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The Structured Query Language API (SQL) API and the MongoDB API represent two different approaches to managing and querying databases. Here are the main differences between these two APIs:

Data model:

• SQL API:
  • Structured Tables: SQL databases (e.g. MySQL, PostgreSQL) use a table-based model with rows and columns.
  • Schema-based: Requires a fixed schema, i.e. the structure of the data (tables, columns, data types) must be defined before inserting data.
• MongoDB API:
  • Document-based: MongoDB uses a document-based model, where data is stored as BSON documents (embedded JSON-like documents).
  • Schemaless: Supports flexible schemas, which means that the structure of documents within a collection can vary.

Query language:

• SQL API:
• SQL Language: Uses standardized SQL to define, manipulate, and query data.
• MongoDB API:
  • MongoDB Query Language: Uses its own non-standard query language based on JSON that performs CRUD (Create, Read, Update, Delete) operations.

Relationships:

• SQL API:
  • Relational: Supports relational database models with normalized data and relationships between tables (primary key, foreign key).
• MongoDB API:
  • Non-relational: Does not support traditional relational database models. Relationships can be established through embedded documents or referencing, but in practice denormalization is often used.

Transactions:

• SQL API:
  • ACID Compliance: Provides strong support for transactions and ensures ACID (Atomicity, Consistency, Isolation, Durability) properties.
• MongoDB API:
• Transactions: Supports ACID transactions at the document level and, since version 4.0, across multiple documents, but is more complex and less mature to use than in traditional SQL.

Scalability:

• SQL API:
  • Vertical scaling: Traditionally, SQL databases are scaled vertically, which means adding more resources to a single server.
• MongoDB API:
  • Horizontal scaling: MongoDB is designed for horizontal scaling (sharding), where data is distributed across multiple servers.

Maturity and Community:

• SQL API:
  • Long-term maturity: SQL databases and their APIs have been established and widely used for decades, with a large community of users and developers.
• MongoDB API:
  • Relatively new: MongoDB is a comparatively newer database model (released in 2009), but has a rapidly growing community and distribution.

Use cases:

• SQL API:
• Structured Data: Suitable for applications with clearly defined and structured data (e.g. financial systems, ERP).
• MongoDB API:
  • Unstructured Data: Preferred for applications with unstructured or semi-structured data that require flexibility in the data structure (e.g. content management systems, big data analytics).

These differences make SQL and MongoDB APIs each more suitable for different types of applications and requirements.