Data governance

As explained in Snowflake Data Engineering

Data governance is the framework of policies, roles, standards, and controls for managing data availability, quality, security, and compliance.

Data governance provides the policies, roles, standards, and controls needed to manage data availability, quality, security, and compliance across a Snowflake data pipeline. It operates alongside extraction, ingestion, transformation, and presentation rather than as a separate pipeline stage. section 1.2 section 10.0

A governed pipeline incorporates access requirements while data is being built so users see only information they are authorized to access. Snowflake implements this boundary through role-based access control and policy-based restrictions. section 10.0

Core Snowflake governance mechanisms include:

• Role-based access control assigns access privileges to roles and grants those roles to users. section 10.0
• Row access policies restrict records by business unit, supporting multitenant data stores where units share a database. section 10.0
• Masking policies hide sensitive personal or financial information when users query tables. section 10.0

Bakery business-unit controls

A bakery separates data engineer and data analyst roles: engineers read and write data, while analysts read only. As bread and pastry operations expand, each business-unit manager sees only that unit’s records, and personal data appears masked in query results. section 10.0

Figure 1.2 Data engineering components with Snowflake include extracting data from source systems, ingesting the data into Snowflake, performing transformations, and presenting the data to downstream consumers, incorporating underlying components and best practices related to security, data modeling, data governance, orchestration, software engineering, and DataOps.

Use the pipeline-component figure to place governance beside security, data modeling, orchestration, software engineering, and DataOps across the Snowflake delivery flow. section 1.2

How Snowflake Data Engineering covers it

How deep it goes

Explains RBAC, row access policies, and masking policies with business-unit and sensitive-data scenarios, without detailing broader governance operations.

Examples

Uses a bakery with data-engineer and data-analyst roles, separate business units, restricted rows, and masked personal data.

What you can do after

Operate/configure Snowflake RBAC, row access policies, and masking policies for role, business-unit, and sensitive-data restrictions.

How it connects

Connects security, data modeling, orchestration, DataOps, role-based access control, row access policies, and masking policies.

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Related concepts

• data contractpart of · A data contract is an agreement specifying the structure, meaning, and expectations for data exchanged between producers and consumers.
• AI Actconfigured by · The AI Act is a European Union regulation establishing harmonised, risk-based requirements for the development, placing on the market, and use of artificial intelligence systems.
• GDPRconfigured by · The General Data Protection Regulation (GDPR) is a European Union regulation governing the processing of personal data and protecting individuals’ rights concerning that data.
• lakehouse catalogconfigured by · A catalog is a service or interface that registers lakehouse tables, resolves their metadata, and coordinates table updates across data engines.
• first-party data strategypart of · A coordinated plan for collecting, storing, governing, analyzing, and activating an organization’s first-party data.

This explainer was generated by Manning's knowledge graph from the text of Snowflake Data Engineering. The section links point to the parts of the book it draws on.