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Bemi – context-aware data change tracking for Prisma

Hacker News

Bemi – context-aware data change tracking for Prisma

Hey HN, we’re Evgeny, Arjun, and Donna - we’re building automatic data change tracking for PostgreSQL and we’re releasing our Prisma integration! The repo is https://github.com/BemiHQ/prisma and our home page is https://bemi.io . We've built robust data tracking systems before, for example at companies that hadn’t implemented an event-sourced architecture from the get-go and needed to scale with traditional transactional databases. We’ve since learned that the tech is useful to lots of others. We’ve gone full-time on Bemi now and have been following that demand to build a general-purpose tracking solution that can be used for a wide range of use cases such as reliable audit trails, surfacing data to customer support, reverting changes made in an API request, or testing different application states. We’ve built Bemi to be lightweight and secure. It takes a practical approach to achieve the benefits of event sourcing without requiring rearchitecting existing code, switching to highly specialized databases, or using unnecessary git-like data versioning abstractions. We want your system to work the way it already does with PostgreSQL to allow keeping things as simple as possible. We plug in at both the database and application levels, to get reliability, performance, and also a comprehensive understanding of every change. On the database level, we ingest changes using change data capture (CDC). On the application level, our library allows passing application context and metadata to the write-ahead logs (WAL) automatically. We then stitch the change data together and store it in a structured format in a destination cloud PostgreSQL. We plan to charge for compute if you’re a company storing >1M changes a month. If this is something that seems interesting to you feel free to play around with it or message us! We’re still early and it’d be amazing to get HN’s feedback. What do you think HN?

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, code · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% 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
10%10% 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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