Hello, welcome to The New Stack Context, a podcast where we review the week’s hottest news in cloud-native technologies/ at-scale application development and look ahead to topics we expect will gain more attention in coming weeks. Hacker News and the Twitterverse were alight this week with discussion of Datadog engineer Jason Moiron’s blog post titled “Is Kubernetes Too Complicated?” This article hints at a Kubernetes backlash afoot, with ongoing concerns about its complexity. We speak with TNS correspondent Scott Fulton, who has a TNS article summarizing these issues. In the second half of the show, we’ll talk with TNS Managing Editor Joab Jackson about his story on TNS about how Facebook keeps its website snappy for billions of users worldwide. There are so many static packages that a page could use that it would be impossible to download them all at once. Yet all the right elements must be downloaded to the browser within a few seconds. We discuss how they’ve solved this problem with machine learning, and the engineering team they’ve built up in New York.
Hello, welcome to The New Stack Context, a podcast where we review the week’s hottest news in cloud-native technologies/ at-scale application development and look ahead to topics we expect will gain more attention in coming weeks.
Hacker News and the Twitterverse were alight this week with discussion of Datadog engineer Jason Moiron’s blog post titled “Is Kubernetes Too Complicated?” This article hints at a Kubernetes backlash afoot, with ongoing concerns about its complexity. We speak with TNS correspondent Scott Fulton, who has a TNS article summarizing these issues.
In the second half of the show, we’ll talk with TNS Managing Editor Joab Jackson about his story on TNS about how Facebook keeps its website snappy for billions of users worldwide. There are so many static packages that a page could use that it would be impossible to download them all at once. Yet all the right elements must be downloaded to the browser within a few seconds. We discuss how they’ve solved this problem with machine learning, and the engineering team they’ve built up in New York.