Data Pipelines with Apache Airflow

Julian LaNeve (@JulianLaneve, CTO @astronomerio) discusses data pipelines, Apache Airflow, Astronomer’s managed offering, and the benefits of data pipelines for both developers and operations. SHOW: 939 SHOW TRANSCRIPT: The Cloudcast #939 Transcript SHOW VIDEO: https://youtube.com/@TheCloudcastNET  NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST: "CLOUDCAST BASICS"  SPONSORS:[VASION] Vasion Print eliminates the need for print servers by enabling secure, cloud-based printing from any device, anywhere. Get a custom demo to see the difference for yourself.[FCTR] Try FCTR.io (that"s F-C-T-R dot io) free for 60 days. Modern security demands modern solutions. Check out Fctr"s Tako AI, the first AI agent for Okta, on their website SHOW NOTES:Astonomer.io websiteApache Airflow Topic 1 - Welcome to the show, Julian. Give everyone a quick introduction. Topic 2 - Our topic today is Data Pipelines with Apache Airflow.  For those unfamiliar, provide an introduction to Apache Airflow and how Airflow manages data pipelines. Topic 3 - What are the advantages of Apache Airflow vs. others in the space? What are the downsides? How does Airflow fit in with other Apache projects? Topic 4 - I would imagine this is where Astronomer potentially comes into play. What makes Astonomer different from Airflow? What problems are you trying to solve for both developers and operations folks? Topic 5 - What does a typical implementation look like? What growing pains do developers typically face when they need to introduce pipelining tools and begin standardization? Is it a scale issue? A complexity of tools issue? Integrations with infrastructure? Topic 6 - One aspect I typically see with automation is security, especially at scale. What recommendations do you have for developers regarding security, particularly in the context of multi-tenancy, for data pipelines? FEEDBACK?Email: show at the cloudcast dot netBluesky: @cloudcastpod.bsky.socialTwitter/X: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod