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Bemi for TypeORM – automatic data change tracking

Hacker News

Bemi for TypeORM – automatic data change tracking

Evgeny, Arjun, and Donna here - we’re building cloud-hosted data change tracking for PostgreSQL. We’re launching our first open-source integration for TypeORM that allows applications to automatically attach additional metadata to database changes. The repo is at https://github.com/BemiHQ/typeorm and our home page is https://bemi.io . We used to all work together at a startup five years ago, and now have got the gang back together to go full-time on Bemi! We’ve built robust compliance engineering systems before, for example at AngelList, but only recently learned that the tech is useful to other companies. We’ve been following that demand to build a general-purpose tracking solution that can be used for a wide range of use cases such as audit trails, reverting changes made in an API request, testing different application states, etc.. We’ve built Bemi to be lightweight and secure. It takes a practical approach to achieving 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 storage and compute if you’re a company storing >1M changes a month. We’re still early and it’d be amazing to get HN’s feedback. What do you think HN?

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90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
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Product HuntOn track for Day 1 leaderboard · Strong signals: context, using, code · Missing: mac, agents, macos
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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
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