Here’s all the tools & information you need to make an informed decision!
Data can be structured (SQL), semi-structured (JSON, XML, etc.), and unstructured (Blob).
In the case of structured they can be relational or columnar, while in the case of semi-structured there is a wide range of possibilities, from key-value to graph.
Here are the main differences between SQL and NoSQL
1) Data Model
SQL: Data is organized into tables with predefined schemas, and relationships between tables are established using primary and foreign keys
NoSQL: Use various data models, such as key-value, document, columnar, or graph. These models are often designed to handle unstructured or semi-structured data
2) Scalability:
SQL: Handle increased workloads by adding more powerful hardware to a single server
NoSQL: It allows for seamless expansion by adding more servers to the database cluster
3) Schema:
SQL: Enforce a rigid schema where tables must adhere to predefined structures and column types
NoSQL: A flexible schema, allowing for dynamic and schema-less data
4) ACID Compliance:
SQL: Typically follow ACID (Atomicity, Consistency, Isolation, Durability) properties, which ensure data integrity and transaction reliability
NoSQL: Often prioritize scalability and performance over strict ACID compliance
5) Use Cases:
SQL: Well-suited for structured data, complex relationships, and when maintaining data integrity is crucial
NoSQL: Suitable for handling large volumes of unstructured or semi-structured data, such as social media feeds, IoT data, real-time analytics, and content management systems where flexibility and scalability are essential
