What to Build

Create a working software application that uses DataHub to solve one of the challenges below. Pick one of the four challenges (or combine them):

• Agents That Do Real Work: Build AI agents that handle data problems — alone or as a team. Your agent reads DataHub through the MCP Server or Agent Context Kit to understand what's connected to what, takes action, and writes results back so the next person or agent inherits the knowledge.
• Metadata-Aware Code Generation & Development: Build agents that generate production data code — transformation models, pipeline DAGs (Airflow, Prefect, Dagster), ingestion scripts, helper scripts, configurations, migration code — that works on the first try because they use DataHub Skills or the MCP Server to read DataHub for the real schemas, lineage, and rules before generating anything. The artifact lives in a Git repo, goes into a PR, and your data team would actually merge it. Strong submissions include sample generated artifacts so judges can see the quality of the output.
• Production ML Agents: Build agents for ML teams that protect models in production. Use DataHub's end-to-end ML lineage — the path from training data to features to models to deployments — accessed via the Agent Context Kit or MCP Server to catch silent problems that can break ML systems before they cost money. 
• Open / Wildcard: Build anything creative that uses DataHub as the foundation — supply chain optimization, financial forecasting, regulatory automation, knowledge capture, or anything else. Use whatever fits from DataHub's open-source stack (MCP Server, Agent Context Kit, DataHub Skills, Analytics Agent, or any other DataHub product).

What to Submit

• Include a URL to your Project that gives judges easy access to test the functionality — a live demo, hosted app, or your repo with clear setup instructions.
• Provide a URL to your public code repository for judging and testing. The repository must contain all necessary source code, assets, and full instructions required for the project to be functional. The repository must be public and open source by including an Apache 2.0 open source license file. This license should be detectable and visible at the top of the repository page (in the About section).
• Include a text description that summarizes your Project that might include describing its features, functionality, technologies, and data you used.
• Include a demonstration video of your Project that is under 3 minutes, uploaded to YouTube or Vimeo with public visibility enabled. The video should include footage that shows the Project functioning and in action.
• Optional: Include Sample outputs. If your Project generates artifacts such as code files, queries, reports, or transformations, include examples in your repository (e.g., an examples/ folder) so judges can evaluate the quality without needing to run the code.

💰Bonus Prize: Interested in the Most Valuable Feedback Survey Prize? Opt in and complete the feedback section during submission to be considered ($50 × 10 awards).

See the rules for all details.