An organization is migrating to a cloud-native analytics stack. It wants a single repository that can store raw semi-structured telemetry JSON alongside structured sales tables on low-cost object storage; expose ANSI-SQL query capabilities with full ACID compliance so finance dashboards never see inconsistent data; and let data scientists run machine-learning notebooks directly against the same files without copying them elsewhere. Which data repository architecture best satisfies all of these requirements?
A data lakehouse combines the inexpensive, schema-on-read storage of a data lake with the metadata management, ACID transaction support, and SQL query performance traditionally found in data warehouses. Because of this hybrid design, it can hold both raw JSON files and highly structured tables while still supporting consistent BI reporting and direct data-science workloads. A plain data lake provides flexible storage but lacks built-in ACID guarantees or strong SQL semantics; a relational data warehouse delivers SQL and transactions but cannot cost-effectively ingest large volumes of raw, semi-structured data; and a data mart is simply a subject-area subset that depends on one of the other repositories. Therefore, only a data lakehouse meets all of the listed requirements.
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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