Orbital – Dynamically unifying APIs and data with no glue code

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Hey HN, I'm excited to share Orbital, a new approach for unifying APIs and Data sources! Rather than relying on glue code to bridge endpoints, Orbital leverages annotations in schemas & API specs to build the integration dynamically. The traditional method of crafting glue code often becomes repetitive and burdensome to maintain in the long run. With Orbital, developers embed tags to their existing API specs (OAP, Protobuf, etc), indicating where data can be sourced, and publish these specs to Orbital (which runs self-hosted). Consumers query these tags with our TaxiQL language, and Orbital generates the integration on the fly. That could be merging multiple APIs, blending API and database queries, or enriching event streams to craft custom message payloads. It feels a lot like writing GraphQL, but there's no resolvers to maintain, and producers are free to use a variety of API Spec languages. The beauty of using tags over field names is the adaptability. As API developers update and publish their specs, the integration remains seamless and automatically adjusts. Under the hood, the tags (and associated query language) are actually Taxi - an OSS meta-language and toolchain we build (and have shared previously). Orbital is a query engine that executes TaxiQL queries, generating the integration. We've been working on this for a while, and have a number of production deployments. We recently made the move to make the source available on Github under a mix of Apache 2 (Open Core) and BuSL. In a nutshell, Orbital excels in Data Composition (covering APIs, DBs), crafting Bespoke Event Streams, and streamlining ELT workloads into databases. Excited to hear your thoughts and feedback!

Visit product View on Hacker NewsOct 25, 2023

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

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

78%78% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

17%17% predicted probability of success on AppSumo, 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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