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vulcan sql

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Basic Information

VulcanSQL is an analytical Data API framework intended to help data professionals and developers expose databases, data warehouses, and data lakes as RESTful APIs for AI agents and data applications. The project converts SQL templates into API endpoints, accepting input parameters and generating SQL on the fly. It emphasizes standardized API interaction via OpenAPI documents so AI agents and external tools can discover and call data endpoints consistently. The repository provides a development workflow similar to dbt with templated SQL and variable insertion, a caching layer using DuckDB to accelerate queries, and tooling to package and deploy APIs via Docker or command-line workflows. The README also points to an online playground and example repositories to demonstrate usage and integration patterns.

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App URL
https://github.com/Canner/vulcan-sql
Github Repository
https://github.com/Canner/vulcan-sql/blob/main/README.md

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App Details

Features
The README highlights templated SQL with variable substitution to dynamically generate queries and produce APIs directly from SQL definitions. It supports OpenAPI generation for standardized API contracts and documentation. DuckDB is used as a caching layer to speed up query response and reduce load on upstream data sources. The project includes a packaging command to bundle assets for deployment and supports Docker or command-based deployment options. Documentation lists connectors for data sources, guidance for writing SQL templates, caching controls, error handling, parameter validation, data privacy guidance, extension and plugin points, and example repositories and an online playground for demos.
Use Cases
VulcanSQL reduces the time and complexity of building custom data APIs by automating SQL-to-API conversion and providing a template-driven workflow. It mitigates manual coding errors and promotes standardization so AI agents and downstream apps can interact with data consistently using OpenAPI. The DuckDB cache improves performance and scalability by lowering response time and offloading repetitive queries from primary data systems. Built-in guidance and features for validation, error handling, and data privacy help address security and compliance concerns while simplifying maintenance. Use cases called out in the README include powering AI agents, customer-facing analytics, secure data sharing with partners, and integrations into internal tools like Zapier, AppSmith, and Retool.

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