Supports sharding and replication for scaling
Posted: Tue May 20, 2025 6:44 am
Weaknesses:
Limited Transaction Support: ACID transactions are typically restricted to single documents. Multi-document transactions are often eventually consistent or more complex to achieve.
Data Duplication: If not carefully modeled, related data might be duplicated across documents, making updates more complex.
No Joins: Relationships between documents are generally handled by the application, requiring multiple database queries instead of a single join.
Ideal Use Cases:
Content Management Systems (CMS): Storing articles, blog posts, user comments.
Catalogs and Product Information: E-commerce product listings with varying attributes.
User Profiles: Storing user data with dynamic and expanding attributes.
Mobile Applications: Often used as a backend due to flexible schema and offline capabilities.
IoT Data: When the structure of sensor data might evolve.
Prominent Examples: MongoDB, Couchbase, Apache CouchDB, Azure Cosmos DB (document API).
3. Column-Family Databases (Wide-Column Stores): Massive Scale for Big Data & High Throughput
Concept: Data is stored in tables, but rows are identified by a car owner data unique row key, and columns are grouped into "column families." Within a row, you can have a flexible number of columns, and columns can be sparse (not all rows need to have all columns). Optimized for very high write throughput and massive datasets, often used for time-series or event data.
Data Model: Rows with dynamic, grouped columns.
Strengths:
Massive Scalability: Excellent horizontal scalability for petabytes of data across thousands of nodes.
Extremely High Write Performance: Optimized for ingesting vast amounts of data very quickly.
Efficient Handling of Sparse Data: Doesn't waste space on empty columns.
Time-Series Focus: Naturally suited for data that changes over time (e.g., sensor readings, log data).
Limited Transaction Support: ACID transactions are typically restricted to single documents. Multi-document transactions are often eventually consistent or more complex to achieve.
Data Duplication: If not carefully modeled, related data might be duplicated across documents, making updates more complex.
No Joins: Relationships between documents are generally handled by the application, requiring multiple database queries instead of a single join.
Ideal Use Cases:
Content Management Systems (CMS): Storing articles, blog posts, user comments.
Catalogs and Product Information: E-commerce product listings with varying attributes.
User Profiles: Storing user data with dynamic and expanding attributes.
Mobile Applications: Often used as a backend due to flexible schema and offline capabilities.
IoT Data: When the structure of sensor data might evolve.
Prominent Examples: MongoDB, Couchbase, Apache CouchDB, Azure Cosmos DB (document API).
3. Column-Family Databases (Wide-Column Stores): Massive Scale for Big Data & High Throughput
Concept: Data is stored in tables, but rows are identified by a car owner data unique row key, and columns are grouped into "column families." Within a row, you can have a flexible number of columns, and columns can be sparse (not all rows need to have all columns). Optimized for very high write throughput and massive datasets, often used for time-series or event data.
Data Model: Rows with dynamic, grouped columns.
Strengths:
Massive Scalability: Excellent horizontal scalability for petabytes of data across thousands of nodes.
Extremely High Write Performance: Optimized for ingesting vast amounts of data very quickly.
Efficient Handling of Sparse Data: Doesn't waste space on empty columns.
Time-Series Focus: Naturally suited for data that changes over time (e.g., sensor readings, log data).