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Putting Airflow Into Production With James Meickle - Episode 43

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When? This feed was archived on January 17, 2023 15:38 (1+ y ago). Last successful fetch was on December 12, 2022 14:50 (2y ago)

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Manage episode 214816547 series 1336909
Content provided by Tobias Macey. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Tobias Macey or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Summary

The theory behind how a tool is supposed to work and the realities of putting it into practice are often at odds with each other. Learning the pitfalls and best practices from someone who has gained that knowledge the hard way can save you from wasted time and frustration. In this episode James Meickle discusses his recent experience building a new installation of Airflow. He points out the strengths, design flaws, and areas of improvement for the framework. He also describes the design patterns and workflows that his team has built to allow them to use Airflow as the basis of their data science platform.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing James Meickle about his experiences building a new Airflow installation

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • What was your initial project requirement?
    • What tooling did you consider in addition to Airflow?
    • What aspects of the Airflow platform led you to choose it as your implementation target?
  • Can you describe your current deployment architecture?
    • How many engineers are involved in writing tasks for your Airflow installation?
  • What resources were the most helpful while learning about Airflow design patterns?
    • How have you architected your DAGs for deployment and extensibility?
  • What kinds of tests and automation have you put in place to support the ongoing stability of your deployment?
  • What are some of the dead-ends or other pitfalls that you encountered during the course of this project?
  • What aspects of Airflow have you found to be lacking that you would like to see improved?
  • What did you wish someone had told you before you started work on your Airflow installation?
    • If you were to start over would you make the same choice?
    • If Airflow wasn’t available what would be your second choice?
  • What are your next steps for improvements and fixes?

Contact Info

Parting Question

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

Links

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

  continue reading

354 episodes

Artwork
iconShare
 

Archived series ("Inactive feed" status)

When? This feed was archived on January 17, 2023 15:38 (1+ y ago). Last successful fetch was on December 12, 2022 14:50 (2y ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 214816547 series 1336909
Content provided by Tobias Macey. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Tobias Macey or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Summary

The theory behind how a tool is supposed to work and the realities of putting it into practice are often at odds with each other. Learning the pitfalls and best practices from someone who has gained that knowledge the hard way can save you from wasted time and frustration. In this episode James Meickle discusses his recent experience building a new installation of Airflow. He points out the strengths, design flaws, and areas of improvement for the framework. He also describes the design patterns and workflows that his team has built to allow them to use Airflow as the basis of their data science platform.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing James Meickle about his experiences building a new Airflow installation

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • What was your initial project requirement?
    • What tooling did you consider in addition to Airflow?
    • What aspects of the Airflow platform led you to choose it as your implementation target?
  • Can you describe your current deployment architecture?
    • How many engineers are involved in writing tasks for your Airflow installation?
  • What resources were the most helpful while learning about Airflow design patterns?
    • How have you architected your DAGs for deployment and extensibility?
  • What kinds of tests and automation have you put in place to support the ongoing stability of your deployment?
  • What are some of the dead-ends or other pitfalls that you encountered during the course of this project?
  • What aspects of Airflow have you found to be lacking that you would like to see improved?
  • What did you wish someone had told you before you started work on your Airflow installation?
    • If you were to start over would you make the same choice?
    • If Airflow wasn’t available what would be your second choice?
  • What are your next steps for improvements and fixes?

Contact Info

Parting Question

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

Links

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

  continue reading

354 episodes

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