Devpost
Participate in our public hackathons
Devpost for Teams
Access your company's private hackathons
Grow your developer ecosystem and promote your platform
Drive innovation, collaboration, and retention within your organization
By use case
Blog
Insights into hackathon planning and participation
Customer stories
Inspiration from peers and other industry leaders
Planning guides
Best practices for planning online and in-person hackathons
Webinars & events
Upcoming events and on-demand recordings
Help desk
Common questions and support documentation
The missing CI for your ML supply chain.
The agentic copilot that turns DataHub lineage into a risk score — and writes the decision back.
An AI data steward that detects stale metadata from engineering changes, reasons using DataHub, and automatically proposes or applies accurate metadata updates.
Pramaan extends beyond passive governance into an active security scanning and validation platform for both Agent-to-Agent (A2A) networks and Model Context Protocol (MCP) servers.
Every AI-generated data change gets a DataHub-backed metadata lease. Before merge, ContextLease rechecks schemas, tags, lineage & owners - if context drifted, the PR is blocked.
MetricGuard finds conflicting SQL definitions for the same business metric, proves how much they disagree, helps approve one canonical truth, writes it to DataHub, and guards against future drift.
We're embedding a conversational AI agent directly into DataHub's UI, turning a static metadata catalog into active, queryable copilot that lets discover data, add skills & act using Action Framework.
Change-Intelligence Layer for DataHub >> intercepts schema changes, computes risk score, generates remediation artifacts, and writes impact analysis back onto the DataHub metadata graph.
An AI agent that uses DataHub as a trusted context layer to help sales and customer support teams generate reliable answers, cite sources, and detect outdated or conflicting knowledge.
Schema changes shouldn't be a surprise your model discovers in production
The Technical Option: Multi-Agent AI orchestration for automated data observability and fault isolation.
An agent that reads DataHub metadata - PII tags, ownership, freshness, lineage - and ranks datasets into a prioritized, explainable security review queue.
AI Chief Data Officer powered by DataHub. Multi-agent AI that investigates incidents, analyzes lineage, simulates data outages, and improves governance to help data teams prevent failures.
An AI agent that checks DataHub before it touches your data — ownership, deprecation, quality — and writes every DLP violation and remediation back to the context graph as the audit trail.
An agentic game built on DataHub’s Context Platform that turns metadata literacy into an interactive, visual learning adventure.
Schema Guardian reads DataHub's real lineage graph via the MCP Server to catch what a schema change breaks — and generates the fix as a ready PR.
Generates what's safe. Warns what's not.
AI agent that predicts which downstream consumers will silently produce wrong data after a schema change, then writes governance warnings back into DataHub autonomously.
The on-call agent that catches silent ML failures your monitoring can't by walking DataHub lineage to the root cause and writing every finding back to the catalog.
An on-call AI agent that catches a silently degrading ML model, traces it through DataHub lineage to the upstream column that broke it, and writes the cause back so the next agent inherits it.
An autonomous AI Data Reliability Engineer that monitors, investigates, and remediates data quality and governance issues across modern data platform
DataHub shows what depends on a dataset. Tally finds the exact code to change, builds an evidence-backed plan, and refuses to guess where the proof ends.
WE HATE SCAMMERS
An autonomous DataHub agent that audits whether a model's reported numbers are honest — catching leakage, overfit & miscalibration — and writes the verdict back into the catalog as a Trust Score.
1 – 24 of 612
