verity-core 0.2.0

Verity: The Governance-as-Code Data Pipeline Tool

verity-governance-as-code

The Governance-First Data Transformation Engine.
Built in Rust. Compliance as Code. Zero-Trust Compilation.

Verity addresses the structural flaws of modern data engineering in the age of RAG. In an era where a pipeline error means a PII leak into a Vector Store, "optional documentation" is no longer acceptable.

Verity refuses to compile if governance guidelines are not met.

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✨ Key Features

Feature	Description
Zero-Trust Compilation	Governance violations are hard compile errors — no data is written before policies pass
Auto PII Masking	Columns tagged policy: hash/redact/mask_email are automatically wrapped at compile time
Parallel DAG Execution	Kahn's topological sort groups independent models for concurrent tokio execution
Auto-Schema Propagation	Undocumented columns are detected and added to schema.yml automatically
Source Generation	verity generate scans data/ and creates models/sources.yaml with smart merge
Data Quality Tests	unique, not_null, row_count_anomaly, z_score_anomaly run post-materialization
Strict vs. Dev Modes	strict: true blocks in CI/Prod; strict: false warns but doesn't block in Dev
Data Catalog	verity docs generates a JSON-LD (DCAT + PROV-O) catalog from the manifest
Single Binary	Pure Rust — cargo install, no Python venv, no cold start

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🛠 Quick Start

Prerequisites: Rust stable (2024 edition)

# Install
cargo install --path verity

# Run the full pipeline
verity run

# Run a single model
verity run --select stg_users

# Static Data Lineage Analysis (pre-flight checks for PII leaks)
verity lineage --check

# Strict mode (CI/Prod)
VERITY_STRICT=true verity run

# Scan data/ and generate/merge sources.yaml
verity generate --owner "data_team" --pii

# Generate data catalog
verity docs

# Ad-hoc SQL query
verity query "SELECT * FROM stg_users LIMIT 5"

# Clean build artifacts
verity clean

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📂 Examples

Example	What it demonstrates
basic_rag_pipeline	Multi-domain pipeline (HR, Supply Chain, Compliance) — PII masking into staging/intermediate/marts
ml_pipeline	Churn prediction — versioned feature store, prediction drift monitoring, security level downgrade

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📚 Documentation

Document	Description
PRD	Product Requirements — goals, features, success metrics
Implementation Guide	Pipeline lifecycle, module map, config reference, extension how-tos
ADRs	Architecture Decision Records — why key design choices were made
Sources Generation	verity generate smart-merge strategy
Contributing	Setup, quality gates, architecture rules, PR process

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📝 Contributing

See CONTRIBUTING.md for local setup, git workflow, quality gates (format → security → zero-panic → tests), and the PR process.

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Verity — Because compliance shouldn't be optional.