verity-bigquery 0.2.1

BigQuery Connector for 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
Modular Architecture	Decoupled Core engine and Cloud Connectors (BigQuery, etc.) for minimalist deployments
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
Enterprise Distribution	Native Rust binaries distributed via PyPI, Homebrew, and Docker

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🛠 Quick Start

1. Enterprise Installation (Recommended)

Verity is distributed as a high-performance Rust binary, but can be seamlessly installed via Python package managers (like uv or pip) to simplify orchestration in Airflow, dbt Cloud, or GitHub Actions.

# Install the core engine and the BigQuery connector in one command
uv add "verity-core[bigquery]==0.2.1"

# Or via standard pip
pip install "verity-core[bigquery]"

Note: Using the [bigquery] extra ensures the Core and Connector versions are perfectly aligned via JSON-RPC.

2. Native Installation (Mac/Linux)

curl -sSL https://raw.githubusercontent.com/axel-mauroy/verity-governance-as-code/main/install.sh | bash

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🚀 Usage

Once installed, use the verity CLI to manage your data lifecycle:

# Initialize a new project
verity init my_governed_project

# 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"

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📂 Examples

Example	What it demonstrates
basic_rag_pipeline	Multi-domain pipeline (HR, Supply Chain, Compliance) — PII masking
ml_pipeline	Churn prediction — versioned feature store, prediction drift monitoring

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📚 Documentation

Document	Description
Deployment Patterns	Cloud, Airflow, and K8s orchestration guide
PRD	Product Requirements — goals, features, success metrics
Implementation Guide	Pipeline lifecycle, module map, config reference
ADRs	Architecture Decision Records — why key design choices were made
Contributing	Setup, quality gates, architecture rules, PR process

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Verity — Because compliance shouldn't be optional.