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Answer: Data warehouses, designed for efficient storage and processing of large volumes of historical data, supporting complex SQL queries.
Data warehouses are specifically designed to efficiently store and process large volumes of historical data, making them ideal for complex analytical queries and reporting. They excel in environments with structured data and read-heavy workloads. Key features include the ability to handle complex SQL queries and integrate data from various sources. - **NoSQL databases** offer flexibility for unstructured data but are less efficient for complex analytical queries. - **Relational databases** are suitable for transactional workloads but may not scale as effectively for large-scale data analysis. - **Object storage** is primarily used for storing and retrieving unstructured data, not for analytical queries.
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In the context of designing a large-scale machine learning system for a financial services company, you are tasked with selecting a data storage solution that optimally handles read-heavy workloads with complex analytical queries. The solution must support structured data, scale efficiently, and integrate seamlessly with various data sources for comprehensive reporting. Considering these requirements, which data storage solution should you choose? (Choose one correct option)
A
NoSQL databases, due to their flexibility in handling unstructured data and scalability.
B
Object storage, for its cost-effectiveness in storing large volumes of unstructured data.
C
Data warehouses, designed for efficient storage and processing of large volumes of historical data, supporting complex SQL queries.
D
Relational databases, suitable for transactional workloads with ACID compliance.
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