Handling large volumes of user-generated content
Posted: Tue May 20, 2025 7:17 am
Document Databases: Store data as JSON-like documents with flexible schemas. Ideal for managing hierarchical or semi-structured data. (e.g., MongoDB, Couchbase)
Key-Value Stores: Store data as key-value pairs, offering fast read and write operations. Suitable for caching, session management, and simple data storage. (e.g., Redis, Memcached, Amazon DynamoDB)
Graph Databases: Model data as nodes (entities) and edges (relationships), optimized mexico whatsapp mobile phone number list for querying and analyzing connections between data points. Useful for social networks, recommendation engines, and knowledge graphs. (e.g., Neo4j, Amazon Neptune)
Column-Family Databases: Store data in columns rather than rows, providing high performance for read-heavy workloads and sparse data. Often used for large-scale data analytics. (e.g., Apache Cassandra, HBase)
Key-Value Stores: Store data as key-value pairs, offering fast read and write operations. Suitable for caching, session management, and simple data storage. (e.g., Redis, Memcached, Amazon DynamoDB)
Graph Databases: Model data as nodes (entities) and edges (relationships), optimized mexico whatsapp mobile phone number list for querying and analyzing connections between data points. Useful for social networks, recommendation engines, and knowledge graphs. (e.g., Neo4j, Amazon Neptune)
Column-Family Databases: Store data in columns rather than rows, providing high performance for read-heavy workloads and sparse data. Often used for large-scale data analytics. (e.g., Apache Cassandra, HBase)