Identify issues in a fraction of the time and streamline root cause analysis for your DAGs. Airflow is the leading orchestration platform for data engineers. But when running Airflow at production scale, many teams have bigger needs for monitoring jobs, creating the right level of alerting, tracking problems in data, and finding the root cause of errors. In this talk we will cover our suggested approach to gaining Airflow observability so that you have the visibility you need to be productive. What is observability? The capability of monitoring and analyzing event logs, along with KPIs and other data, that yields actionable insights. In the data engineering context, observability is crucial for finding problems in jobs and data before those problems impact data consumers downstream. It’s a particularly difficult challenge because of the different platforms data engineers use (Airflow, Spark, Kubernetes, etc.) and the complicated life cycle of data pipeline CI/CD. In the session, we will do a deep dive into the visibility gaps your team might face running production-scale Airflow. We will walk through a typical day in the life of finding errors in DAGs, offer best practices, and discuss open source tools you can use to extend Airflow for observability and robust monitoring. We will use standard Airflow DAG examples to guide the presentation.

While Airflow is a central product for data engineering teams, it’s usually one piece of a bigger puzzle. The vast majority of teams use Airflow in combination with other tools like Spark, Snowflake, and BigQuery. Making sure pipelines are reliable, detecting issues that lead to SLA misses, and identifying data quality problems requires deep visibility into DAGs and data flows. Join this session to learn how Databand’s observability system makes it easy to monitor your end-to-end pipeline health and quickly remediate issues. This is a sponsored talk, presented by Databand .

Being a pioneer for the past 25 years, SONY PlayStation has played a vital role in the Interactive Gaming Industry. Over 100+ million monthly active users, 100+ million PS-4 console sales along with thousands of game development partners across the globe, big-data problem is quite inevitable. This presentation talks about how we scaled Airflow horizontally which has helped us building a stable, scalable and optimal data processing infrastructure powered by Apache Spark, AWS ECS, EC2 and Docker. Due to the demand for processing large volumes of data and also to meet the growing Organization’s data analytics and usage demands, the data team at PlayStation took an initiative to build an open source big data processing infrastructure where Apache Spark in Python as the core ETL engine. Apache Airflow is the core workflow management tool for the entire eco system. We started with an Airflow application running on a single AWS EC2 instance to support parallelism of 16 with 1 scheduler and 1 worker and eventually scaled it to a bigger scheduler along with 4 workers to support a parallelism of 96, DAG concurrency of 96 and a worker task concurrency of 24. Containerized all the services on AWS ECS which gave us an ability to scale Airflow horizontally.

Financial Times is increasing its digital revenue by allowing business people to make data-driven decisions. Providing an Airflow based platform where data engineers, data scientists, BI experts and others can run language agnostic jobs was a huge swing. One of the most successful steps in the platform’s development was building our own execution environment, allowing stakeholders to self deploy jobs without cross team dependencies on top of the unlimited scale of Kubernetes. In this talk we share how we have integrated and extended Airflow at Financial Times. The main topics we will cover include: Providing team level security isolation Removing cross team dependencies Creating execution environment for independently creating and deploying R, Python, JAVA, Spark, etc jobs Reducing latency when sharing data between task instances Integrating all these features on top of Kubernetes

This talk discusses how to build an Airflow based data platform that can take advantage of popular ML tools (Jupyter, Tensorflow, Spark) while creating an easy-to-manage/monitor As the field of data science grows in popularity, companies find themselves in need of a single common language that can connect their data science teams and data infrastructure teams. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports. This talk will discuss how to build an Airflow based data platform that can take advantage of popular ML tools (Jupyter, Tensorflow, Spark) while creating an easy-to-manage/monitor ecosystem for data infrastructure and support team. In this talk, we will take an idea from a single-machine Jupyter Notebook to a cross-service Spark + Tensorflow pipeline, to a canary tested, production-ready model served on Google Cloud Functions. We will show how Apache Airflow can connect all layers of a data team to deliver rapid results.

At Nielsen Identity Engine, we use Spark to process 10’s of TBs of data. Our ETLs, orchestrated by Airflow, spin-up AWS EMR clusters with thousands of nodes per day. In this talk, we’ll guide you through migrating Spark workloads to Kubernetes with minimal changes to Airflow DAGs, using the open-sourced GCP Spark-on-K8s operator and the native integration we recently contributed to the Airflow project.

How do you create fast and painless delivery of new DAGs into production? When running Airflow at scale, it becomes a big challenge to manage the full lifecycle around your pipelines; making sure that DAGs are easy to develop, test, and ship into prod. In this talk, we will cover our suggested approach to building a proper CI/CD cycle that ensures the quality and fast delivery of production pipelines. CI/CD is the practice of delivering software from dev to prod, optimized for fast iteration and quality control. In the data engineering context, DAGs are just another piece of software that require some form of lifecycle management. Traditionally, DAGs have been thought of as relatively static, but the new wave of analytics and machine learning efforts require more agile DAG development, in line with how agile software engineering teams build and ship code. In this session, we will dive into the challenges of building CI/CD cycles for Airflow DAGs. We will focus on a pipeline that involves Apache Spark as an extra dimension of real-world complexity, walking through a typical flow of DAG authoring, debugging, and testing, from local to staging to prod environments. We will offer best practices and discuss open-source tools you can use to easily build your own smooth cycle for Airflow CI/CD.