Catalogs, Schemas, and Tables in Databricks

Concept: Databricks organizes data using a three-level namespace: catalog.schema.table

Structure:

• Catalog: Top-level container (e.g., example).
• Schema: Logical grouping of related tables (e.g., databricks).
• Table: Dataset stored in Delta format (e.g., databricks_test).

Example Commands:

spark.sql("CREATE CATALOG IF NOT EXISTS example")
spark.sql("CREATE SCHEMA IF NOT EXISTS example.databricks")
df.write.mode("overwrite").saveAsTable("example.databricks.databricks_test")

Benefits:

• Clear separation of data domains.
• Simplified access control and governance.
• Consistent naming and dCatalogs, Schemas, and Tables in Databricks- Delta Tables and Catalogs

In Databricks, all managed data assets - including Delta Tables in Databricks are organized hierarchically within the Unity Catalog system.

Hierarchy

catalog.schema.table

• Catalog: Top-level container (e.g., main, example, samples).
• Schema: Logical grouping of related tables within a catalog.
• Table: Actual data entity (e.g., Delta table, view, or external table).

Example:

SELECT * FROM example.sales.customers;

Here:

• example = catalog
• sales = schema
• customers = Delta Table

How Delta Tables Fit In

A Delta Table can be: Managed (inside Databricks) — stored within a specific catalog and schema.

• Databricks manages both the metadata and physical files.

• Example:

  df.write.format("delta").saveAsTable("example.sales.transactions")

Why Catalogs Matter

Catalogs in Databricks:

• Provide governance via Unity Catalog (access control, lineage, auditing).
• Enable data discovery across teams.
• Help enforce consistent naming conventions and permissions.

Typical Structure

Level	Example Name	Description
Catalog	example	Project or business domain
Schema	sales	Logical grouping (e.g., transactions, customers)
Delta Table	transactions	The actual dataset stored in Delta format