SQL (relational databases) Vs NoSQL (non-relational databases)

 

Aspect	SQL (Relational Databases)	NoSQL (Non-Relational Databases)
Data Model	Follows a fixed schema with tables and rows.	Follows a flexible schema with documents, key-value pairs, or graph structures.
Structure	Tabular structure with rows and columns.	Document-based, key-value pairs, column-family, or graph structures.
Scalability	Vertically scalable (scaling up by adding more powerful hardware).	Horizontally scalable (scaling out by adding more machines to the cluster).
Schema Flexibility	Strict schema with predefined structure.	Flexible schema, allowing changes to data structure without downtime.
Transactions	Supports ACID transactions (Atomicity, Consistency, Isolation, Durability).	May not support ACID transactions, offering eventual consistency instead.
Data Consistency	Strong data consistency guarantees.	Eventual consistency or eventual availability may be prioritized.
Query Language	Structured Query Language (SQL).	Query languages specific to each NoSQL database (e.g., MongoDB Query Language).
Joins	Supports complex joins between tables.	Joins are often limited or not supported, encouraging denormalization.
Atomicity of Operations	Ensures atomicity of operations within transactions.	Atomicity may vary based on the database and operation.
Data Integrity Constraints	Supports primary keys, foreign keys, and other constraints.	May not support foreign keys or complex constraints, emphasizing schema flexibility.
Horizontal Partitioning	Generally less efficient due to rigid schema.	Efficiently supports sharding and distributed databases.
Data Replication	Replication methods are well-established and standardized.	Replication strategies may vary between databases and configurations.
Examples	MySQL, PostgreSQL, Oracle Database.	MongoDB, Cassandra, Redis.
Use Cases	Best suited for structured data and complex queries.	Suitable for semi-structured or unstructured data, real-time analytics.
Schema Evolution	Schema changes often require downtime or complex migrations.	Schema evolution is easier, allowing for agile development and iteration.
Development Time	Longer development time due to schema design and normalization.	Faster development time, especially for rapidly evolving requirements.
Cost	Can be more expensive due to licensing and hardware costs.	May be more cost-effective, especially for distributed architectures.
Community Support	Large and mature community with extensive resources.	Growing community with evolving best practices.
Consistency Models	Generally follows strong consistency models (e.g., strict serializability).	May offer different consistency models (e.g., eventual consistency, causal consistency).
Backup and Recovery	Standard backup and recovery procedures are well-established.	Backup and recovery procedures may vary depending on the database architecture.

Here are some popular SQL (relational) and NoSQL (non-relational) databases:

SQL (Relational Databases):

1. MySQL
2. PostgreSQL
3. Oracle Database
4. Microsoft SQL Server
5. SQLite
6. IBM Db2
7. MariaDB
8. Amazon RDS (Relational Database Service)

NoSQL (Non-Relational Databases):

1. MongoDB (Document-based)
2. Cassandra (Wide-column)
3. Redis (Key-value)
4. Couchbase (Document-based)
5. Amazon DynamoDB (Document-based and Key-value)
6. Apache HBase (Wide-column)
7. Neo4j (Graph-based)
8. Amazon DocumentDB (Document-based)