<< Click to Display Table of Contents >>

Navigation:  »No topics above this level«


Return to chapter overviewNext page

Welcome to the RAPID-ML™ Quick Start Guide!

Welcome!  This guide will get you up to speed quickly with API design, using RAPID-ML and RepreZen™ API Studio.

RAPID-ML is a domain-driven API description language that promotes shared data models across APIs, ensuring high interoperability and integration cost-efficiency. RepreZen API Studio is a powerful API-first design environment that helps you design, document, visualize, collaborate, test, and implement your APIs.

RepreZen API Studio supports industry-standard Swagger (now the OpenAPI Specification) and RAPID-ML.  This Quick Start Guide focuses exclusively on RAPID-ML, and its unique approach to data model sharing and adaptation across APIs.

Together, RAPID-ML and RepreZen API Studio let you focus on the key design decisions and organization of your RESTful APIs without having to worry about how you will implement them. That said, when it's time, RepreZen API Studio's code generator will help you to create schemas, interchange formats, scaffolding code and other implementation artifacts.  It also generates API documentation and diagram visualizations that you can provide to API consumers and use yourself to keep track of what you're doing as you go along.

Outside-in, data-first approach leads to well designed APIs

RAPID-ML enables you to create RESTful APIs from the outside in, evolving them in layers, in an iterative manner, as you progress through design to implementation. This API-first approach helps prevent you from getting lost in implementation details before being clear about what an API will look like and do, and is crucial to creating APIs that meet the real needs of clients rather than simply reflecting the way in which underlying systems are organized and made. APIs thus produced are also easier to understand and use.

Central to this approach is the practice of designing APIs around shared canonical data models. Teams simply self-organize and share data models which are adapted, using realization modeling techniques, to the specific needs of each service interface.  When service APIs are designed to 'speak' the same language, clients can integrate faster, and you can stop runaway spending on complex data transformation, data governance, MDM and other remedial integration solutions.

API-First Development Process

This Quick Start guide describes how to design, refine and (to some extent) test RESTful APIs and their associated data models. It takes you on a fast, hands-on tour through the key features of RAPID-ML and RepreZen API Studio.

The high-level process is:

1.Design the API: Define the data types and resources that will constitute the API (adapting the data types used according to the specific requirements of our service). This is an iterative process as we discover how best to expose the data via an appropriate range of resources.
2.Try out the API using the Mock Service. (If it seems to be ready goto step 3, else return to step 1.)
3.Generate the scaffolding code and and other artifacts for API implementation.
4.Implement the API - create a full, working service using the generated materials from the previous step.

This guide will mostly cover the first three steps. We will start by defining the data model and then put this to work in some API. In doing this we will see how RepreZen API Studio enables us to adapt the rules of an abstract data model to suit the specific purposes of an application without either breaking the model or having to create something entirely new, and unrelated.

Copyright © 2016 ModelSolv, Inc.  All rights reserved. RepreZen and RAPID-ML are trademarks of ModelSolv, Inc. Swagger is a registered trademark of SmartBear Software, Inc. RepreZen API Studio is not associated with nor endorsed by SmartBear Software, Inc.