Delta Live Tables Flashcards

What is Delta Live Tables (DLT) in the Databricks ecosystem?

Answer: A declarative ETL framework for building reliable data pipelines

Delta Live Tables is a declarative ETL framework that automates pipeline infrastructure management and enforces data quality constraints.

Which Python decorator is used to define a materialized Delta table in a DLT pipeline?

Answer: @dlt.table

The @dlt.table decorator defines a materialized table whose results are stored persistently as a Delta table.

What is the primary purpose of DLT Expectations?

Answer: To enforce data quality constraints and handle invalid records

DLT Expectations allow you to define data quality constraints with named rules and specify actions to take when records violate them.

Which DLT pipeline execution mode processes all available data and then stops?

Answer: Triggered mode

Triggered mode processes all available data at the time of the run and then terminates, making it suitable for scheduled batch workloads.

What does the CONSTRAINT clause do in a DLT SQL pipeline?

Answer: Specifies data quality expectations with a name and condition

The CONSTRAINT clause in DLT SQL defines a named data quality expectation with a boolean condition that each record must satisfy.

In DLT, what happens to records that violate an expectation configured with ON VIOLATION DROP ROW?

Answer: The invalid record is excluded from the output dataset

With ON VIOLATION DROP ROW, records failing the expectation are silently excluded from the output, and the metric is tracked in the event log.

Which of the following is a key benefit of DLT over manually written Spark pipelines?

Answer: DLT automatically manages dependencies between tables in the correct execution order

DLT infers the dependency graph between tables and automatically executes them in the correct order without manual orchestration.