explaingit

apache/ossie

Analysis updated 2026-05-18

1,489PythonAudience · dataComplexity · 2/5LicenseSetup · easy

TLDR

An Apache incubating specification that standardizes how KPI and metric definitions are shared across BI and AI tools.

Mindmap

mindmap
  root((Apache Ossie))
    What it does
      Standardizes semantic metadata
      Vendor neutral specification
      JSON and YAML based
    Tech stack
      JSON
      YAML
      Python
    Use cases
      Keep KPI definitions consistent
      Convert between semantic formats
      Validate semantic models
    Audience
      Data engineers
      Analytics teams
      AI platform builders
    Features
      Reference converters
      Example TPC-DS model
      Validation tooling

Code map

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What do people build with it?

USE CASE 1

Adopt a single semantic model specification so KPI definitions stay consistent across BI tools and AI agents.

USE CASE 2

Convert existing semantic models between Ossie and formats like dbt, GoodData or Salesforce using the reference converters.

USE CASE 3

Validate a semantic model against the Ossie schema before rolling it out across your data stack.

USE CASE 4

Study the example TPC-DS semantic model as a template for defining your own metrics.

What is it built with?

JSONYAMLPython

How does it compare?

apache/ossiebeenuar/aisocwenyuchiou/awesome-agentic-ai-zh
Stars1,4891,4791,504
LanguagePythonPythonPython
Last pushed2026-06-30
MaintenanceActive
Setup difficultyeasyhardeasy
Complexity2/54/51/5
Audiencedataops devopsvibe coder

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 30min

This is a specification and schema, not a running service, so there is nothing to deploy.

Free to use, modify and redistribute for any purpose, including commercial use, as long as you keep the license notice.

In plain English

Apache Ossie, previously called Open Semantic Interchange, is an open source effort to create one shared way of describing what data means across the many different tools used in analytics, AI and business intelligence. The problem it addresses is that the same business metric, like a KPI, often gets defined differently in each tool a company uses, which forces teams to spend real time reconciling those different definitions by hand, and can lead AI agents to produce unreliable answers because they are working from inconsistent underlying business logic. The project's answer is a single specification, written in JSON and YAML, that any tool can read and write. The goal is a vendor neutral single source of truth for semantic metadata, so a data field's meaning and value stay the same no matter which tool or AI agent is using it, removing the inconsistencies that come from each platform maintaining its own separate definitions. The repository is organized into a few clear parts. The core-spec folder holds the actual specification document along with its machine readable schema files. A converters folder contains reference code that translates between the Ossie format and other semantic formats used by tools like dbt, GoodData, Polaris and Salesforce. An examples folder includes sample semantic models, including a full model based on the standard TPC-DS benchmark dataset. There is also a validation folder with tooling to check whether a semantic model matches the Ossie schema, and a docs folder with general project documentation. As an Apache incubating project, Ossie is community driven. The README points people who want to get involved toward a CONTRIBUTING guide for proposing specification changes, a ROADMAP document describing current working groups and planned enhancements, and both GitHub Discussions and a Slack community for ongoing conversation. The project is licensed under the Apache License, Version 2.0.

Copy-paste prompts

Prompt 1
Explain what problem the Apache Ossie semantic specification solves for AI and BI tools.
Prompt 2
Walk me through the structure of the core-spec folder and the osi-schema.json file.
Prompt 3
Show me how to use the converters to translate a dbt semantic model into the Ossie format.
Prompt 4
Help me validate my own semantic model against the Ossie schema using the validation tooling.

Frequently asked questions

What is ossie?

An Apache incubating specification that standardizes how KPI and metric definitions are shared across BI and AI tools.

What language is ossie written in?

Mainly Python. The stack also includes JSON, YAML, Python.

What license does ossie use?

Free to use, modify and redistribute for any purpose, including commercial use, as long as you keep the license notice.

How hard is ossie to set up?

Setup difficulty is rated easy, with roughly 30min to a first successful run.

Who is ossie for?

Mainly data.

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