In this talk we will share some of the lessons we have learned after using Airflow for a couple of years and growing from 2 users to 8 teams. We cover: establishing a reliable review process on AirFlow, managing multiple Airflow configurations, data versioning.

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. In this talk we walk you through some current UX challenges, an overview of how the Astronomer platform addresses the major challenges, and also provide sneak peek of the things that we’re working on in the coming months to improve Airflow’s user experience. This is a sponsored talk, presented by Astronomer .

Bolke and Maxime tell us about past on current time of Apache Airflow.

A team of core committers explain what is coming to Airflow 2.0.

In this talk, colleagues from Airbnb, Twitter and Lyft share details about how they are using Apache Airflow to power their data pipelines. Slides: Tao Feng (Lyft) Dan Davydov (Twitter)

Gris Cuevas shares some statistics about the state of D&I at the Apache Software Foundation and also the initiative the foundation is taking to make projects more diverse and inclusive. Then, Aizhamal shares her own journey on becoming an open source contributor, and dives into project specific initiatives that help Apache Airflow to be one of the most sustainable projects in open source.

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.

In this talk Anita showcases 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.

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.

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 the contemporary world security is important more than ever - Airflow installations are no exception. Google Cloud Platform and Cloud Composer offer useful security options for running your DAGs and tasks in a way so you effectively can manage a risk of data exfiltration and access to the system is limited. This is a sponsored talk, presented by Google Cloud .

In this talk, we share the lessons learned 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. Within Electronic Arts, to provide scheduler-as-a-service and to support hundreds of thousands of execution workflows, each team requires an isolated environment with access to a central data lake containing several petabytes of anonymized player and game metrics. Leveraging Airflow, each team is provided a private code repository and namespace with which they can deploy their DAGs at their own behest. To support agile development cycles, a private testing sandbox and auto-deployment to an isolated multi-tenant airflow platform has been made available to game studios. In production, a single dockerized airflow deployment on Kubernetes is utilized to ensure highly availability and single-step deployment. Custom SSO-integration and RBAC-based operator and sensor whitelisting allows for secure logical isolation. In addition, providing dynamic DAG instantiation capability helps address varied SLA’s during game launch seasons that are staggered through a financial year.

Scribd is migrating its data pipeline from an in house system to Airflow. It’s a one big giant data pipeline consisting of more than 1,500 tasks. In this talk, I would like to share couple best practices on setting up a cloud native Airflow deployment in AWS. For those who are interested in migrating a non-trivial data pipeline to Airflow, I will also share how Scribd plans and executes the migration. Here are some of the topics that will be covered: How to setup a highly available Airflow cluster in AWS using both ECS and EKS with Terraform. How to manage Airflow DAGs across multiple git repositories. How we manage Airflow variables using a custom Airflow Terraform provider. Best practices on monitoring multiple Airflow clusters with Datadog and Pagerduty. How to Airflow to make it feature parity with Scribd’s in house orchestration system. How to plan and execute non-trivial data pipeline migrations. We transcompiled internal DSL to Airflow DAG to simulate what a real run will look like to surface performance issues early in the process. How we fixed an Airflow performance bottleneck so our giant DAG can be properly rendered in Web UI. For detailed deep dives on some of topics mentioned above, please check out our blog post series at https://tech.scribd.com/tag/airflow-series/ [Slides] ( https://docs.google.com/presentation/d/e/2PACX-1vRb-iH5NX2d7m-rQ7WGc6XlRvRCADwXq2hdjRjRuJ5h7e9ybfoUA13ytxpHgx7JG815fIKEE-QKuRUV/pub?start=false&loop=false&delayms=3000 )

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 cover: How to test and debug tasks locally How to test with and without task instance context How to test against external systems, e.g. how to test a PostgresOperator? How to test the integration of multiple tasks to ensure they work nicely together

In this talk we review how Airflow helped create a tool to detect data anomalies. Leveraging Airflow 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 Airflow can also speed up development of those tools. The Data Anomaly Detector was created at One Medical to scan thousands of metrics per day for data anomalies. It’s a complicated tool and much of that complexity was outsourced to Airflow. Because the data infrastructure at One Medical was already built around Airflow, and Airflow had many desirable features, it made sense to build the tool to integrate closely with Airflow. The end result was more time could be spend on building features to do statistical analysis, and less effort had to be spent on database authentication, interoperability or process management. It’s an interesting example of how Airflow can be leveraged to build data intensive tools.

Summary Every business collects data in some fashion, but sometimes the true value of the collected information only comes when it is combined with other data sources. Data trusts are a legal framework for allowing businesses to collaboratively pool their data. This allows the members of the trust to increase the value of their individual repositories and gain new insights which would otherwise require substantial effort in duplicating the data owned by their peers. In this episode Tom Plagge and Greg Mundy explain how the BrightHive platform serves to establish and maintain data trusts, the technical and organizational challenges they face, and the outcomes that they have witnessed. If you are curious about data sharing strategies or data collaboratives, then listen now to learn more!

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Tom Plagge and Gregory Mundy about BrightHive, a platform for building data trusts

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what a data trust is?

Why might an organization want to build one?

What is BrightHive and what is its origin story? Beyond having a storage location with access controls, what are the components of a data trust that are necessary for them to be viable? What are some of the challenges that are common in establishing an agreement among organizations who are participating in a data trust?

What are the responsibilities of each of the participants in a data trust? For an individual or organization who wants to participate in an existing trust, what is involved in gaining access?

How does BrightHive support the process of building a data trust? How is ownership of derivative data sets/data products and associated intellectual property handled in the context of a trust? How is the technical architecture of BrightHive implemented and how has it evolved since it first started? What are some of the ways that you approach the challenge of data privacy in these sharing agreements? What are some legal and technical guards that you implement to encourage ethical uses of the data contained in a trust? What is the motivation for releasing the technical elements of BrightHive as open source? What are some of the most interesting, innovative, or inspirational ways that you have seen BrightHive used? Being a shared platform for empowering other organizations to collaborate I imagine there is a strong focus on long-term sustainability. How are you approaching that problem and what is the business model for BrightHive? What have you found to be the most interesting/unexpected/challenging aspects of building and growing the technical and business infrastructure of BrightHive? What do you have planned for the future of BrightHive?

Contact Info

Tom

LinkedIn tplagge on GitHub

Gregory

LinkedIn gregmundy on GitHub @graygoree on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

BrightHive Data Science For Social Good Workforce Data Initiative NASA NOAA Data Trust Data Collaborative Public Benefit Corporation Terraform Airflow

Podcast.init Episode

Dagster

Podcast Episode

Secure Multi-Party Computation Public Key Encryption AWS Macie Blockchain Smart Contracts

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how their teams are organized to allow for rapid growth and massive scale. Getting an inside look at the companies behind the services we use is always useful, and this conversation was no exception.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference in NYC, Strata Data in San Jose, and PyCon US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Vadim Semenov about how data engineers work at DataDog

Interview

Introduction How did you get involved in the area of data management? For anyone who isn’t familiar with DataDog, can you start by describing the types and volumes of data that you’re dealing with? What are the main components of your platform for managing that information? How are the data teams at DataDog organized and what are your primary responsibilities in the organization? What are some of the complexities and challenges that you face in your work as a result of the volume of data that you are processing?

What are some of the strategies which have proven to be most useful in overcoming those challenges?

Who are the main consumers of your work and how do you build in feedback cycles to ensure that their needs are being met? Given that the majority of the data being ingested by DataDog is timeseries, what are your lifecycle and retention policies for that information? Most of the data that you are working with is customer generated from your deployed agents and API integrations. How do you manage cleanliness and schema enforcement for the events as they are being delivered? What are some of the upcoming projects that you have planned for the upcoming months and years? What are some of the technologies, patterns, or practices that you are hoping to adopt?

Contact Info

LinkedIn @databuryat on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

DataDog Hadoop Hive Yarn Chef SRE == Site Reliability Engineer Application Performance Management (APM) Apache Kafka RocksDB Cassandra Apache Parquet data serialization format SLA == Service Level Agreement WatchDog Apache Spark

Podcast Episode

Apache Pig Databricks JVM == Java Virtual Machine Kubernetes SSIS (SQL Server Integration Services) Pentaho JasperSoft Apache Airflow

Podcast.init Episode

Apache NiFi

Podcast Episode

Luigi Dagster

Podcast Episode

Prefect

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools. At WeWork they needed a system that would provide visibility into their Airflow pipelines and the outputs produced. In this episode Julien Le Dem and Willy Lulciuc explain how they built Marquez to serve that need, how it is architected, and how it compares to other options that you might be considering. Even if you already have a metadata repository this is worth a listen to learn more about the value that visibility of your data can bring to your organization.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You work hard to make sure that your data is clean, reliable, and reproducible throughout the ingestion pipeline, but what happens when it gets to the data warehouse? Dataform picks up where your ETL jobs leave off, turning raw data into reliable analytics. Their web based transformation tool with built in collaboration features lets your analysts own the full lifecycle of data in your warehouse. Featuring built in version control integration, real-time error checking for their SQL code, data quality tests, scheduling, and a data catalog with annotation capabilities it’s everything you need to keep your data warehouse in order. Sign up for a free trial today at dataengineeringpodcast.com/dataform and email [email protected] with the subject "Data Engineering Podcast" to get a hands-on demo from one of their data experts. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Corinium Global Intelligence, ODSC, and Data Council. Upcoming events include the Software Architecture Conference, the Strata Data conference, and PyCon US. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Willy Lulciuc and Julien Le Dem about Marquez, an open source platform to collect, aggregate, and visualize a data ecosystem’s metadata

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Marquez is?

What was missing in existing metadata management platforms that necessitated the creation of Marquez?

How do the capabilities of Marquez compare with tools and services that bill themselves as data catalogs?

How does it compare to the Amundsen platform that Lyft recently released?

What are some of the tools or platforms that are currently integrated with Marquez and what additional integrations would you like to see? What are some of the capabilities that are unique to Marquez and how are you using them at WeWork? What are the primary resource types that you support in Marquez?

What are some of the lowest common denominator attributes that are necessary and useful to track in a metadata repository?

Can you explain how Marquez is architected and how the design has evolved since you first began working on it?

Many metadata management systems are simply a service layer on top of a separate data storage engine. What are the benefits of using PostgreSQL as the system of record for Marquez?

What are some of the complexities that arise from relying on a relational engine as opposed to a document store or graph database?

How is the metadata itself stored and managed in Marquez?

How much up-front data modeling is necessary and what types of schema representations are supported?

Can you talk through the overall workflow of someone using Marquez in their environment?

What is involved in registering and updating datasets? How do you define and track the health of a given dataset? What are some of the interesting questions that can be answered from the information stored in Marquez?

What were your assumptions going into this project and how have they been challenged or updated as you began using it for production use cases? For someone who is interested in using Marquez what is involved in deploying and maintaining an installation of it? What have you found to be the most challenging or unanticipated aspects of building and maintaining a metadata repository and data discovery platform? When is Marquez the wrong choice for a metadata repository? What do you have planned for the future of Marquez?

Contact Info

Julien Le Dem

@J_ on Twitter Email julienledem on GitHub

Willy

LinkedIn @wslulciuc on Twitter wslulciuc on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Marquez

DataEngConf Presentation

WeWork Canary Yahoo Dremio Hadoop Pig Parquet

Podcast Episode

Airflow Apache Atlas Amundsen

Podcast Episode

Uber DataBook LinkedIn DataHub Iceberg Table Format

Podcast Episode

Delta Lake

Podcast Episode

Great Expectations data pipeline unit testing framework

Podcast.init Episode

Redshift SnowflakeDB

Podcast Episode

Apache Kafka Schema Registry

Podcast Episode

Open Tracing Jaeger Zipkin DropWizard Java framework Marquez UI Cayley Graph Database Kubernetes Marquez Helm Chart Marquez Docker Container Dagster

Podcast Episode

Luigi DBT

Podcast Episode

Thrift Protocol Buffers

The intro and outro music is from a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug?utm_source=rss&utm_medium=rss"…

Summary Despite the fact that businesses have relied on useful and accurate data to succeed for decades now, the state of the art for obtaining and maintaining that information still leaves much to be desired. In an effort to create a better abstraction for building data applications Nick Schrock created Dagster. In this episode he explains his motivation for creating a product for data management, how the programming model simplifies the work of building testable and maintainable pipelines, and his vision for the future of data programming. If you are building dataflows then Dagster is definitely worth exploring.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Nick Schrock about Dagster, an open source system for building modern data applications

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Dagster is and the origin story for the project? In the tagline for Dagster you describe it as "a system for building modern data applications". There are a lot of contending terms that one might use in this context, such as ETL, data pipelines, etc. Can you describe your thinking as to what the term "data application" means, and the types of use cases that Dagster is well suited for? Can you talk through how Dagster is architected and some of the ways that it has evolved since you first began working on it?

What do you see as the current industry trends that are leading us away from full stack frameworks such as Airflow and Oozie for ETL and into an abstracted programming environment that is composable with different execution contexts? What are some of the initial assumptions that yo