odgs-databricks-bridge 0.4.2

Bridge: Databricks Unity Catalog → ODGS Runtime Enforcement Schemas

ODGS Databricks Bridge

Transform your Databricks Unity Catalog into active ODGS runtime enforcement schemas.

Unity Catalog describes your data. ODGS enforces it.

The ODGS Databricks Bridge is an institutional connector that translates Databricks Unity Catalog table and column metadata into cryptographically addressable ODGS enforcement schemas. It converts passive catalog definitions — column constraints, non-nullable fields, type assertions — into mechanically executable governance rules enforced at pipeline runtime.

Architecturally aligned with federated data sovereignty principles, and on a path toward standardization.

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Architecture

flowchart LR
    subgraph UC["Databricks Unity Catalog"]
        CAT["Catalogs & Schemas"]
        TBL["Tables & Columns"]
        CON["Constraints\n(NOT NULL, Types)"]
    end

    subgraph Bridge["odgs-databricks-bridge"]
        T["DatabricksBridge\n.sync()"]
        TR["Transformer\n→ ODGS Schema"]
    end

    subgraph ODGS["ODGS Protocol (odgs>=5.1.0)"]
        I["Universal Interceptor\nHARD_STOP / SOFT_STOP / WARNING / LOG_ONLY"]
        WB["Write-Back\n→ Unity Catalog Comments"]
    end

    subgraph MP["Metric Provenance (Commercial)"]
        SC["S-Cert Registry\n(JWS Audit Seal)"]
    end

    CAT & TBL & CON --> T --> TR --> I
    I -->|"sovereign_audit.log"| WB --> UC
    I -->|"Certified S-Cert"| SC

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What Gets Generated

Unity Catalog Input	ODGS Output Type	Rule Example
Table metadata	metrics	Metric definitions with full column schemas
Non-nullable columns	rules (HARD_STOP)	revenue.amount IS NOT NULL
Column data type	rules (WARNING)	type(amount) == 'decimal'

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Install

pip install odgs-databricks-bridge

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Quick Start

Python API

from odgs_databricks import DatabricksBridge

bridge = DatabricksBridge(
    workspace_url="https://adb-1234567890.azuredatabricks.net",
    token="dapi...",
    organization="acme_corp",
)

# Sync all tables from a catalog → ODGS metric definitions
bridge.sync(
    catalog="production",
    output_dir="./schemas/custom/",
    output_type="metrics",
)

# Sync column constraints → ODGS enforcement rules
bridge.sync(
    catalog="production",
    schema_filter="finance",
    output_dir="./schemas/custom/",
    output_type="rules",
    severity="HARD_STOP",
)

CLI

# Using standard Databricks SDK environment variables
export DATABRICKS_HOST=https://adb-1234567890.azuredatabricks.net
export DATABRICKS_TOKEN=dapi...

odgs-databricks sync \
    --org acme_corp \
    --catalog production \
    --schema finance \
    --type rules \
    --severity HARD_STOP \
    --output ./schemas/custom/

# Push compliance results back to Unity Catalog table comments
odgs-databricks write-back \
    --log-path ./sovereign_audit.log \
    --url https://adb-1234567890.azuredatabricks.net \
    --token dapi...

Output Schema

{
  "$schema": "https://metricprovenance.com/schemas/odgs/v5",
  "metadata": {
    "source": "databricks",
    "organization": "acme_corp",
    "tables_processed": 12,
    "items_generated": 47
  },
  "items": [
    {
      "rule_urn": "urn:odgs:custom:acme_corp:rule:revenue_amount_not_null",
      "name": "revenue.amount NOT NULL",
      "severity": "HARD_STOP",
      "constraint_type": "NOT_NULL",
      "target_table": "production.finance.revenue",
      "plain_english_description": "The revenue amount field must never be null in financial transaction records",
      "content_hash": "a1b2c3..."
    }
  ]
}

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Bi-Directional Write-Backs

The bridge supports Bi-Directional Sync: it parses your sovereign_audit.log offline and pushes compliance results back into Unity Catalog table comments — creating a deterministic feedback loop for Data Stewards without compromising the air-gapped nature of the core ODGS protocol.

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Authentication

Method	CLI Flag	Environment Variable
Personal Access Token	--token	DATABRICKS_TOKEN
Workspace URL	--url	DATABRICKS_HOST

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Regulatory Alignment

This bridge is designed for organisations governed by:

Regulation	Relevance
DORA (Regulation EU 2022/2554)	ICT resilience — data lineage and operational incident traceability across Databricks workloads
EU AI Act (2024/1689) Articles 10 & 12	Training data governance and audit trail requirements for High-Risk AI Systems built on Databricks
Basel Committee BCBS 239	Risk data aggregation — accuracy and integrity of data sourced from Unity Catalog

For cryptographic legal indemnity (Ed25519 JWS audit seals, certified Sovereign Packs for DORA/EU AI Act), see the Metric Provenance Enterprise Platform.

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Requirements

• Python ≥ 3.9
• odgs ≥ 5.1.0 (core protocol — v6.0 compatible)
• Databricks workspace with Unity Catalog enabled

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Related

• ODGS Protocol — The core enforcement engine
• ODGS FLINT Bridge — TNO FLINT legal ontology connector
• ODGS Collibra Bridge — Collibra integration
• ODGS Snowflake Bridge — Snowflake integration

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License

Apache 2.0 — Metric Provenance | The Hague, NL 🇳🇱