This text categorizes databases into nine types. Relational databases (RDBMS) like Oracle and MySQL use tables and SQL, emphasizing ACID properties for business systems. NoSQL databases, including key-value stores like Redis, offer flexibility and scalability. Document databases like MongoDB store data as JSON/BSON documents. Wide-column stores like Cassandra handle massive data writes. Graph databases like Neo4j manage complex relationships. Search engine databases like Elasticsearch focus on full-text search. NewSQL databases like TiDB combine consistency with horizontal scaling. Time-series databases like InfluxDB optimize time-stamped data. Multi-model databases like Azure Cosmos DB support multiple data models within a single engine.
0x01 Relational Database (RDBMS):
The most classic and widely-used type. Data is organized in table structures, SQL is supported, and it emphasizes transactional ACID properties (Atomicity, Consistency, Isolation, Durability).
Leading products: Oracle, MySQL, PostgreSQL, SQL Server, SQLite
Use cases: The vast majority of business systems, such as finance, ERP, and e-commerce order management.
0x02 Non-Relational Database (NoSQL):
Created to overcome the scalability and flexibility limitations of relational databases, NoSQL encompasses a wide range of sub-types.
Key-Value Store: Data exists as the simplest Key-Value pairs, enabling blazing-fast reads and writes.
Notable examples: Redis, Amazon DynamoDB, etcd.
Use cases: Caching, session management, leaderboards.
0x03 Document Database:
Data is stored in a document format similar to JSON/BSON, offering highly flexible structures.
Notable examples: MongoDB, Couchbase
Use cases: Content management, user personalization profiles, mobile applications.
0x04 Wide-Column Store:
Stores data by column rather than by row, making it ideal for writing and analyzing massive datasets.
0x01 Relational Database (RDBMS):
The most classic and widely-used type. Data is organized in table structures, SQL is supported, and it emphasizes transactional ACID properties (Atomicity, Consistency, Isolation, Durability).
0x02 Non-Relational Database (NoSQL):
Created to overcome the scalability and flexibility limitations of relational databases, NoSQL encompasses a wide range of sub-types.
0x03 Document Database:
Data is stored in a document format similar to JSON/BSON, offering highly flexible structures.
0x04 Wide-Column Store:
Stores data by column rather than by row, making it ideal for writing and analyzing massive datasets.
0x05 Graph Database:
Data is stored as nodes and edges (relationships), excelling at handling complex interconnections.
0x06 Search Engine Database:
Specializes in full-text search and log analytics.
0x07 NewSQL Database:
Combines the strong consistency of relational databases with the horizontal scalability of NoSQL.
0x08 Time-Series Database:
Purpose-built and optimized for writing and querying data in chronological order, with highly efficient timestamp storage.
0x09 Multi-Model Database:
A single database engine that supports multiple data models (relational, document, graph, etc.), reducing technology stack complexity.