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Answer: A data lakehouse stores unstructured data and is ACID-compliant.
A lakehouse combines the best aspects of data lakes (supporting unstructured data like video, flexible storage formats) and data warehouses (ACID transactions, schema enforcement, and reliable governance), making it well-suited for both video-based machine learning and highly audited batch ETL/ELT workloads. Specifically, it can store large unstructured data such as video and still provide ACID transactions and schema enforcement for audited batch operations. Option A is incorrect because modeling is still important; option B is incorrect because lakehouses decouple compute and storage (not combine) for scalability and governance; option C is true of some platforms but not a defining characteristic addressing the requirement; option E is not universally true and is not relevant to the workload needs.
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A data architect is designing a data model that works for both video-based machine learning workloads and highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of both workloads?
A
A data lakehouse requires very little data modeling.
B
A data lakehouse combines compute and storage for simple governance.
C
A data lakehouse provides autoscaling for compute clusters.
D
A data lakehouse stores unstructured data and is ACID-compliant.
E
A data lakehouse fully exists in the cloud.