Build with Prifina: Releasing the 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 the GraphQL Data Model Editor we’ve built in open collaboration with our community, including Startup Commons and the Grow VC Group, so we can develop it together in the public domain and open collaboration with others that see value in using it.
What We’ve Built: The GraphQL Data Model Editor
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.
Here you can see the clickable prototype. Feel free to add comments, questions, or suggestions.
Here is the link to the Data Model Builder GitHub.
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:
- Automated versioning (think Google docs)
- User and access management
- Setup to spin up fake data en masse to populate and test data model (using e.. Faker https://github.com/marak/Faker.js/)
- Create a hosted version
Connect With Us and Stay in Touch
You can follow Prifina on Twitter, LinkedIn, or join our Facebook group. You can also explore our Github: “Liberty. Equality. Data.”
