned, and the strategies employed to manage and scale this critical infrastructure. Furthermore, we will introduce our innovative economy-of-share approach for providing ready-to-use Airflow environments, significantly enhancing both user productivity and cost efficiency.

During this workshop you are going to learn the latest features published within Cloud Composer which is a managed service for Apache Airflow on Google Cloud Platform.

During this sessions audience is going to learn about newest feature of managed Airflow offering provided by Google Cloud. If you would like to operate Airflow at scale or in regulated environments then this session is for you.

We have a similar pattern of DAGs running for different data quality dimensions like accuracy, timeliness, & completeness. To do this again and again, we would be duplicating and potentially introducing human error while doing copy paste of code or making people write same code again. To solve for this, we are doing few things: Run DAGs via DagFactory to dynamically generate DAGs using just some YAML code for all the steps we want to run in our DQ checks. Hide this behind a UI which is hooked to github PR open step, now the user just provides some inputs or selects from dropdown in UI and a YAML DAG is generated for them. This highlights the potential for DAGFactory to hide Airflow Python code from users and make it more accessible to Data Analysts and Business Intelligence along with normal Software Engg, along with reducing human error. YAML is the perfect format to be able to generate code, create a PR and DagFactory is the perfect fir for that. All of this is running in GCP Cloud Composer.

Discover how Apache Airflow powers scalable ELT pipelines, enabling seamless data ingestion, transformation, and machine learning-driven insights. This session will walk through: Automating Data Ingestion: Using Airflow to orchestrate raw data ingestion from third-party sources into your data lake (S3, GCP), ensuring a steady pipeline of high-quality training and prediction data. Optimizing Transformations with Serverless Computing: Offloading intensive transformations to serverless functions (GCP Cloud Run, AWS Lambda) and machine learning models (BigQuery ML, Sagemaker), integrating their outputs seamlessly into Airflow workflows. Real-World Impact: A case study on how INTRVL leveraged Airflow, BigQuery ML, and Cloud Run to analyze early voting data in near real-time, generating actionable insights on voter behavior across swing states. This talk not only provides a deep dive into the Political Tech space but also serves as a reference architecture for building robust, repeatable ELT pipelines. Attendees will gain insights into modern serverless technologies from AWS and GCP that enhance Airflow’s capabilities, helping data engineers design scalable, cloud-agnostic workflows.

This session will dive deep into leveraging the robust logging and audit capabilities of Google Cloud Platform, Cloud Composer and Apache Airflow to establish a fully transparent and verifiable data orchestration layer. We’ll demonstrate how to track and attribute every change—from environment configuration to individual task execution—essential for meeting stringent enterprise governance, compliance, and auditing requirements.

Traditional time-based scheduling in Airflow can lead to inefficiencies and delays. With Airflow 3.0, we can now leverage native event-driven DAG execution, enabling workflows to trigger instantly when data arrives—eliminating polling-based sensors and rigid schedules. This talk explores real-time orchestration using Airflow 3.0 and Google Cloud Pub/Sub. We’ll showcase how to build an event-driven pipeline where DAGs automatically trigger as new data lands, ensuring faster and more efficient processing. Through a live demo, we’ll demonstrate how Airflow listens to Pub/Sub messages and dynamically triggers dbt transformations only when fresh data is available. This approach improves scalability, reduces costs, and enhances orchestration efficiency. Key Takeaways: How event-driven DAGs work vs. traditional scheduling, Best practices for integrating Airflow with Pub/Sub,Eliminating polling-based sensors for efficiency,Live demo: Event-driven pipeline with Airflow 3.0, Pub/Sub & dbt. This session will showcase how Airflow 3.0 enables truly real-time orchestration.

At Trendyol, Turkey’s leading e-commerce company, Apache Airflow powers our task orchestration, handling DAGs with 500+ tasks, complex interdependencies, and diverse environments. Managing on-prem Airflow instances posed challenges in scalability, maintenance, and deployment. To address these, we built TaskHarbor, a fully managed orchestration platform with a hybrid architecture—combining Airflow on GKE with on-prem resources for optimal performance and efficiency. This talk covers how we: Enabled seamless DAG synchronization across environments using GCS Fuse. Optimized workload distribution via GCP’s HTTPS & TCP Load Balancers. Automated infrastructure provisioning (GKE, CloudSQL, Kubernetes) using Terraform. Simplified Airflow deployments by replacing Helm YAML files with a custom templating tool, reducing configurations to 10-15 lines. Built a fully automated deployment pipeline, ensuring zero developer intervention. We enhanced efficiency, reliability, and automation in hybrid orchestration by embracing a scalable, maintainable, and cloud-native strategy. Attendees will obtain practical insights into architecting Airflow at scale and optimizing deployments.

Apache Airflow 3 is a new state-of-the-art version of Airflow. For many users who plan to adopt Airflow 3 it’s important to understand how Airflow 3 behaves from performance perspective compared to Airflow 2. This presentation is going to present performance results for various Airflow 3 configurations and provides potential Airflow 3 adopters good understanding of its performance. The reference Airflow 3 configuration will be using Kubernetes cluster as a compute layer, PostgreSQL as Airflow Database and would be performed on Google Cloud Platform. Performance tests will be performed using community version of performance tests framework and there might be references to Cloud Composer (managed service for Apache Airflow). The tests will be done in production-grade configurations that might be good references for Airflow community users. Users will be provided with comparison of Airflow 3 and Airflow 2 from performance standpoint Users also will learn how to optimize Airflow scheduler performance by understanding DAG file processing, task scheduling and configuring Scheduler to run tens of thousands of DAGs/tasks in Airflow 3

The journey from ML model development to production deployment and monitoring is often complex and fragmented. How can teams overcome the chaos of disparate tools and processes? This session dives into how Apache Airflow serves as a unifying force in MLOps. We’ll begin with a look at the broader MLOps trends observed by Google within the Airflow community, highlighting how Airflow is evolving to meet these challenges and showcasing diverse MLOps use cases – both current and future. Then, Priceline will present a deep-dive case study on their MLOps transformation. Learn how they leveraged Cloud Composer, Google Cloud’s managed Apache Airflow service, to orchestrate their entire ML pipeline end-to-end: ETL, data preprocessing, model building & training, Dockerization, Google Artifact Registry integration, deployment, model serving, and evaluation. Discover how using Cloud Composer on GCP enabled them to build a scalable, reliable, adaptable, and maintainable MLOps practice, moving decisively from chaos to coordination. Cloud Composer (Airflow) has served as a major backbone in transforming the whole ML experience in Priceline. Join us to learn how to harness Airflow, particularly within a managed environment like Cloud Composer, for robust MLOps workflows, drawing lessons from both industry trends and a concrete, successful implementation.