talk-data.com

As a data engineer, backfilling data is an important part of your day-to-day work. But, backfilling interdependent DAGs is time-consuming and often associated with an unpleasant experience. For example, let’s say you were tasked with backfilling a few months worth of data. You’re given the start and end date for the backfill that will be used to run an ad-hoc backfilling script that you have painstakingly crafted locally on your machine. As you sip your morning coffee, you kick off the backfilling script, hoping it’ll work, and think to yourself, there must be a better way. Yes, there is, and collecting DAG lineage metadata would be a great start! In this talk, Willy Lulciuc will briefly introduce you to how backfills are handled in Airflow, then discuss how DAG lineage metadata stored in Marquez can be used to automate backfilling DAGs with complex upstream and downstream dependencies.

OpenLineage is an open standard for metadata and lineage collection designed to instrument jobs as they are running. The standard has become remarkably adept at understanding the lifecycle of data within an organization. Additionally, Airflow lets you make use of OpenLineage with a convenient integration. Gathering data lineage has never been easier. In this talk, we’ll provide an update-to-date report on OpenLineage features and the Airflow integration – essential information for data governance architects & engineers.

Imagine if you could chain together SQL models using nothing but python, write functions that treat Snowflake tables like dataframes and dataframes like SQL tables. Imagine if you could write a SQL airflow DAG using only python or without using any python at all. With Astro SDK, we at Astronomer have gone back to the drawing board around fundamental questions of what DAG writing could look like. Our goal is to empower Data Engineers, Data Scientists, and even the Business Analysts to write Airflow DAGs with code that reflects the data movement, instead of the system configuration. Astro will allow each group to focus on producing value in their respective fields with minimal knowledge of Airflow and high amounts of flexibility between SQL or python-based systems. This is way beyond just a new way of writing DAGs. This is a universal agnostic data transfer system. Users can run the exact same code against different databases (snowflake, bigquery, etc.) and datastores (GCS, S3, etc.) with no changes except to the connection IDs. Users will be able to promote a SQL flow from their dev postgres to their prod snowflake with a single variable change. We are ecstatic to reveal over eight months of work around building a new open-source project that will significantly improve your DAG authoring experience!

Broken data is costly, time-consuming, and nowadays, an all-too-common reality for even the most advanced data teams. In this talk, I’ll introduce this problem, called “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate — and discuss how to eliminate it in your data ecosystem with end-to-end data observability. Drawing corollaries to application observability in software engineering, data observability is a critical component of the modern DataOps workflow and the key to ensuring data trust at scale. I’ll share why data observability matters when it comes to building a better data quality strategy and highlight tactics you can use to address it today.

This workshop is sold out By attending this workshop, you will learn how you can become a contributor to the Apache Airflow project. You will learn how to setup a development environment, how to pick your first issue, how to communicate effectively within the community and how to make your first PR - experienced committers of Apache Airflow project will give you step-by-step instructions and will guide you in the process. When you finish the workshop you will be equipped with everything that is needed to make further contributions to the Apache Airflow project.

This workshop is sold out Hands on workshop showing how easy it is to deploy Airflow in a public Cloud. Workshop consists of 3 parts: Setting up Airflow environment and CI/CD for DAG deployment Authoring a DAG Troubleshoot Airflow DAG/Task execution failures This workshop will be based on Cloud Composer ( https://cloud.google.com/composer ) This workshop is mostly targeted at Airflow newbies and users who would like to learn more about Cloud Composer and how to develop DAGs using Google Cloud Platform services like BigQuery, Vertex AI, Dataflow.

“Why is my data missing?” “Why didn’t my Airflow job run?” “What happened to this report?” If you’ve been on the receiving end of any of these questions, you’re not alone. As data pipelines become increasingly complex and companies ingest more and more data, data engineers are on the hook for troubleshooting where, why, and how data quality issues occur, and most importantly, fixing them so systems can get up and running again. In this talk, Francisco Alberini, Monte Carlo’s first product hire, discusses the three primary factors that contribute to data quality issues and how data teams can leverage Airflow, dbt, and other solutions in their arsenal to conduct root cause analysis on their data pipelines.

This talk is a walk through throug a number of ways maintainers of open-source projects (for example Airflow) can improve the communication with their users by exercising empathy. This subject is often overlooked in the cirriculum of average developer and contributor, but one that can make or break the product you developed, simply because it will become more approachable for users. Maintainers often forget or simply do not realize how many assumptions they have in their head. There are a number of techniques maintainers can use to improve it. This talk will walk through a number of examples (from Airflow and other projects), reasoning and ways how communication between maintainers and users can be improved - in the code, documentation, communication but also with involving and engaging the users they are commmunicating with, as more often than not - the users might be of great help when it comes to communication with them - if only asked. This talk is for both - maintainers and users, as I consider communication between users and maintainers two way street.

In this talk, I am going to share things that I learned while contributing to Apache Airflow. I am an Outreachy Intern for Apache Airflow. I made my first contribution to Open Source in the Apache Airflow project. I will also add a short description about myself and my experience working in Software Engineering and how i needed help in contributing to open source and ended up as an Intern for Outreachy. I also like to share about my first contribution towards Apache Airflow in its doc and how much confidence it gave me to continue contributing to it. Key things that I learned when contributing to Apache Airflow are: Clear communication in written form is very powerful. Code is not an asset and don’t worry about throwing it away. Don’t feel shy about asking questions. Open Source is a rich ecosystem where each projects help each other and thrive. Trivial things became no more trivial to me. While the above things are overall learning about open source contribution, I had specific important learnings for me which include writing unit tests, got to communicate with developers across the globe, improved written style of communication, knowing about many python libraries, understanding the CI pipeline.

Recently there has been much discussion around data monitoring, particularly in regards to reducing time to mitigate data quality problems once they’ve been detected. The problem with reactive or periodic monitoring as the de-facto standard for maintaining data quality is that it’s expensive. By the time a data problem has been identified, it’s effects may have been amplified across a myriad of downstream consumers, leaving you (a data engineer) with a big mess to clean-up. In this talk, we will present an approach for proactively addressing data quality problems using orchestration based on a central metadata graph. Specifically, we will walk through use cases highlighting how the open source metadata platform DataHub can enable proactive pipeline circuit-breaking by serving as the source of truth for both technical & semantic health status for a pipeline’s data dependencies. We’ll share practical recipes for how three powerful open source projects can be combined to build reliable data pipelines: Great Expectations for generating technical health signals in the form of assertion results on datasets, DataHub for providing a semantic identity for a dataset, including ownership, compliance, & lineage, and Airflow for orchestration.

Automatic Speech Recognition is quite a compute intensive task, which depends on complex Deep Learning models. To do this at scale, we leveraged the power of Tensorflow, Kubernetes and Airflow. In this session, you will learn about our journey to tackle this problem, main challenges, and how Airflow made it possible to create a solution that is powerful, yet simple and flexible.

This session will talk about the awesome new features the community has built that would be part of Airflow 2.3. Highlights: Dynamic Task Mapping DB. Downgrades Pruning old DB records Connections using JSON UI Improvements

With a small amount of Cloud Build automation and the use of GitHub version control, your Airflow DAGs will always be tested and in sync no matter who is working on them. Leah will walk you through a sample CICD workflow for keeping your Airflow DAGs tested and in sync between environments and teammates.

This talk will cover the challenges we can face managing a large number of Airflow instances on private environment. Monitoring and metrics layers for production environment. Collecting and customizing logs. Resource consumption and green IT. Providing support for users and shared responsibility. Pain points

We the Data Engineering Team here at WB Games implemented an internal Redshift Loader DAG(s) on Airflow that allow us to ingest data in near real-time at scale into Redshift, taking into account variable load on the DB and been able to quickly catch up data loads in case of various DB outages or high usage scenarios. Highlights: Handle any type of Redshift outages and system delays dynamically between multiple sources(S3) to sinks(Redshift). Auto tuning data copies for faster data backfill in case of delay without overwhelming commit queue. Supports schema evolution on Game data dynamically. Maintain data quality to ensure we do not create data gaps or dupes. Provide embedded custom metrics for deeper insights and anomaly detection. Airflow config based Declarative Dag implementation.

Two years after starting our Airflow adoption, we’re running over 10,000 DAGs in production. On this journey we’ve learned a lot about Airflow management and stewardship and developed some unique tools to help us scale. We’re excited to share our experience and some of the lessons we’ve picked up along the way. In this talk we’ll cover: The history of Airflow at Shopify Our infrastructure and architecture Custom tools and procedures we’ve adopted to keep Airflow running smoothly and our users happy

Testing is an important part of the DataOps life cycle, giving teams confidence in the integrity of their data as it moves downstream to production systems. But what happens when testing doesn’t catch all of your bad data and “unknown unknown” data quality issues fall through the cracks? Fortunately, data engineers can apply a thing or two from DevOps best practices to tackle data quality at scale with circuit breakers, a novel approach to stopping bad data from actually entering your pipelines in the first place. In this talk, Prateek Chawla, Founding Team Member and Technical Lead at Monte Carlo, will discuss what circuit breakers are, how to integrate them with your Airflow DAGs, and what this looks like in practice. Time permitting, Prateek will also walk through how to build and automate Airflow circuit breakers across multiple cascading pipelines with Python and other common tools.

This talk tells the story of how we have approached data and analytics as a startup at Preset and how the need for a data orchestrator grew over time. Our stack is (loosely) Fivetran/Segment/dbt/BigQuery/Hightouch, and we finally got to a place where we suffer quite a bit from not having an orchestrator and are bringing in Airflow to address our orchestration needs. This talk is about how startups approach solving data challenges, the shifting role of the orchestrator in the modern data stack, and the growing need for an orchestrator as your data platform becomes more complex.

Needing to trigger DAGs based on external criteria is a common use case for data engineers, data scientists, and data analysts. Most Airflow users are probably aware of the concept of sensors and how they can be used to run your DAGs off of a standard schedule, but sensors are only one of multiple methods available to implement event-based DAGs. In this session, we’ll discuss different ways of implementing event-based DAGs using Airflow 2 features like the API and deferrable operators, with a focus on how to determine which method is the most efficient, scalable, and cost-friendly for your use case.