The Open Data Product Specification (ODPS) is a vendor-neutral, open-source, machine-readable metadata model for defining and managing data products in a consistent, interoperable way. Backed by the Linux Foundation, ODPS helps organizations build data products that align business value, governance, and technical delivery
Discord Server for collaboration
Join our community forum to ask questions, share ideas, and collaborate on Open Data Product Specification (ODPS) adoption.
FAQ with examples
Access a growing knowledge base of frequently asked questions, with practical YAML examples to help you apply ODPS effectively.
Udemy MasterClasses
Learn from structured courses covering data product monetization, governance, AI readiness, and more — designed for all levels.
Data Product Toolkit (PDF canvases)
Download free canvases (CC-BY) to design, measure, and manage data products using proven frameworks like the Data Product Canvas 2.1 and Blueprint Model.
YAML Builder for Open Data Products (GPT)
Helps author YAML for Open Data Product Specification with live output. A custom built GPT in OpenAI. Warning! Might hallicinate, so always double check the result, but still good for learning purposes.
If you can not find what you are looking for, raise an issue and describe the need.
The ODPS Discord Server is an online community forum designed to support collaboration, knowledge sharing, and discussion around the Open Data Product Specification (ODPS). It provides a space where practitioners, spec authors, and adopters can ask questions, share ideas, propose improvements, and connect with peers in the data product ecosystem. The platform helps strengthen community-driven development and best practices for ODPS.
If you're looking to apply ODPS in real-world business scenarios, including monetization strategies, AI agent readiness, and MCP integration, check out these advanced Udemy courses:
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Udemy course - Master the Leading Data Product Specification with GPT tool Introduction to ODPS as a short Udemy course. Not too technical, but not just "slides" either. Suitable for Architects, Business Management and Data Product Managers.
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Udemy Data Product Monetization MasterClass
Learn how to turn ODPS-based data products into scalable revenue streams, including machine-readable pricing, AI agent monetization, and shadow pricing templates. Includes a complete Monetization Toolkit. -
Udemy Data Product MasterClass
Adopt the data product mindset with practical frameworks, blueprints, and actionable strategies that build on ODPS concepts. Features real-world examples, and AI-powered narration. -
Udemy Minimum Lovable Governance
Adopt the data product mindset with practical frameworks, blueprints, and actionable strategies that build on ODPS concepts. Features real-world examples, and AI-powered narration.
👉 All courses are designed to complement this knowledge base—helping you move from learning the spec to delivering business impact.
The Data Product Toolkit is a free, open set of canvases designed to help teams design, measure, and manage data products effectively. It includes practical frameworks like the Data Product Canvas 2.1, Value Measure Framework, Blueprint Model, and more — all licensed under CC-BY. The toolkit guides you through defining use cases, aligning business value, and ensuring governance and scalability for successful data product development
This tool assists data practitioners in authoring, validating, and maintaining machine-readable YAML documents that conform to the Open Data Product Specification (ODPS). It ensures strict compliance with the ODPS schema, supporting both interactive construction and troubleshooting of ODPS data product YAML files.
Remember it is AI and might hallucinate. Always double check the results.
Core Features:
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ODPS Compliance: Every YAML definition generated is validated against the ODPS 4.0 schema and is ready for use in data product catalogs, marketplaces, or governance tooling.
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Schema Guidance: Provides detailed, context-sensitive explanations of each ODPS component, including Data Product Details, Data Contract, Data Holder, Data Access, and more, leveraging official schema and documentation references.
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Reference Support: Supports ODPS-native referencing (with $ref), enabling modular YAMLs, DRY principles, and scalable metadata management for SLA, DQ, access, or pricing profiles.
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Multilingual Structure: Allows structuring of business and product details in multiple languages according to ODPS patterns.
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Spec-Driven: All fields, options, and object nesting strictly follow the official ODPS specification—no fields are invented or assumed beyond the standard.
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Example-First: Provides real-world, spec-conformant YAML snippets for each ODPS object for rapid onboarding and as templates for new products.
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Download-Ready Output: Presents the resulting YAML in a copyable, downloadable format for immediate integration into CI/CD, validation, or product onboarding flows.
This section helps you understand and apply the ODPS specification through practical, modular examples. Each question below links to a separate answer file, which includes:
- Explanation of the concept
- Code snippets (just plain YAML)
- ODPS YAML examples (
.yml) alongside the markdown answer
- What is ODPS, and why should I use it?
- What does a complete ODPS-compliant product look like?
- What are the required and optional sections in ODPS?
- How do I define metadata for my data product?
- How do I define related products and use cases?
- How do I define pricing plans?
- Can I offer free and paid tiers in the same product?
- How do I assign SLAs to pricing plans?
- How do I define and reuse a payment gateway?
- How do I reuse SLA, DQ, and Access across products?
- Can I reference external YAML files?
- What’s the difference between internal and external references?
- How does ODPS support AI agent consumption?
- How to use ODPS spec with LLMs.txt?
- How to build AI-assisted Minimum Lovable Governance with Claude?
- How do I validate an ODPS specs in tools?
- How do I validate an ODPS YAML file?
- Are there templates I can use to start faster?
📂 Each FAQ answer is located in the /faq folder, and includes a related .yaml file in the yaml folder. Contributions welcome.
