Unity Catalog in Databricks Cheat Sheet

Updated 2026-04-21

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Unity Catalog is Databricks' unified governance layer for data and AI assets, built on a metastore and a three-level namespace (catalog → schema → object). It standardizes access control, auditing, lineage, and sharing across every workspace and compute type, and as of 2026 extends the same governance model to AI assets such as models, functions, and connections. A useful mental model is "secure by default": querying an object typically requires both container privileges (USE CATALOG + USE SCHEMA) and an object privilege (like SELECT), while governance objects (storage credentials, external locations) gate all cloud storage access. Since September 30 2025, Unity Catalog is mandatory for all new workspaces.

What This Cheat Sheet Covers

This topic spans 18 focused tables and 172 indexed concepts, 78 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core Namespace ObjectsTable 2: Metastore Setup & OperationsTable 3: Catalog & Schema DDLTable 4: Table & View DDLTable 5: Table Types & ConversionTable 6: Volumes (Non-tabular Data)Table 7: Cloud Storage Governance ObjectsTable 8: Privilege Model ConceptsTable 9: Grants & Core PrivilegesTable 10: Tags, Governed Tags & ABACTable 11: Row Filters & Column MasksTable 12: Metadata Discovery (Information Schema)Table 13: Audit, Lineage & System TablesTable 14: Sharing & Clean RoomsTable 15: Federation, Connections & Foreign CatalogsTable 16: Data Classification & AI GovernanceTable 17: Data Optimization & MaintenanceTable 18: Automation (REST API, CLI, IaC)

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Table 1: Core Namespace Objects

Everything in Unity Catalog hangs off the three-level namespace, and these are the objects you address with it. The metastore sits at the top, catalogs and schemas form the containers, and the rest — tables, views, volumes, functions, models, connections — are the actual assets you query and govern. Notice how broad the list has grown: ML models and external connections are first-class citizens here, which is exactly how UC extends one governance model across both data and AI.

Object	Example	Description
Metastore	`metastore → catalogs → schemas → objects`	• Top-level container for Unity Catalog metadata and permissions • one per region per account.
Catalog	`sales_catalog.analytics.orders`	• First namespace level that groups schemas • maps to environments, domains, or teams.
Schema	`sales_catalog.analytics`	Second namespace level (also called database) that groups tables, views, functions, volumes, and models.
Table	`SELECT * FROM sales_catalog.analytics.orders;`	• Structured dataset registered in a schema • can be managed, external, or foreign.
View	`SELECT * FROM sales_catalog.analytics.active_customers;`	Read-only named query that executes at read time.
Materialized View	`SELECT * FROM sales_catalog.analytics.daily_revenue;`	Precomputed view whose results are stored and refreshed incrementally.
Streaming Table	`SELECT * FROM sales_catalog.analytics.events_stream;`	Append-only managed table designed for incremental ingestion pipelines.
Volume	`"/Volumes/sales_catalog/analytics/raw_files/2026-01-01.csv"`	Governed storage for non-tabular files (managed or external).
Function	`SELECT sales_catalog.analytics.mask_ssn(ssn) FROM customers;`	Unity Catalog-governed UDF (SQL or Python) callable from any SQL context.
Model	`"sales_catalog.analytics.churn_model"`	ML model registered as a governed asset with versioning and lineage.
Metric View	`SELECT * FROM sales_catalog.analytics.revenue_metrics;`	Semantic layer object that defines reusable business metrics with dimensions and filters.
Connection	`my_postgres_conn`	Securable object that stores credentials and metadata for external systems (databases, HTTP APIs, MCP servers).

Table 2: Metastore Setup & Operations

The metastore is the regional root of everything, so getting it set up and bound correctly is the first thing any admin does. These tasks cover that account-level lifecycle — onboarding, binding workspaces, and the isolation and quota controls that decide which workspaces can see which catalogs.

Task	Example	Description
Get-started	`Admin Console → Data → Unity Catalog`	Onboarding flow to enable and start using Unity Catalog.

The rest of this section is part of the full cheat sheet

Table 3: Catalog & Schema DDL

The everyday SQL for managing the top two namespace levels. These CREATE / ALTER / DROP / DESCRIBE commands are how you carve up catalogs and schemas — typically one catalog per environment or domain — and USE CATALOG is the small but essential one that sets your default context so you can stop fully qualifying every name.

Command	Example	Description
CREATE CATALOG	`CREATE CATALOG sales_catalog;`	Creates a new catalog in the current metastore.

The rest of this section is part of the full cheat sheet

Table 4: Table & View DDL

One level deeper, this is the DDL for the objects that actually hold and shape data inside a schema. It spans plain tables and views through the more specialized materialized views, streaming tables, functions, and procedures — the full vocabulary for defining what lives in your schemas and how it's computed.

Command	Example	Description
CREATE TABLE	`CREATE TABLE sales_catalog.analytics.orders (order_id BIGINT, amount DECIMAL(10,2));`	Creates a table (format and location depend on options).

The rest of this section is part of the full cheat sheet

Table 1: Core Namespace Objects

Object	Example	Description
Metastore	`metastore → catalogs → schemas → objects`	• Top-level container for Unity Catalog metadata and permissions • one per region per account.
Catalog	`sales_catalog.analytics.orders`	• First namespace level that groups schemas • maps to environments, domains, or teams.
Schema	`sales_catalog.analytics`	Second namespace level (also called database) that groups tables, views, functions, volumes, and models.
Table	`SELECT * FROM sales_catalog.analytics.orders;`	• Structured dataset registered in a schema • can be managed, external, or foreign.
View	`SELECT * FROM sales_catalog.analytics.active_customers;`	Read-only named query that executes at read time.
Materialized View	`SELECT * FROM sales_catalog.analytics.daily_revenue;`	Precomputed view whose results are stored and refreshed incrementally.
Streaming Table	`SELECT * FROM sales_catalog.analytics.events_stream;`	Append-only managed table designed for incremental ingestion pipelines.
Volume	`"/Volumes/sales_catalog/analytics/raw_files/2026-01-01.csv"`	Governed storage for non-tabular files (managed or external).
Function	`SELECT sales_catalog.analytics.mask_ssn(ssn) FROM customers;`	Unity Catalog-governed UDF (SQL or Python) callable from any SQL context.
Model	`"sales_catalog.analytics.churn_model"`	ML model registered as a governed asset with versioning and lineage.
Metric View	`SELECT * FROM sales_catalog.analytics.revenue_metrics;`	Semantic layer object that defines reusable business metrics with dimensions and filters.
Connection	`my_postgres_conn`	Securable object that stores credentials and metadata for external systems (databases, HTTP APIs, MCP servers).

Table 2: Metastore Setup & Operations

Task	Example	Description
Get-started	`Admin Console → Data → Unity Catalog`	Onboarding flow to enable and start using Unity Catalog.

Table 3: Catalog & Schema DDL

Command	Example	Description
CREATE CATALOG	`CREATE CATALOG sales_catalog;`	Creates a new catalog in the current metastore.

Table 4: Table & View DDL

Command	Example	Description
CREATE TABLE	`CREATE TABLE sales_catalog.analytics.orders (order_id BIGINT, amount DECIMAL(10,2));`	Creates a table (format and location depend on options).