A data analyst is working on a new social media analytics platform. The platform must store vast amounts of user-generated content, including posts and user profiles that have varying attributes. The system must also efficiently map and query complex user relationships, such as friendships and content interactions, to identify key influencers. Given these requirements for a flexible schema and relationship-focused queries, which type of non-relational database would be the MOST suitable choice?
The correct answer is a graph database. Graph databases are purpose-built to store and navigate relationships. Data is modeled as nodes (e.g., users, posts) and edges (e.g., 'friend', 'liked'), making them extremely efficient for traversing complex and deep relationships, which is a core requirement for identifying influencers in a social network.
A document database is incorrect because while it handles flexible schemas and user profiles well, it is less efficient than a graph database for analyzing complex, interconnected relationships between different data entities. A key-value store is also incorrect. It is designed for simple, fast lookups based on a unique key but lacks the capability to perform the complex relationship queries needed for this scenario. A column-family database is incorrect as it is optimized for analytical queries on large datasets and high-speed writes, but not specifically for traversing the types of relationships found in a social network.
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CompTIA Data+ DA0-002 (V2)
Data Concepts and Environments
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