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# Introducing JSON Structure

JSON Schema lacks the strict typing needed for robust code generation. JSON Structure transforms the familiar format into a strictly typed data definition language for modern polyglot applications.

- **Speakers:** [Clemens Vasters](https://www.wearedevelopers.com/@clemens-vasters)
- **Event:** World Congress 2026 Europe
- **Published:** July 10, 2026
- **Duration:** 31:01
- **URL:** https://www.wearedevelopers.com/videos/100219-introducing-json-structure

## Summary

Modern polyglot applications and real-time data pipelines require precise, cross-language data structures. While JSON Schema is widely used, it functions primarily as a document validation language, lacking the strict typing and determinism needed for robust code generation. JSON Structure, a new IETF Draft, refactors the familiar JSON format into a strictly typed data definition language purpose-built for telemetry databases and message brokering. By shifting from passive validation to definitive data modeling, it enables engineering teams to define exact data shapes without being locked into binary-specific ecosystems like Protobuf or Avro. \n\nTo overcome JSON's inherent limitations—such as IEEE double precision capping safe integers at 53 bits—JSON Structure introduces extended primitive types like 64-bit integers, precise decimals, and validatable RFC 3339 dates. It also natively implements advanced compound types like sets, maps, and tuples. The tuple concept is a highly effective design for bandwidth-constrained environments, allowing structured object definitions to be serialized as lean arrays on the wire while retaining full metadata in the schema. Furthermore, JSON Structure tames the notoriously unpredictable reference keyword by confining schema mappings to local definition sections and requiring explicit file imports, ensuring predictable resolution and eliminating external lookup hazards. \n\nAt a structural level, the specification introduces formalized namespaces to prevent naming collisions in massive data models and enables structured type reuse via abstract bases, bypassing rigidly forced object-oriented polymorphism. Companion specifications add robust support for internationalization aliasing, explicitly defined scientific units, and currencies, embedding vital business logic context for downstream deployment or AI coding agents. Organizations can operationalize these definitions using tooling like Structurize and Avrotize, which bi-directionally convert schemas across formats and emit native, self-serializing data classes for languages like Python, Rust, and Go, establishing a unified metadata narrative across the entire engineering stack.

**Keywords:** json structure, json schema limitations, data definition language, polyglot data modeling, real-time data pipelines, strict schema typing, json precise integers, tuple array serialization, schema reference resolution, structured type reuse, cross-language data classes, schema format conversion, avrotize tool, metadata registries, ietf specifications

## Chapters

1. **Establishing metadata standards for real-time data pipelines** (00:03) — Formalizing data schema definitions is necessary to handle unstructured streams in modern telemetry platforms.
1. **Breaking the lock-in of specific data serialization formats** (03:20) — Defining data structures strictly for polyglot environments resolves the inadequacies of document-bound validation languages.
1. **Refactoring JSON Schema into a formal data definition language** (06:09) — Structuring exact names, compound types, and precise primitives enables rigorous code generation for applications.
1. **Overcoming lossy JSON serialization with extended primitive types** (09:28) — Extended primitive types preserve high-precision integers and decimals without relying on custom parsers.
1. **Optimizing data wire space with sets, maps, and tuples** (12:21) — Resolving schema structures through tuple-based array encodings securely minimizes string-heavy metadata payloads.
1. **Neutering the reference keyword to prevent schema resolution hazards** (15:02) — Forcing self-contained type references inside local definition modules guarantees isolated, predictable data validation.
1. **Implementing explicit file imports for external schema module resolution** (19:02) — Sourcing external definitions through direct import commands provides simple integrations that behave as localized resources.
1. **Structuring flexible type reuse without enforced object-oriented polymorphism** (20:24) — Flattening inherited types via abstract bases and field copying accommodates varied programming language implementations.
1. **Adding semantic richness with aliases, currencies, and technical units** (22:49) — Declaring concrete technical and business units natively within the schema drastically improves interpretation by AI code tools.
1. **Standardizing open data definitions via accessible IETF internet drafts** (25:06) — Adopting a plain-text structure specification promotes open-source consumption without licensing burdens.
1. **Generating polyglot application code and native database schemas** (26:18) — Automating the construction of diverse application classes and native database representations standardizes disjointed development environments.

## Related Moments

- [Adopting JSON Structure as a strict data definition language](https://www.wearedevelopers.com/videos/1616-bringing-clarity-to-event-streams-enabling-analytics-and-ai-through-rich-metadata) (from "Bringing Clarity to Event Streams: Enabling Analytics and AI Through Rich Metadata")
- [Submitting JSON structure as an internet engineering draft](https://www.wearedevelopers.com/videos/1534-introducing-json-structure-a-better-schema) (from "Introducing JSON Structure - A Better Schema")
- [Creating a standardized JSON schema for framework-agnostic discoverability](https://www.wearedevelopers.com/videos/1012-micro-frontends-discovery) (from "Micro-Frontends Discovery")
- [Leveraging the JSON API standard for data structure](https://www.wearedevelopers.com/videos/33-api-design-getting-started) (from "API Design - Getting Started")
- [Adopting JSON relational duality functions for maximum schema flexibility](https://www.wearedevelopers.com/videos/632-crypto-secure-data-management-with-in-database-blockchain) (from "Crypto-secure Data Management with In-Database Blockchain")
- [Structuring model payloads precisely with custom JSON schemas](https://www.wearedevelopers.com/videos/1269-exploring-google-gemini-and-generative-ai) (from "Exploring Google Gemini and Generative AI")

## Related Articles

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- [Dev Digest 136 - No JS(on) of mine](https://www.wearedevelopers.com/magazine/482-dev-digest-136-no-js-on-of-mine)
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