Different approaches are being explored for building an operational data lake with an easy data access API, as well as a federated data access API, and GraphQL opens up opportunities for enabling these architectures by laying the foundation for a data mesh.

I'll introduce the concept of a data mesh and why people increasingly are gravitating towards that as a solution to their data (both online and analytical) and application modernization problems. I'll talk about the key requirements around 1) domain-oriented decentralized data ownership and architecture, 2) data as a product, 3) self-serve data infrastructure as a platform, and 4) federated computational governance (ref). And then I'll talk about how GraphQL opens up an opportunity for laying the foundation for a data mesh and best-practices in building, operating and maintaining an enterprise grade data mesh.

GraphQL integration (and API/data fetching in general) becomes quite repetitive and complex as our app scales. New features need to be built that are sort of similar to features that already existed, but what bits they can reuse is not clear (eg: pagination). New members join the team and we’d like them to work on their UI components without worrying about the data fetching logic of the rest of the component tree. Relay takes an opinionated stance to solve some of these problems that are worth understanding and learning from. In this talk, I'm going to motivate the core features in Relay from the ground-up. I'll do hands-on demos to explain the common challenges GraphQL clients run into, how one would fix them without Relay and then fix them with Relay. I'll also touch upon how Relay works and its design briefly and how Relay’s design goal is not just being a high-performance GraphQL client, but also increasing developer productivity and happiness.

As GraphQL enters its 6th year, we've come a long way as a community and an ecosystem. But there's still a lot of work to be done for GraphQL to go totally mainstream and retain its momentum. In this talk, I'll highlight the top cross-cutting technical and tooling challenges that practitioners face in getting GraphQL adopted and hope to spark new ideas and discussions around what we need to spec out, what we need to build and what need to improve!
I'm excited to share a list of opportunities and ideas that encompass a) the boring things that need to be done (eg: health-check
&
error handling!), b) the hard problems that need to be solved (eg: rate-limiting
&
security) and c) the exciting challenges (eg: GraphQL
&
wasm) facing us as a GraphQL community.
I hope that at the end of the talk we have a real sense of what the top challenges are and why, and that we're excited about going into 2021 discussing these challenges and building potential solutions.

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