Airflow then and Now
This talk will cover a presentation of the tool as well as feedback from the implementation of Airflow in a banking production environment which is Société Générale. It will be the summary of a two-year experience, a storytelling of an adventure within Société Générale in order to offer an internal cloud solution based on Airflow (AirflowaaS).
In this talk, we share the lessons learnt while building a scheduler-as-a-service leveraging Apache Airflow to achieve improved stability and security for one of the largest gaming companies. The platform integrates with different data sources and meets varied SLA’s across workflows owned by multiple game studios. In particular, we present a comprehensive self-serve airflow architecture with multi-tenancy, auto-dag generation, SSO-integration with improved ease of deployment.
To be detailed
Let’s be honest about it. Many of us don’t consider data lineage to be cool. But what if lineage would allow you to write less boilerplate and less code, while at the same time make your data scientists, your auditors, your management and well everyone more happy? What if you could write DAGs that mix between tasks based and data based?
I have been one of the engineers at Nieslen Digital leading our migration of ETLs to Airflow on Kubernetes. This talk will teach you the ins and outs of Airflow on Kubernetes, from deploying Airflow to best practices for DAG development in a containerized environment. Airflow on Kubernetes will ease your Airflow DAG development, minimize its infrastructure costs, avoid wasted resources, and providing tasks with the optimal infrastructure to run on all through Kubernetes features within Airflow.
With data becoming the new oil, Data Reliability Engineering(DRE) has been buzzing all around in all leading Fin Tech industries. And in this space, working on big data/data science necessarily deals with building pipelines starting from data exploration to enrichment at scheduled intervals. While simple scripts are easy to create it gets cumbersome to manage when we want to build a resilient data pipe with intelligent failure handling for unexpected long running/short lived dependent tasks. Apa
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 execution environment, allowing stakeholders to self deploy jobs without cross team dependencies on top of the unlimited scale of Kubernetes.
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.
What's new in Airflow 2.0
To share the experience of adopting Airflow as the next generation of workflow system at pinterest, distributing the worker loads k8s, load sharding/partitioning via multiple schedulers, transparent existing user flow migration and more .
Astronomer is focused on improving Airflow’s user experience through the entire lifecycle — from authoring + testing DAGs, to building containers and deploying the DAGs, to running and monitoring both the DAGs and the infrastructure that they are operating within — with an eye towards increased security and governance as well.
Deploying bad DAGs to your airflow environment can wreak havoc. This talk provides an opinionated take on a mono repo structure for GCP data pipelines leveraging BigQuery, Dataflow and a series of CI tests for validating your Airflow DAGs before deploying them to Cloud Composer.
One of the significant challenges in scaling Airflow at an organization is the number of qualified developers fluent in Python. To speed the development of complex pipelines we developed a DAG authoring and editing tool for Airflow. Installed as a plugin, this tool allows users to author DAGs compose existing operators and hooks with virtually no Python experience.
A live demo of the tool and accompanying code.
Airflow does not currently have an explicit way to declare messages passed between tasks in a DAG. XCom are available but are hidden in execution functions inside the operator. AIP-31 proposes a way to make this message passing explicit in the DAG file and make it easier to reason about your DAG behaviour.
In this talk we will review how Airflow helped create a tool to detect data anomalies. Leveraging Airlfow for process management, database interoperability, and authentication created an easy path forward to achieve scale, decrease the development time and pass security audits. While Airflow is generally looked at as a solution to manage data pipelines, integrating tools with Airlfow can also speed up development of those tools.
This talk describes how Airflow is utilized in an Autonomous driving project, originating from Munich - Germany. We describe the Airflow setup, what challenges we encountered and how we maneuvered to achieve a distributed and highly scalable Airflow setup.
Learn how Devoted Health went from cron jobs to to Airflow deployment Kubernetes using a combination of open source and internal tooling.
Go over the yesterday, today and tomorrow for Airflow in Airbnb. Share our learnings and vision in Airflow core and around Airflow in its eco system.
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.
This talk will guide you trough internals of the official Production Docker Image of Airflow. It will show you the foreseen use cases for it and how to use it in conjunction with the Official Helm Chart to make your own deployments.
In search of a better, modern, simplistic method of managing ETL's processes and merging them with various AI and ML tasks, we landed on Airflow. We envisioned a new user friendly interface that can leverage dynamic DAG's and reusable components to build an ETL tool that requires virtually no training.
We’re using airflow for almost two years now and scaled it from two users to 8 teams.
We would like to share our story, how we reason about the reliability of our data pipelines.
We will tell:
How are we establishing a reliable review process on AirFlow?
How we’re using multiple-airflow configuration to migrate from our DC to cloud and to reuse the production data in acceptance wherever possible.
How do we use data versioning to make sure that data is up-to-date throughout the pipeline?
Databand is a data engineering-focused observability and monitoring solution. We built the solution for modern data teams that need to guarantee the reliability and health of their data products.
BigQuery is GCP's serverless, highly scalable and cost-effective cloud data warehouse that can analyze petabytes of data at super fast speeds. Amazon S3 is one of the oldest and most popular cloud storage offerings. Folks with data in S3 often want to use BigQuery to gain insights into their data. Using Apache Airflow, they can build pipelines to seamlessly orchestrate that connection. In this talk, Emily and Leah will walk through how they created an easily configurable pipeline to extract data
To improve automation of data pipelines, I propose a universal approach to ELT pipeline that optimizes for data integrity, extensibility, and speed to delivery. The workflow is built using open source tools and standards like Apache Airflow, Singer, Great Expectations, and DBT.
How do you ensure your workflows work before deploying to production? In this talk I'll go over various ways to assure your code works as intended - both on a task and a DAG level. In this talk I will cover:
Engaging with a new community is a common experience in OSS development.
There are usually expectations held by the project about the contributor's exposure
to the community, and by the contributor about interactions with the community.
When these expectations are misaligned, the process is strained. In this talk,
I'll discuss a real life experience that required communication,
persistence, and patience to ultimately lead to a positive outcome.
In this talk I will showcase how to use the newly released Airflow Backport Providers.
Some of the topics we will cover are:
How to install them in Airflow 1.10.x
How to install them in Composer
How to migrate one or more DAG from using legacy to new providers.
Known bugs and fixes.
At Bluevine we use Airflow to drive our ML platform. In this talk, I'll present the challenges and gains we had at transitioning from a single server running python scripts with cron to a full blown Airflow setup. This includes: supporting multiple Python versions, event driven DAGs, performance issues and more!
Working with Airflow is no breeze. For three years we at LOVOO, a market-leading dating app, have been using the Google Cloud managed version of Airflow, a product we’ve been familiar with since its Alpha release. We took a calculated risk and integrated the Alpha into our product, and, luckily, it was a match. Since then, we have been leveraging this software to build out not only our data pipeline, but also boost the way we do analytics and BI.
Data Infrastructures look differently between small, mid, and large sized companies. Yet, most content out there is for large and sophisticated systems. And almost none of it is on migrating a legacy, on-prem, databases over to the cloud.
We'll begin with the fundamentals of building a modern Data Infrastructure from the ground up through a hierarchy of needs. The hierarchy has a (subjective) 7 levels, ranging from Automation to Data Streaming.
Cross-DAG dependency may reduce cohesion in data pipelines and, without having an explicit solution in Airflow or in a third-party plugin, those pipelines tend to become complex to handle. That is the reason we, at QuintoAndar, have created an intermediate DAG to handle relationships across data pipelines called Mediator, in order for them to be scalable and maintainable by any team.
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.
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
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.