Episode 11 — 1.2 Compare Repositories: Data Lakes, Lakehouses, Marts, Warehouses, Silos
This episode clarifies the repository options that show up repeatedly in Data+ DA0-002 scenarios, especially when a prompt asks where data should live and how it should be organized for analysis and reporting. You will distinguish a data lake as low-friction storage for varied, often raw data from a data warehouse as curated, structured, and performance-oriented storage designed for consistent querying. You will also define a data mart as a narrower, purpose-built subset that supports a specific team or function, and a lakehouse as an approach that blends lake flexibility with stronger management and query performance characteristics. The exam expects you to recognize these terms and select the repository type that fits constraints such as governance needs, data variety, and speed of access. You will also address silos as a pattern that undermines shared definitions and creates conflicting metrics.
You will apply repository thinking to realistic scenarios like enterprise reporting, departmental analytics, and cross-team metric reconciliation. You will practice identifying what “curated” means in context, how schema enforcement and metadata influence trust, and how refresh timing can create disagreements when different repositories run on different schedules. You will also cover common failure modes tested on the exam, such as a mart drifting from the warehouse definition of a KPI, or a lake accumulating data without sufficient cataloging to make it discoverable. By the end, you can explain the tradeoffs in plain language and justify a choice based on cost, governance, and query patterns rather than buzzwords. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
