Databricks SQL concepts
This page introduces the core concepts you need to use Databricks SQL effectively.
Interfaces and tools
Interface | Description |
|---|---|
A browser-based interface for writing and running SQL queries. Includes integrated AI assistance, code comments, and version history. | |
A business intelligence solution that includes dashboards and Genie spaces for self-service data analysis and conversational data exploration. | |
Interactive documents that combine SQL with Python, Scala, or R. Attach a notebook to a SQL warehouse to run SQL alongside other languages. | |
A semantic layer for defining reusable business metrics with consistent calculations across queries and dashboards. | |
Automated monitors that evaluate query results against a condition and deliver notifications when the condition is met. | |
Scheduled execution of SQL queries for automated data processing and reporting workflows. | |
Define and refresh streaming tables and materialized views in Databricks SQL for incremental ETL pipelines. | |
A programmatic interface for automating and managing Databricks SQL objects. |
Queries and computation
Concept | Description |
|---|---|
A SQL statement that retrieves or transforms data. Queries in Databricks SQL are saved, versioned, and reusable. | |
The compute resource that executes SQL queries. All Databricks SQL interfaces run queries on a SQL warehouse. | |
A record of executed queries including execution time, resource usage, and performance characteristics. | |
A detailed view of a query's execution plan. Use it to identify bottlenecks and optimization opportunities. | |
A graphical representation of query results, such as a bar chart, line chart, or table. |
Data management
Concept | Description |
|---|---|
A collection of visualizations and commentary built from one or more queries. Dashboards are shareable and support interactive filtering. | |
A named, reusable definition of a business metric backed by a SQL expression. Enforces consistent calculations across teams and tools. | |
A table that ingests data incrementally from a streaming source, managed by a Databricks SQL pipeline. | |
A precomputed view whose results are stored and refreshed automatically by a Databricks SQL pipeline. |
Authentication and authorization
Concept | Description |
|---|---|
User and group | A user is a unique individual with access to the Databricks workspace. A group is a collection of users managed together for access control purposes. |
A credential used to authenticate to the REST API and to connect third-party tools to SQL warehouses. | |
A set of permissions attached to an object that controls which principals can perform which actions on it. | |
The governance layer for Databricks. Provides centralized access control, auditing, lineage, and data discovery across workspaces. |