Sequor – dbt for API Integration

Hacker News

Hey HN! We built Sequor to solve a recurring problem: choosing between two bad options for API/app integration: 1. Proprietary black-box SaaS connectors with vendor lock-in 2. Custom scripts that are brittle, opaque, and hard to maintain Sequor is a SQL-centric workflow framework for building reliable API integrations in modern data stacks — code-first, declarative, and version-controlled. It’s an open source alternative to SaaS connectors, giving data teams full control over their integration pipelines. Sequor combines SQL with HTTP request handling — think dbt for API integration, but with explicit flow control: • Iterate over input tables to make parameterized API calls • Parse and map JSON responses back into database tables • Use SQL for logic, YAML for flows, and Jinja/Python for dynamic behavior • Works equally well for data extraction, reverse ETL, and iPaaS-style end-to-end automation • Git-friendly, no drag-and-drop UIs Our goal was to bring software engineering best practices to integration workflows — without proprietary environments or vendor lock-in. Think of Sequor as your integration foundation, not your ceiling. We provide the proven patterns — you build the custom connectors that match your data flows and business logic. We’d love your feedback, ideas, and contributions. Website: https://sequor.dev — with code examples Quickstart: https://docs.sequor.dev/getting-started/quickstart GitHub: https://github.com/paloaltodatabases/sequor Prebuilt integrations: https://github.com/paloaltodatabases/sequor-integrations

Visit product View on Hacker NewsMay 28, 2025

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

86%86% 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.

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released

66%66% 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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, recurring · Missing: arr, mrr, revenue

22%22% 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.

Incorrect prediction on native model

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