Product Analytics in SQL with dbt

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Hey everyone! Like many data analysts and engineers, I love SQL and the dbt ecosystem. So it bothers me that we have to use separate tools for product analytics. We do our transformations, BI work, and ad-hoc queries in SQL, but when it's time to look at funnels and flows, we have to use (and procure) a separate platform like Mixpanel or Amplitude. This dbt package is a (very rough) start at fixing that. With it, you can create event streams and run funnel analyses via dbt[0]. More features like flows and retention are coming soon! But I'm mostly curious how you all are doing product analytics right now. Are you using a dedicated tool like Amplitude? What could be better? Do you want to do product analytics in SQL in the warehouse or would you rather it live somewhere else? Would love to get your thoughts, and thanks for taking a look! --- 0. (and soon, with dbt Server, in your favorite BI tool or SQL client): https://www.youtube.com/watch?v=MdSMSbQxnO0&ab_channel=dbt

Visit product View on Hacker NewsJun 25, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos

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68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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