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2.1 Dataset Discovery & Asset Tagging

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Databricks Certified Data Analyst Associate Exam

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1 Platform Overview & Unity Catalog Foundations

1.1 Databricks Architecture & Compute Concepts 1.2 Databricks Marketplace & Data Sharing Basics 1.3 Unity Catalog Namespace & Object Hierarchy 1.4 Governed Asset Discovery & System Tables

2 Managing & Cleaning Governed Data

2.1 Dataset Discovery & Asset Tagging 2.2 Lineage Inspection & Data Traceability 2.3 SQL Data Cleaning & Transformation Techniques

3 Data Ingestion & Import Patterns

3.1 Workspace Uploads & Cloud File Ingestion 3.2 Incremental File Ingestion with Auto Loader 3.3 Ingesting Data via Delta Sharing & External Connections

4 Databricks SQL Warehouses & Core Querying

4.1 Databricks SQL Interface & SQL Assistant 4.2 SQL Warehouses Sizing & Management 4.3 Table Creation & Data Modification in Databricks SQL

5 Advanced SQL Authoring & Time Travel

5.1 Advanced Aggregations, Window Functions & Analytics 5.2 Complex Joins, CTEs & Set Operations 5.3 Views, Materialized Views & Delta Time Travel

6 Query Performance Analysis & Photon Engine

6.1 Photon Engine Architecture & Acceleration 6.2 Query History & Execution Profile Inspection 6.3 Delta Lake Audit History & Commit Logs

7 Liquid Clustering, Caching & Performance Troubleshooting

7.1 Caching Hierarchy in Databricks SQL 7.2 Data Layout Optimization: Liquid Clustering vs Partitioning & Z-Ordering 7.3 Troubleshooting Slow & Failing Queries

8 AI/BI Dashboards & Visualization Design

8.1 AI/BI Dashboards Overview & Canvas Layout 8.2 Chart Types & Visualization Selection 8.3 Notebook & SQL Editor Visualizations

9 Dashboard Interactivity, Publishing & Alerts

9.1 Dashboard Parameters & Interactive Filtering 9.2 Publishing, Sharing & Access Control 9.3 Scheduled Refresh, Email Subscriptions & Alerts

10 Developing & Maintaining AI/BI Genie Spaces

10.1 AI/BI Genie Architecture & Space Setup 10.2 Curation via Instructions, Descriptions & Sample Questions 10.3 Trusted Assets & Parameterized Functions 10.4 Monitoring, Benchmarks, Feedback & Maintenance

11 Data Modeling & Medallion Architecture

11.1 Dimensional Modeling Fundamentals 11.2 Medallion Architecture for Analytics 11.3 Constraint Enforcement & Relationships in Unity Catalog

12 Securing Data & Unity Catalog Governance

12.1 Unity Catalog Privilege Model & Grants 12.2 Ownership, Inheritance & Access Control Patterns 12.3 Fine-Grained Security: Row Filters & Column Masks

2.1 Dataset Discovery & Asset Tagging

Key Takeaways Explain This Topic AI

Unity Catalog tag keys are case-sensitive and limited to 255 characters; tag values allow up to 1,000 characters, with a maximum of 50 tags per securable object.

Workspace search in Databricks SQL indexes catalog names, schemas, tables, columns, tags, and comments within 30 seconds of metadata creation.

Tags can be applied to catalogs, schemas, tables, and individual columns using ALTER TABLE ... SET TAGS ('PII' = 'true', 'Tier' = 'Gold') statements or the Catalog Explorer UI.

System tables information_schema.table_tags and column_tags store tag assignments, enabling programmatic governance audits across up to 10,000+ workspace assets.

Unity Catalog marks trusted assets with the system tag system.certification_status=certified , shown as a check mark in Catalog Explorer for analyst discovery.

Last updated: July 2026

2.1 Dataset Discovery & Asset Tagging

Data discovery and asset tagging form the cornerstone of effective data governance within the Databricks Data Intelligence Platform . As organizations expand their lakehouses to house petabytes of analytical data across thousands of tables, data analysts must be equipped to quickly locate, evaluate, and categorize governed data assets. Unity Catalog provides a centralized governance layer that unifies dataset discovery, rich metadata annotation, key-value asset tagging, and automated system auditing across all workspace environments.

Discovering Governed Assets in Unity Catalog

Finding the right dataset quickly is critical for building accurate analytical queries, dashboards, and machine learning features. Databricks combines visual navigation with intelligent search engines to simplify dataset discovery.

Catalog Explorer and Workspace Search

Catalog Explorer serves as the primary visual interface for inspecting Unity Catalog objects. It enables data analysts to browse through the three-level namespace hierarchy ( catalog.schema.table_or_view ), inspect table schemas, view data previews, examine table properties, and verify access permissions.

Complementing Catalog Explorer, Databricks Workspace Search provides a universal search bar powered by Databricks IQ . The search engine indexes metadata across the workspace within 30 seconds of object creation or alteration. Key search capabilities include:

Multi-attribute Indexing : Workspace search indexes catalog names, schema names, table names, column names, column comments, table tags, and column tags.

Semantic Search : Users can search using plain language terms (e.g., "customer churn 2025") to find relevant tables even if exact column names differ.

Faceted Filtering : Analysts can narrow search results by catalog, schema, asset owner, object type (table, view, volume, model), and specific tag key-value pairs.

Unity Catalog Object Hierarchy and Metadata

All discoverable assets in Databricks adhere to the three-level namespace :

Catalog : The high-level container for organizing data assets (e.g., main , finance_prod , sandbox ).

Schema (Database) : The logical grouping of tables, views, functions, and volumes within a catalog (e.g., sales , hr , raw ).

Table / View / Volume : The leaf objects containing data rows or files (e.g., dim_customers , fact_transactions ).

Rich documentation enhances discoverability. Analysts can add explicit comments to tables and columns using standard SQL statements: -- Annotating table and column metadata

  • COMMENT ON TABLE main.finance_prod.fact_transactions
  • IS 'Contains finalized enterprise sales transactions processed daily via Delta Live Tables.';
  • ALTER TABLE main.finance_prod.fact_transactions
  • ALTER COLUMN transaction_amount COMMENT 'Net transaction amount in USD after applying discounts and taxes.';

Databricks IQ also offers AI-generated documentation , suggesting descriptions for tables and columns based on schema structure, sample data distributions, and column names. Analysts can review, edit, and accept these AI suggestions with one click in Catalog Explorer.

Certified Datasets in Unity Catalog

The Databricks Data Analyst Associate exam explicitly tests discovering, querying, cleaning, and managing certified datasets . In Unity Catalog, certification is not a separate object type — it is a governed trust signal applied through the system tag system.certification_status .

  • Tag value Catalog Explorer UI signal Analyst meaning
  • certified Check-mark badge on the asset Curators have validated this table/view as trusted for analytical use
  • deprecated Restricted / warning icon Prefer alternatives; treat as sunsetting or unreliable for new work
  • How analysts discover certified datasets

Open Catalog Explorer and look for the certification badge on tables and views.

Search workspace assets and filter by the certification status when available.

Query governance metadata to automate discovery: -- Apply certification (requires ASSIGN on system.certification_status)

SET TAG ON TABLE main.gold.orders `system.certification_status` = `certified`;

  • - Discover certified tables via tag metadata
  • SELECT catalog_name, schema_name, table_name, tag_name, tag_value
  • FROM system.information_schema.table_tags
  • WHERE tag_name = 'system.certification_status'
  • AND tag_value = 'certified';

Workspace search also supports type:table certificationStatus:certified to filter certified tables.

Analyst workflow implications

Prefer certified gold/silver tables for dashboards, Genie spaces, and exam-style “trusted source” scenarios.

Certification does not replace privileges: you still need USE CATALOG , USE SCHEMA , and SELECT (or equivalent) to query the data.

Owners or users with APPLY TAG (and governed-tag ASSIGN where required) set or clear certification; analysts primarily consume the signal.

Pair certification with lineage and comments: a certified badge answers “is this trusted?”, while lineage answers “where did it come from?”

Implementing Metadata and Asset Tagging

While descriptive comments provide human-readable context, asset tagging enables structured categorization, programmatic policy enforcement, and regulatory compliance tracking across the entire data estate.

Tag Structure and Governance Standards

Tags in Unity Catalog are key-value pairs assigned directly to catalogs, schemas, tables, views, and individual columns. Standardizing tag conventions ensures data consistency across enterprise business units.

Character Limits : Tag keys allow up to 255 characters; tag values allow up to 1,000 characters. You can assign a maximum of 50 tags to a single securable object, and a table can have at most 1,000 column tags across all columns.

Case Sensitivity : Tag keys are case-sensitive. For example, Sales and sales are two distinct tags in Unity Catalog.

Tag Inheritance : Tags applied at the catalog or schema level do not automatically propagate down to child tables or columns as active inherited attributes. However, catalog and schema tags serve as logical groupings during discovery and audit reporting.

Common enterprise tagging frameworks include:

Data Sensitivity / PII : Key: PII , Values: true , false , Direct_Identifier , Quasi_Identifier .

Data Quality Tier : Key: Tier , Values: Bronze , Silver , Gold .

Asset Ownership : Key: Owner_Team , Values: Finance-Analytics , Marketing-Ops .

Compliance Scope : Key: Compliance , Values: GDPR , CCPA , HIPAA , SOX .

Applying Tags via SQL and UI

Analysts and data stewards can apply tags interactively through the Catalog Explorer GUI or programmatically via Databricks SQL execution. -- Applying table-level key-value tags

  • ALTER TABLE main.finance_prod.dim_customers
  • SET TAGS (
  • 'Tier' = 'Gold',
  • 'Owner_Team' = 'Customer-Analytics',
  • 'Compliance' = 'GDPR'
  • );
  • - Applying column-level key-value tags for PII tracking
  • ALTER TABLE main.finance_prod.dim_customers
  • ALTER COLUMN ssn SET TAGS ('PII' = 'Direct_Identifier', 'Confidentiality' = 'Restricted');
  • ALTER TABLE main.finance_prod.dim_customers
  • ALTER COLUMN email SET TAGS ('PII' = 'Quasi_Identifier', 'Confidentiality' = 'Confidential');
  • - Removing specific tags from a table
  • ALTER TABLE main.finance_prod.dim_customers
  • UNSET TAGS ('Compliance');
  • Auditing Governance via Information Schema

To verify governance compliance at scale, data analysts can query Unity Catalog's read-only Information Schema system tables. The Information Schema provides standardized SQL views detailing catalog structure, permissions, and active tag assignments.

Querying System Tables for Tag Audit

The primary system views for inspecting asset tags are information_schema.table_tags and information_schema.column_tags . These views can be joined with information_schema.tables and information_schema.columns to perform comprehensive compliance reporting across 10,000+ workspace assets.

Information Schema View Key Columns Primary Use Case

information_schema.table_tags catalog_name , schema_name , table_name , tag_name , tag_value Auditing table-level classification and ownership tags

information_schema.column_tags catalog_name , schema_name , table_name , column_name , tag_name , tag_value Identifying PII and sensitive columns across schemas

information_schema.tables table_catalog , table_schema , table_name , table_type , created , last_altered Listing active tables, views, and creation timestamps -- Identifying all un-tagged Gold tables in the finance catalog

  • SELECT
  • t.table_schema,
  • t.table_name,
  • t.table_type
  • FROM main.information_schema.tables t
  • LEFT JOIN main.information_schema.table_tags tag
  • ON t.table_schema = tag.schema_name
  • AND t.table_name = tag.table_name
  • AND tag.tag_name = 'Tier'
  • WHERE t.table_schema = 'finance_prod'
  • AND tag.tag_value IS NULL;
  • Real-World Scenario: Compliance Audit for PII Discovery

Suppose a lead data analyst at a healthcare financial firm must prepare for a European GDPR compliance audit scheduled for May 2026. The auditor requires a list of all columns across the patient_billing schema that store direct personal identifiers.

Tag Application : The data engineering team tags candidate columns using ALTER TABLE patient_billing.claims ALTER COLUMN national_id SET TAGS ('PII' = 'Direct_Identifier') .

Programmatic Audit : The analyst executes the following query against information_schema.column_tags to generate the compliance report: SELECT

  • table_name,
  • column_name,
  • tag_value AS pii_classification
  • FROM main.information_schema.column_tags
  • WHERE schema_name = 'patient_billing'
  • AND tag_name = 'PII'
  • ORDER BY table_name, column_name;

Validation : The resulting output provides audit proof of all tagged PII elements, enabling governance teams to configure downstream Unity Catalog fine-grained row/column access control policies.