Looking for a way to streamline your data workflows and master the art of orchestration? As we navigate the complexities of modern data engineering, Airflow’s dynamic workflow and complex data pipeline dependencies are starting to become more and more common nowadays. In order to empower data engineers to exploit Airflow as the main orchestrator, Airflow Datasets can be easily integrated in your data journey. This session will showcase the Dynamic Workflow orchestration in Airflow and how to manage multi-DAGs dependencies with Multi-Dataset listening. We’ll take you through a real-time data pipeline with Pub/Sub messaging integration and dbt in Google Cloud environment, to ensure data transformations are triggered only upon new data ingestion, moving away from rigid time-based scheduling or the use of sensors and other legacy ways to trigger a DAG.

In the realm of data engineering, machine learning pipelines and using cloud and web services there is a huge demand for orchestration technologies. Apache Airflow belongs to the most popular orchestration technologies or even is the most popular one. In this presentation we are going to focus these aspects of Airflow that make it so popular and whether it became the orchestration industry standard.

Does your organization feel like the responsibility to write Airflow DAGs, handle the Airflow infrastructure administration, debug failing tasks, and keep up with new features and best practices is too much for too few people? Perhaps you only have one data team that owns all of that; or you have too many teams that have too many permissions into other teams’ DAGs. The topic of this talk is how Rakuten Kobo enables self-service for various teams within its organization to build their own DAGs in Airflow. The talk will include how we delineate the Airflow responsibilities of various teams, build guard rails for new Airflow developers, how different teams automatically have permissions required for their “own” DAGs (but not others), the unique responsibilities of Operations and Data Engineering teams, and how it is done in a scalable manner. Maybe you’ll be inspired to make changes in your own organization, or have some tips of your own to share! Depending on questions, we could discuss some of the technical details as well.

Every data team out there is being asked from their business stakeholders about Generative AI. Taking LLM centric workloads to production is not a trivial task. At the foundational level, there are a set of challenges around data delivery, data quality, and data ingestion that mirror traditional data engineering problems. Once you’re past those, there’s a set of challenges related to the underlying use case you’re trying to solve. Thankfully, because of how Airflow was already being used at these companies for data engineering and MLOps use cases, it has become the defacto orchestration layer behind many GenAI use cases for startups and Fortune 500s. This talk will be a tour of various methods, best practices, and considerations used in the Airflow community when taking GenAI use cases to production. We’ll focus on 4 primary use cases; RAG, fine tuning, resource management, and batch inference and take a walk through patterns different members in the community have used to productionize this new, exciting technology.

In the realm of data engineering, there is a prevalent tendency for professionals to develop similar Directed Acyclic Graphs (DAGs) to manage analogous tasks. Leveraging Dag Params presents an effective strategy for mitigating redundancy within these DAGs. Moreover, the utilization of Dag Params facilitates seamless enforcement of user inputs, thereby streamlining the process of incorporating validations into the DAG codebase.

While Airflow is widely known for orchestrating and managing workflows, particularly in the context of data engineering, data science, ML (Machine Learning), and ETL (Extract, Transform, Load) processes, its flexibility and extensibility make it a highly versatile tool suitable for a variety of use cases beyond these domains. In fact, Cloudflare has publicly shared in the past an example on how Airflow was leveraged to build a system that automates datacenter expansions. In this talk, I will share a few more of our use cases beyond traditional data engineering, demonstrating Airflow’s sophisticated capabilities for orchestrating a wide variety of complex workflows, and discussing how Airflow played a crucial role in building some of the highly successful autonomous systems at Cloudflare, from handling automated bare metal server diagnostics and recovery at scale, to Zero Touch Provisioning that is helping us accelerate the roll out of inference-optimized GPUs in 150+ cities in multiple countries globally.