Build with Prifina: GraphQL Data Model Editor

At Prifina we are grateful to be able to work in collaboration with leading software developers in different companies and in open collaboration. We’re also fortunate to have the chance to work with amazing new technologies like GraphQL, that unlocks much more flexibility and permeability than their predecessors like the Restful API setup. 

Every once in a while we notice there that something new doesn’t have full support available for something we want to do, where we have to solve what looks like a common problem for ourselves. As we’re building our own solution to what seems like a common problem, our solution is to release it to the public domain. 

This post is about a GraphQL Data Model Editor we've built together with Startup Commons and Grow VC Group and are now releasing, so we can develop it together in collaboration with others that see value in using it.

What we’ve built: The GraphQL Data Model Editor

​Here you can see the clickable prototype. Feel free to add comments, questions or suggestions.

A complete application to create, document (for the business user and the technical user), edit, manage, publish, populate, test, verify and deploy data models in SLS to AWS, and use this model to spin up AWS infrastructure. 

Building and documenting the data model

Dual view, from “document” to SLS

Encouraging the designer and developer to work together via dual views in text and SLS

Testing the data model

Populating and testing the data model (with test data)

Visualizing and documenting the data model

Publishing and connecting the data model to AWS

In our internal setup, this published SLS data model can be deployed directly into AWS and used to spin up the needed backend setup. It can also be used to make amendments into the existing infrastructure, which naturally requires clear versioning, deployment protocols and access controls. 

Further development

We see the need for further development in at least the following areas:

1. Versioning (think Google docs)
2. User and access management 
3. Setup to spin up fake data en masse to populate and test data model (using e.. Faker https://github.com/marak/Faker.js/)

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