Non-relational (NoSQL) databases are engineered to address the challenges of big data. Their core design principles, such as horizontal scalability (scaling out) and flexible data models (document, key-value, graph), allow them to efficiently store and process massive volumes of unstructured or semi-structured data. Unlike relational databases that can become bottlenecks, NoSQL systems are built for high availability and performance at scale, making them the preferred choice for applications dealing with large, diverse, and rapidly growing datasets.

B. They are often called "NoSQL" (Not only SQL) and typically use proprietary or non-SQL query languages, not the standard SQL of relational systems.

C. Relational databases, with strict ACID properties, are ideal for transactional accuracy. NoSQL databases often prioritize availability over consistency (BASE model).

D. This describes the tabular structure of relational databases. Non-relational databases use flexible models like document, key-value, column-family, or graph stores.

1. Amazon Web Services (AWS) Documentation. "What is NoSQL?". In the section "Relational vs. NoSQL," it states, "NoSQL databases are a great fit for many modern applications such as mobile, web, and gaming that require flexible, scalable, high-performance, and highly functional databases to provide great user experiences." This directly aligns with the demands of big data applications.

2. Stonebraker, M. (2010). "SQL Databases v. NoSQL Databases". Communications of the ACM, 53(4), p. 10-11. This foundational article explains that NoSQL systems emerged to meet the scalability and flexibility requirements of large-scale web applications, which are primary generators of big data. (https://doi.org/10.1145/1721654.1721693)

3. Carnegie Mellon University, Database Group Courseware. "15-445/645 Database Systems (Fall 2022)", Lecture #21: NoSQL Databases. The lecture notes explicitly contrast NoSQL with SQL databases, highlighting NoSQL's relaxed consistency (BASE vs. ACID) and non-relational data models as trade-offs for achieving higher scalability for large datasets. (See slides on "NoSQL Use Cases" and "Data Models").