09. Databricks Terminology

Understanding the vocabulary is one of the fastest ways to become productive on Databricks.

Term	Meaning
Account	Top-level administrative scope
Workspace	Development and execution environment
Compute	Processing resources
SQL warehouse	SQL-oriented compute
Notebook	Interactive development interface
Job	Automated workload definition
Task	Unit of work inside a job
Delta Lake	Reliable lakehouse table/storage layer
Unity Catalog	Governance layer for data and AI
Metastore	Top-level Unity Catalog metadata container
Catalog	First level of the three-level namespace
Schema	Second level
Table	Structured data object
View	Logical query definition
Volume	Governed file-oriented object
Auto Loader	Incremental file ingestion
Structured Streaming	Spark streaming model
Lakeflow	Databricks data engineering and pipeline capabilities
Photon	Databricks native execution engine
Medallion	Bronze/Silver/Gold organization pattern
Lineage	Relationships between upstream/downstream assets

Three-level namespace

Unity Catalog uses:

catalog.schema.object

Example:

SELECT *
FROM sales_prod.orders.customer_orders;

Here sales_prod is the catalog, orders is the schema, and customer_orders is the table.

Platform mental model

Account
  │
  ├── Workspace
  │     └── Compute / Jobs / Notebooks
  │
  └── Unity Catalog
        └── Metastore
              └── Catalog
                    └── Schema
                          └── Tables / Views / Volumes

Six boundaries to remember

Workspace → where you work.

Compute → where processing happens.

Storage → where durable data lives.

Delta → reliable lakehouse table layer.

Unity Catalog → governance and metadata.

Job → automation.

Once these boundaries are clear, advanced Databricks architecture becomes much easier to reason about.

Next

Continue with the next topic in 01. Databricks Foundations.