Fabric DP-700 key terms

A single, tenant-wide logical data lake that holds all Fabric data as Delta Parquet. Each workspace gets its own folder inside it, and items reference that storage rather than keeping private copies.

A Fabric item that pairs file storage with managed Delta tables and an auto-generated SQL endpoint, so the same data can be queried with Spark or T-SQL without duplication.

A Fabric item offering a full T-SQL relational engine with multi-table transactions, views and stored procedures, storing its tables as Delta Parquet in OneLake.

A container that holds Fabric items such as lakehouses, notebooks, pipelines and reports, and is where you assign roles, attach capacity and connect source control.

The pool of compute, measured in capacity units, that powers every workload in a workspace. It can be paused, resized, and throttles work when demand exceeds its size.

An object in OneLake that points to data stored elsewhere, such as another lakehouse, ADLS Gen2, S3 or Dataverse, making it appear locally without being copied.

An open table format made of Parquet data files plus a JSON transaction log that records commits, giving ACID guarantees and time travel. Fabric stores all OneLake data this way.

The read-only T-SQL surface automatically created for a lakehouse or mirrored database, exposing Delta tables as queryable tables and views without duplicating storage.

A Power BI storage mode that reads Delta Parquet files straight from OneLake, avoiding an import copy while keeping import-like speed; it can fall back to DirectQuery when needed.

The layer of tables, relationships, measures and security rules that reports query. It sits on top of a lakehouse, warehouse or other source and shapes how business users see data.

A named grouping of tables inside a lakehouse that organizes objects into folders and lets permissions be managed at the schema level rather than table by table.

Role definitions scoped to folders, schemas or tables in OneLake that grant or deny read access for users and groups, applying consistently across Spark, SQL and Power BI.

A rule that filters the rows a person can see based on their identity, implemented with a security predicate in T-SQL or with model roles in a semantic model.

A Fabric feature that promotes items through development, test and production workspaces, with rules that swap data source and parameter values at each stage.

Connecting a workspace to a Git repository so item definitions are versioned, changed on branches and synced between source control and the Fabric service.

A layered data design where raw ingested data lands in bronze, is cleaned and conformed into silver, and is modeled into gold tables ready for reporting.

A Fabric item that orchestrates activities such as copy, notebook, stored procedure and conditional control flow, started by a schedule, event or manual run.

A Fabric item focused on bulk or incremental data movement between a source and destination, requiring less configuration than a full orchestration pipeline.

A Power Query based item with a low-code graphical editor that extracts, transforms and loads data, writing results into lakehouses, warehouses or other supported destinations.

An interactive document of Spark code cells in PySpark, Spark SQL, Scala or R, attached to a lakehouse, used for exploration, transformation and ad hoc analysis.

A Fabric item that runs a script or compiled artifact against a Spark cluster on a schedule, without the interactive, cell-by-cell experience of a notebook.

A Fabric item that ingests streaming events from sources such as Event Hubs or Kafka, optionally transforms them in flight, and routes them to a destination.

A real-time analytics store holding one or more KQL databases, optimized for high-volume streaming data queried with Kusto Query Language and shown in real-time dashboards.

A central catalog in Fabric for discovering streaming sources, previewing data and subscribing to events so they can be consumed by eventstreams and other items.

Continuous replication of an external database such as SQL Server, Cosmos DB or Snowflake into OneLake, producing a near-real-time read-only copy without building pipelines.

A saved T-SQL routine in a Fabric warehouse that accepts parameters and executes one or more statements, often invoked by a pipeline activity to load or transform data.

A pattern that loads only rows newer than a stored high-water mark instead of reloading a whole table, cutting runtime and compute cost on repeated runs.

The Fabric page listing activity across items, including pipeline runs, notebook runs and refreshes, with status, duration and drill-through details for troubleshooting failures.

A Power BI app that reports capacity unit consumption, throttling and overload broken down by item and operation, used to right-size and tune a Fabric capacity.

A capacity behavior that spreads the cost of background operations over time so short bursts are accounted for gradually rather than causing immediate throttling.

The diagnostic interface for a Spark session showing jobs, stages, tasks, shuffles and executor memory, used to spot skew, spills and slow or failing notebook runs.

A Fabric warehouse feature that surfaces the most frequent and most expensive queries along with their plans and resource usage so they can be tuned.

Metadata describing how column values are distributed, used by the SQL engine to pick join orders and access paths; stale statistics often cause slow queries.

A write-time layout optimization that sorts and compresses Parquet files so Fabric engines read them faster, particularly benefiting Direct Lake and SQL queries.

A Delta maintenance command that rewrites many small files into fewer larger ones, optionally sorting or clustering rows, improving read performance for downstream queries.

A Delta command that deletes data files no longer referenced by the transaction log and older than a retention threshold, reclaiming storage and speeding file listing.

A Delta layout technique that co-locates related values across several columns into the same files, letting filtered queries skip more data during reads.

Physically splitting a table into folders by a column such as date so that queries filtering on that column read only the relevant files instead of scanning everything.