The Stack Overflow Podcast
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Your snips
[00:47] Toby Mao's Career Journey
[01:53] Iaroslav Zygerman's Early Path
[02:57] Netflix's Data Tooling Gap
[05:04] Use SQL to Lower Barriers
[06:30] Mock SQL Engines for Unit Tests
[08:16] Auto-generate Unit Tests From Data
[10:17] SQL Evolves Divisively
[15:01] Adopt SQL Mesh for Pipelines
[18:06] Challenges Managing Playback Data
[20:30] Separate Analytics From Production
[23:22] AI Increases Data Pipeline Stakes
[25:14] Leverage SQL Mesh for Dev Speed
๐ง๐ผ๐ฑ๐ฎ๐'๐ ๐ฃ๐ผ๐ฑ๐ฐ๐ฎ๐๐ ๐๐ป๐๐ถ๐ด๐ต๐ ๐ฅ๐ฒ๐ฐ๐ฎ๐ฝ๐๏ธ
Just wrapped up this episode: ๐ฃ๐ผ๐ฑ๐ฐ๐ฎ๐๐: The Stack Overflow Podcast with Ryan Donovan, Toby Mao, and Iaroslav Zygerman ๐๐ฝ๐ถ๐๐ผ๐ฑ๐ฒ: You've got 99 problems but data shouldn't be one ๐๐ฎ๐๐ฒ: July 2, 2025
๐๐ฒ๐ ๐ง๐ฎ๐ธ๐ฒ๐ฎ๐๐ฎ๐
Separate your analytics from production databases as you scale. While small datasets (under 100GB) might work in a single system, once you hit terabytes of data, you need specialized data warehousing solutions with clear separation between engineering and analytics functions.
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As organizations grow, the skills and priorities of engineers (who optimize for performance and data integrity) diverge from those of analysts (who focus on business insights). This separation becomes critical for maintaining system performance, preventing production database locks, and enabling each team to work efficiently with tools optimized for their specific needs.
๐ฅ๐ฒ๐ณ๐น๐ฒ๐ฐ๐๐ถ๐ผ๐ป ๐ง
This reminded me how often I've seen teams try to shortcut proper data architecture only to create technical debt later. With AI now consuming more of our data pipelines, the stakes are even higher โ garbage in truly means 10x garbage out with LLMs. Taking the time to build clean, separate data systems pays dividends when scaling becomes inevitable.
To get the full insight, check out the episode!
#DataScience #Finance #MachineLearning #AI #CareerGrowth #TechLeadership #DecisionMaking #StackOverflowPodcast #99ProblemsButDataShouldntBeOne