What term refers to data stores so vast that conventional database management systems cannot handle them?

What term refers to data stores so vast that conventional database management systems cannot handle them?

• A. Big Data
• B. Data Mining
• C. Data Warehousing
• D. Metadata

Answer: (A)

Big Data refers to data stores so vast, fast-changing, and diverse that conventional database management systems struggle to capture, store, search, and analyze them. It’s often described by the three Vs: volume (huge amounts of data), velocity (rapid data generation and processing), and variety (many different data types and sources). Traditional DBMS are designed for structured data and mostly scale vertically, which becomes impractical as data grows to petabytes or more and arrives from many streams. Big Data solutions use distributed, scalable architectures to handle this scale and complexity. Data mining is the practice of extracting patterns and insights from data, not the data storage itself. Data warehousing is a centralized repository optimized for reporting and analysis of structured data, but it doesn’t by itself imply the extreme scale or the need for distributed processing that “Big Data” captures. Metadata is information about data, such as its origin, format, and usage, and isn’t the term for oversized data stores.

Big Data refers to data stores so vast, fast-changing, and diverse that conventional database management systems struggle to capture, store, search, and analyze them. It’s often described by the three Vs: volume (huge amounts of data), velocity (rapid data generation and processing), and variety (many different data types and sources). Traditional DBMS are designed for structured data and mostly scale vertically, which becomes impractical as data grows to petabytes or more and arrives from many streams. Big Data solutions use distributed, scalable architectures to handle this scale and complexity.

Data mining is the practice of extracting patterns and insights from data, not the data storage itself. Data warehousing is a centralized repository optimized for reporting and analysis of structured data, but it doesn’t by itself imply the extreme scale or the need for distributed processing that “Big Data” captures. Metadata is information about data, such as its origin, format, and usage, and isn’t the term for oversized data stores.