Data Catalog and Metadata Management Cheat Sheet

Updated 2026-04-12

Data catalogs and metadata management form the central nervous system of modern data platforms, providing discovery, governance, and lineage capabilities across distributed architectures. A data catalog is a user-facing inventory that helps teams find and understand data assets, while metadata management is the broader discipline of capturing, storing, and governing information about data—schema, lineage, quality, ownership, and usage. In 2026, the convergence of active metadata, AI-powered classification, and multi-cloud integration has transformed catalogs from passive documentation into intelligent systems that enforce governance, detect drift, and power agentic workflows. Understanding the distinction between technical metadata (schemas, types, lineage) and business metadata (glossaries, ownership, policies) is essential: technical metadata enables traceability and system-level accuracy, while business metadata ensures that non-technical users can confidently interpret what the data means.

What This Cheat Sheet Covers

This topic spans 15 focused tables and 99 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Metadata TypesTable 2: Catalog Architecture PatternsTable 3: Metadata Collection MethodsTable 4: Search and Discovery CapabilitiesTable 5: Data Lineage TypesTable 6: Data Classification and TaggingTable 7: Governance IntegrationTable 8: Open-Source Catalog ToolsTable 9: Enterprise Catalog PlatformsTable 10: Cloud-Native Catalog ServicesTable 11: Collaboration FeaturesTable 12: Access Control ModelsTable 13: Catalog Integration PatternsTable 14: Data Quality IntegrationTable 15: Advanced Capabilities

Table 1: Metadata Types

Type	Example	Description
Technical Metadata	`table: users, column: email, type: VARCHAR(255), pk: user_id`	Describes schema structure, data types, primary keys, foreign keys, indexes — the machine-readable contract for how data is stored and accessed.
Business Metadata	`term: "Active Customer", definition: "User with purchase in last 90 days", owner: Marketing`	Human-readable context defining what data means, who owns it, usage rules, and domain-specific definitions from business glossaries.
Operational Metadata	`last_refresh: 2026-04-12 03:00 UTC, rows_processed: 1.2M, status: success`	Tracks runtime behavior — job execution times, row counts, success/failure status, pipeline latency, and data freshness.
Semantic Metadata	Knowledge graph linking `customer.id` → `orders.customer_id` → `CustomerMetrics.cust_key`	• Captures relationships and meaning across datasets using ontologies or knowledge graphs • powers intelligent search and cross-domain lineage.

Table 1: Metadata Types

Type	Example	Description
Technical Metadata	`table: users, column: email, type: VARCHAR(255), pk: user_id`	Describes schema structure, data types, primary keys, foreign keys, indexes — the machine-readable contract for how data is stored and accessed.
Business Metadata	`term: "Active Customer", definition: "User with purchase in last 90 days", owner: Marketing`	Human-readable context defining what data means, who owns it, usage rules, and domain-specific definitions from business glossaries.
Operational Metadata	`last_refresh: 2026-04-12 03:00 UTC, rows_processed: 1.2M, status: success`	Tracks runtime behavior — job execution times, row counts, success/failure status, pipeline latency, and data freshness.
Semantic Metadata	Knowledge graph linking `customer.id` → `orders.customer_id` → `CustomerMetrics.cust_key`	• Captures relationships and meaning across datasets using ontologies or knowledge graphs • powers intelligent search and cross-domain lineage.