Modern API in Data Science
API in Data Science:
API stands for Application Programming Interface. An API is a set of subroutine definitions, protocols, and tools for building application software. In general terms, it is a set of clearly defined methods of communication between various software components. A good API makes it easier to develop a computer program by providing all the building blocks, which are then put together by the programmer. It may be for a web-based system, operating system, database system, computer hardware, or software library.
Modern API is a product and it has its own software development lifecycle (SDLC). It consists of design, testing, build, management, and versioning. More recently, however, the modern API has taken on some characteristics that make them extraordinarily valuable and useful.
Characteristics of Modern API:
1. Modern APIs adhere to standards typically HTTP and REST that is developer-friendly, easily accessible, and understood broadly.
2. They are treated more like products than code. It designed for consumption by specific audiences. They are documented and they are versioned in a way that users can have certain expectations of their maintenance and lifecycle.
3. They are much more standardized, they have a much stronger discipline for security and governance, as well as monitored and managed for performance and scale.
4. As any other piece of productized software, the modern API has its own software development lifecycle (SDLC) of designing, testing, building, managing, and versioning. An API consists of:
i. Bring in the API module.
ii. Obtain database connection
iii. Issue SQL statements and then store procedures.
iv. Close the connection.