Storing massive volumes of log data for analytics.
Posted: Tue May 20, 2025 6:45 am
Weaknesses:
Complex Data Modeling: Requires a deep understanding of read and write access patterns to design efficient schemas. Queries are optimized for row key lookups and sequential reads within column families.
Limited Querying: Not designed for complex analytical queries across many rows or columns, aggregations are often done at the application layer.
Eventual Consistency: Data consistency across distributed nodes is typically eventual.
Ideal Use Cases:
Time-Series Data: IoT sensor data, stock market data, application metrics.
Fraud Detection: Real-time analysis of transaction streams.
Large-Scale Messaging Systems: Storing message feeds.
User Activity Tracking: Storing clickstream data, Browse history.
Prominent Examples: Apache Cassandra, Apache HBase, Google Bigtable.
4. Graph Databases: Modeling and Traversing Relationships
Concept: Data is stored as nodes (entities, e.g., "Person," "Product") and usa student data edges (relationships between entities, e.g., "FRIENDS_WITH," "BOUGHT"). Both nodes and edges can have properties (attributes). Designed for efficiently storing and querying highly interconnected data.
Data Model: Nodes and Edges.
Strengths:
Exceptional at Relationship Traversal: Excels at querying complex, multi-hop relationships that are extremely cumbersome and slow for relational databases (requiring many costly JOINs).
Flexible Schema: Adapts easily to evolving relationships between entities without schema migrations.
Intuitive Modeling: Data models directly reflect real-world relationships.
Complex Data Modeling: Requires a deep understanding of read and write access patterns to design efficient schemas. Queries are optimized for row key lookups and sequential reads within column families.
Limited Querying: Not designed for complex analytical queries across many rows or columns, aggregations are often done at the application layer.
Eventual Consistency: Data consistency across distributed nodes is typically eventual.
Ideal Use Cases:
Time-Series Data: IoT sensor data, stock market data, application metrics.
Fraud Detection: Real-time analysis of transaction streams.
Large-Scale Messaging Systems: Storing message feeds.
User Activity Tracking: Storing clickstream data, Browse history.
Prominent Examples: Apache Cassandra, Apache HBase, Google Bigtable.
4. Graph Databases: Modeling and Traversing Relationships
Concept: Data is stored as nodes (entities, e.g., "Person," "Product") and usa student data edges (relationships between entities, e.g., "FRIENDS_WITH," "BOUGHT"). Both nodes and edges can have properties (attributes). Designed for efficiently storing and querying highly interconnected data.
Data Model: Nodes and Edges.
Strengths:
Exceptional at Relationship Traversal: Excels at querying complex, multi-hop relationships that are extremely cumbersome and slow for relational databases (requiring many costly JOINs).
Flexible Schema: Adapts easily to evolving relationships between entities without schema migrations.
Intuitive Modeling: Data models directly reflect real-world relationships.