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Protobuf Based Schema Centric Logging Framework

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Protobuf Based Schema Centric Logging Framework

Hi HN, have been working on this as a side project, would appreciate any feedback :) LogUnify is built around schemas based on protobuf and all the events in LogUnify are structured. By schematizing events, we offer the following benefits: - Consistency: All the events across all platforms and languages are produced in a uniform way with the generated type-safe SDKs, which ensures data consistency from the root. - Shared Understanding: Event schemas with rich data types and metadata are self-explained, which enables different departments from developers to business stakeholders to share the same understanding when consuming the events. - Flexibility: LogUnify offers flexibility in terms of integrating with different systems, as the schemas can be easily converted into different formats and protocols without requiring dedicated transformations from developers. The platform currently offers seamless integration with BigQuery and is expanding to support other systems. - Reliability: Structured events are more reliable as they are logged in the desired format from the start by developers who have full control over the producing logic. While traditional solutions require developers to log events in an unstructured manner and then set up the transformation on the server side. Data Analysis: Structured events enable better and more accurate data analysis naturally, as they provide a standardized and consistent format that can be easily processed and analyzed using various data analysis tools and techniques.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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: using, plain · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% 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: way · 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 · Missing: plus, intuitive, reviews
31%31% 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
9%9% 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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