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Domain 2.2: Design Data IntegrationData IntegrationDatabricksDelta Lake

AZ-305 · Question 25 · Domain 2.2: Design Data Integration

Your data engineering team is building a 'Lakehouse' architecture using Azure Databricks.

They need a storage format that supports ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It must also allow them to perform 'time travel' queries to view previous versions of the data.

Which data format should you recommend?

Answer options:

A.

Parquet

B.

Delta Lake

C.

Avro

D.

JSON

How to approach this question

Keywords: 'ACID transactions', 'time travel', 'Databricks'. This defines the Delta Lake format.

Full Answer

B.Delta Lake✓ Correct
Delta Lake is the foundation of the Lakehouse architecture in Azure Databricks. It stores data in Parquet format but adds a transactional log. This enables ACID transactions, concurrent reads/writes, and 'time travel' (querying older versions of the data for auditing or rollbacks).

Common mistakes

Choosing Parquet. Delta Lake *uses* Parquet files under the hood, but Parquet alone does not provide ACID transactions.
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