Co-Development of GraphQL Data Model Editor

In our tireless pursue to 'push the envelope' towards enabling ecosystem level digital connectivity, we have the opportunity to work with amazing new technologies like GraphQL, to unlock new flexibility than their predecessors like Restful API's.

Sometimes in the process we notice that something new doesn’t have full support available for something how we want to use such technology, where we end up solving what looks like a common problem for ourselves. While building our own solution in such common problem, our basic approach is to release it to the public domain.

Startup Commons, together with Prifina - a Silicon Valley based consumer and developer focused personal data platform and Grow VC Group is co-developing a GraphQL Data Model Editor, a complete application where business and technical users collide to create, document, edit, manage, publish, populate, test, verify and deploy ecosystem open standard data models.

Standard data model development is needed to enable digital connectivity that allows ecosystem builders, developers, operators and other key ecosystem actors to share ecosystem data with peer entities supporting entrepreneurs and startups along the journey following MyData principles.

Making data accessible to between ecosystem applications, allows collaborating actors to build a better ecosystem level user experience, where view into progress of building/supporting innovations is more accurate, and where ecosystem builders, developers and operators can compare, analyze, measure and orchestrate ecosystem development more effectively, to make more sound decisions.

What We Have Built So Far

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:

1. Automated 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/)
4. Create a hosted version

Connect With Us and Stay in Touch

We are scaling up our operations and building our digital team in different positions. What we are building is incredibly ambitious and we want to get inspired by anyone passionate about data, innovation and entrepreneurship, and as such, we have an open-door policy for those that want to get to know us, to connect with others and to share knowledge. We, as serial entrepreneurs, know and accept this risk but we don't know any other way to reach our vision for digital transformation applied to scaling innovation, entrepreneurship and ecosystem development. A tool to empower ecosystem builders with cutting edge capabilities to unlock ecosystem data and put data to work in favor of all ecosystem actors, bringing dramatic improvements in ecosystem orchestration. This is ecosystemOS.