A data science team at a financial institution is architecting a system for regulatory compliance reporting. The system must guarantee transactional atomicity, consistency, isolation, and durability (ACID). It also requires strict schema-on-write enforcement for data integrity and auditability, while providing optimized performance for predefined, complex analytical queries. Given these critical requirements, which storage concept is the most appropriate choice?
The correct answer is a relational database management system (RDBMS). RDBMS are fundamentally designed around the principles of ACID compliance and strict, predefined schemas (schema-on-write). These features are critical for financial and regulatory applications where data integrity, consistency, and auditability are non-negotiable.
A document-oriented NoSQL database is incorrect because its primary advantage is schema flexibility (dynamic schema), which is the opposite of the strict schema enforcement required for this use case.
A key-value store is not suitable as it is designed for simple data retrieval based on a key and does not support the complex analytical queries needed for reporting.
A data lake using Parquet files, while efficient for analytics, is a schema-on-read architecture that prioritizes flexibility and typically lacks the built-in, strict transactional guarantees of an RDBMS, making it less ideal for this specific compliance scenario.
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What does ACID compliance mean in an RDBMS?
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Why are RDBMS better suited for complex analytical queries compared to NoSQL or other approaches?