Databricks Data Analyst key terms

A data architecture that combines the cheap, flexible storage of a data lake with the management and performance features of a data warehouse, letting one copy of data serve BI, SQL, and machine learning workloads.

Databricks' centralized governance layer that stores metadata about catalogs, schemas, tables, volumes, and permissions, and enforces access control across all workspaces attached to a metastore.

The top-level Unity Catalog container that holds catalogs and is assigned to one cloud region; workspaces are attached to it so users share the same objects and permissions.

An open table format that adds a transaction log on top of Parquet files, providing ACID transactions, schema enforcement, and versioned history for tables stored in cloud object storage.

A layered data design pattern where raw ingested data lands in a bronze layer, is cleaned and conformed in silver, and is aggregated into business-ready gold tables for reporting.

The browser-based environment where users create notebooks, run queries, build dashboards, and manage compute; each workspace is tied to a cloud region and an attached metastore.

The cluster of virtual machines that actually runs SQL queries and notebook code. Its size and type determine cost, concurrency, and how fast workloads finish.

The first level of the Unity Catalog three-part namespace, grouping related schemas (databases) that usually belong to one project, team, or environment such as dev or prod.

A container inside a catalog that holds tables, views, volumes, and functions; it is the middle part of the three-level name used when referencing an object in SQL.

A table whose data files are owned and placed by Databricks inside the metastore's managed storage location; dropping the table deletes both metadata and underlying data.

A table whose data files live at a path you specify, often in a cloud storage location; dropping the table removes only the metadata and leaves the files untouched.

A Delta Lake feature that lets you query an earlier version or timestamp of a table using a version or timestamp clause, useful for auditing changes or recovering accidentally modified data.

A command that compacts many small Delta files into fewer larger ones and can also apply clustering, improving read performance for downstream queries.

A maintenance command that deletes data files no longer referenced by the table and are older than a retention threshold, freeing storage but reducing how far back time travel can go.

A table layout technique that lets Databricks automatically organize data by chosen columns without fixed partitions, and adapt the layout as new data arrives.

A SQL command that loads files from a cloud storage location into a Delta table, tracking which files were already processed so repeated runs do not duplicate data.

An incremental ingestion mechanism that detects new files arriving in cloud storage and processes them efficiently at scale, tracking discovered files so each is loaded once.

A Databricks file system layer that mounts cloud storage so files can be browsed from the workspace; many paths under it are legacy and are not governed by Unity Catalog permissions.

A compute resource purpose-built for running SQL and serving BI tools. It has configurable size and scaling behavior and is separate from general-purpose clusters used for notebooks.

A SQL warehouse whose compute is fully managed by Databricks, starting quickly and scaling automatically with no cluster configuration required from the user.

The Databricks SQL interface where analysts write, run, and save SQL statements against a selected warehouse, view results as tables or charts, and share queries with others.

A named placeholder in a SQL statement that receives its value at run time from a widget, dashboard filter, or API call, allowing one saved query to serve many scenarios.

A log of executed statements showing who ran what, on which warehouse, with duration and status, used for auditing, troubleshooting failures, and comparing performance over time.

A visual breakdown of how a query executed, showing operators, rows processed, time spent per stage, and where time or data volume bottlenecks occurred.

Databricks' vectorized query engine written in C++ that accelerates SQL and DataFrame workloads on Databricks compute, often reducing query run time substantially.

A warehouse-level cache that stores the output of a previously run query so an identical repeat query returns almost instantly without rescanning the underlying data.

A shareable collection of saved queries, visualizations, and filters presented on one page, with its own permission settings and optional refresh schedules.

A chart or table configuration applied to a query result set, choosing axes, series, aggregation, and chart type so trends and comparisons are easy to read.

A configured AI/BI assistant environment where business users ask questions in natural language and receive answers backed by chosen tables and curated instructions.

A curated item in a Genie space, such as a sample question, example SQL statement, or defined measure, that guides the assistant toward correct answers for common requests.

A stored set of representative questions and expected answers used to evaluate whether a Genie space responds accurately, helping authors spot gaps before sharing it broadly.

A Databricks SQL object that defines reusable measures, dimensions, and joins on top of source tables, so consistent business metrics can be queried without rewriting aggregation logic.

A dimensional model where a central fact table stores measurable events and surrounding dimension tables hold descriptive attributes, joined by keys for simple, fast analytical queries.

A database object that stores the precomputed result of a query and can be refreshed incrementally, trading storage and refresh time for faster reads by consumers.

The SQL statement used to give a user, group, or service principal a specific privilege such as SELECT or MODIFY on a catalog, schema, table, or view.

A Unity Catalog table or view property that attaches a SQL function to restrict which rows each user can see based on their identity or group membership.

A Unity Catalog feature that applies a function to a column so different users see different values, such as full values for one team and masked values for another.

A non-human identity used by applications, jobs, and automated tools to authenticate to Databricks and be granted permissions, instead of sharing a person's credentials.