Faster decisions on large data sets
Modernising frontend architecture and data flows in a distributed adtech platform spanning multiple business domains.
OpenX
35%+
lower Time to Interactive in reporting dashboards
The problem that required change
Reporting dashboards processed large advertising data sets, and their usefulness depended on how quickly people could reach a clear view of the metrics.
Several React applications were developed in parallel. They needed shared contracts, components and state patterns without duplicating solutions across domains.
From diagnosis to delivery
- 01Designing shared architecture for multiple React applications in an Nx monorepo
- 02Leading the migration of key flows from REST to GraphQL
- 03Refactoring data fetching and advertising-metric visualisation
- 04Working with Product, UX and backend teams on API contracts
- 05Introducing profiling, Lighthouse CI and integration tests into release validation
Decisions organised into a controlled process
- 01
Diagnose data flows
Profile dashboards and identify where fetching and transformation delayed interaction.
- 02
Shared architecture
Consolidate GraphQL clients, state and UI components within the Nx monorepo.
- 03
Refactor reporting
Reduce overfetching and simplify query logic for the most important views.
- 04
Guard against regressions
Add profiling, Lighthouse CI, Cypress and GitHub Actions to regular release validation.
35%+
lower Time to Interactive on large data sets
1
shared architecture across multiple React apps and business domains
CI/CD
earlier detection of performance and interface regressions
TypeScript · React 18 · Nx · GraphQL · Redux Toolkit · React Query · Cypress · GCP
Next step
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