talk-data.com
People (1 result)
Activities & events
| Title & Speakers | Event |
|---|---|
|
Building Lineage Observability at Scale
2025-07-03 · 16:30
Jeroen Schmidt
– Senior Data Engineer
@ Booking.com
Observability in data workflows often stops at logs and metrics, leaving data lineage as a blind spot. At Booking, we set out to change that by treating lineage as a core observability layer. In this talk, I'll walk through how we integrated lineage tracking into our Airflow ecosystem, what metadata we capture, and how we surface it to users in a meaningful way. I'll also share how lineage data helps us debug failures, detect unexpected changes, and ensure compliance. You'll leave with a practical view of what it takes to make lineage not just visible, but actionable. |
|
|
Building multi-tenant Airflow at scale: From Chaos to Control
2025-07-03 · 16:00
Omid Karami
– Software Engineer
@ Booking.com
Running Airflow at scale for thousands of workflows across multiple teams introduces challenges around standardization, governance, and isolation. At Booking, we've built a multi-tenant Airflow platform that serves over 4,000 workflows using a custom DSL defined in workflow.yaml files. In this talk, I'll show how we use automated DAG generation to bring structure to complexity, how we achieved horizontal scalability by decoupling orchestration from execution, and how reusable step templates help us enforce governance--without sacrificing workflow isolation. You'll leave with a blueprint for taming Airflow at scale. |
|
|
How booking workflows use data asset to track data evolution
2025-07-03 · 15:00
Anirban Saha
– Technical Product Manager
@ Booking.com
Traditionally, managing the lifecycle of data in workflows at Booking involved ad hoc tracking and custom logic to handle data changes. With the adoption of data assets, we now have a standardized way to represent, version, and evolve data over time. In this talk, I'll introduce how data assets are implemented at Booking, how versioning is handled under the hood, and how our workflows are built to consume and respond to these evolving assets. I'll close with a real-world example that shows how data assets help us ensure consistency and traceability in a complex production workflow. |
|
|
Deepdive into event-driven scheduling with Airflow 3
2025-07-03 · 14:30
Bas Harenslak
– Staff Architect
@ Astronomer
Historically Airflow was only capable of time-based scheduling, where a DAG would run at certain times. For data updates at varying times, such as an external party delivering data to an S3 bucket, that meant having to run a DAG and continuously poll for updates. Airflow 3 introduces event-driven scheduling that enables you to trigger DAGs based on such updates. In this talk I'll demonstrate how this changes your DAG's code and how this works internally in Airflow. Lastly, I'll demonstrate a practical use case that leverages Airflow 3's event-driven scheduling. |
|
|
Data Pipelines with Apache Airflow
2021-05-09
Julian de Ruiter
– author
,
Bas Harenslak
– author
A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. About the Technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the Book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You’ll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline’s needs. What's Inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the Reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the Authors Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Quotes An Airflow bible. Useful for all kinds of users, from novice to expert. - Rambabu Posa, Sai Aashika Consultancy An easy-to-follow exploration of the benefits of orchestrating your data pipeline jobs with Airflow. - Daniel Lamblin, Coupang The one reference you need to create, author, schedule, and monitor workflows with Apache Airflow. Clear recommendation. - Thorsten Weber, bbv Software Services AG By far the best resource for Airflow. - Jonathan Wood, LexisNexis |
O'Reilly Data Engineering Books
|