High Write Performance: Optimized for writing large volumes of data.
Sparse Data Handling: Efficiently stores rows where many columns might be empty.
Time-Series Data: Well-suited for storing data that changes over time (e.g., sensor readings).
Weaknesses:
Complex Data Modeling: Requires a deep job seekers data understanding of data access patterns to design efficient schemas.
Limited Joins and Aggregations: Not designed for complex analytical queries that span many rows or columns.
Eventual Consistency: Data across nodes may not be immediately consistent.
Ideal Use Cases:
Time-series data (sensor data, stock market data).
Event logging and analytics.
Fraud detection.
Large-scale messaging platforms.
IoT applications requiring high ingest rates.
Prominent Examples: Apache Cassandra, Apache HBase, Google Bigtable.
4. Graph Databases: Navigating Relationships
Core Principle: Data is stored as nodes (entities) and edges (relationships between entities). Both nodes and edges can have properties (attributes). Designed for modeling and querying highly interconnected data.
Strengths:
Efficient Relationship Traversal: Excels at querying complex relationships and multi-hop connections, which are cumbersome for relational databases (requiring many costly joins).
Flexible Schema: Easily adapts to evolving relationships between entities.
Intuitive for Connected Data: Data models directly reflect real-world relationships.
Weaknesses:
Scaling Challenges: Horizontal scaling can be more complex than other NoSQL types, especially for queries that traverse the entire graph.
Less Suited for Simple Data: Overkill for data with few relationships.
New Query Languages: Often use specialized query languages (e.g., Cypher, Gremlin), requiring a learning curve.