Data Contract

Data contracts; contract-as-code; schema & SLAs/SLOs; data quality; validation & monitoring; Data Mesh; 

This area studies how Large Language Models (LLMs) can help specify, generate, and maintain data contracts (schema, semantics, SLAs/SLOs, and validation rules) across data-intensive architectures. It explores NL→contract generation, contract enforcement (batch vs. stream), and continuous adaptation based on telemetry (freshness, drift, null/dup rates). The goal is reliable, explainable, and auditable data quality at scale—spanning warehouses, Data Mesh domains, and IoT/edge pipelines—while keeping humans in the loop for review and governance.

• Define a contract-as-code meta-model linking schema, constraints, SLAs/SLOs, lineage, owners, and versioning.

• Build an LLM pipeline that converts natural-language requirements into executable checks (e.g., GE/SQL), with concise rationales and safety guardrails.

• Implement adaptive thresholds using profile signals (freshness delay, drift, null/dup/OOV rates) and feedback from production incidents.

• Orchestrate contract enforcement across batch/stream paths with CI/CD, canary validation, and impact analysis.

• Design human-in-the-loop workflows: propose → rank → approve → monitor → learn.

• Provide governance & traceability: lineage-backed exceptions, policy alignment, and audit logs.

• Evaluate on real datasets (e.g., telecom/IoT) with metrics for effectiveness (defect detection/precision–recall) and efficiency (latency, cost).

• Release reference tooling & benchmarks to support reproducibility and adoption.

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