HighlightJSON Schema (IETF-oriented draft)

JSON Schema defines a media type for describing JSON document structure

IETF-oriented Internet-Draft for JSON Schema as application/schema+json, including instance media type notes.

This Internet-Draft defines JSON Schema as the media type application/schema+json, a JSON-based format for describing JSON structure, extraction, and interaction. It also discusses application/schema-instance+json for richer integration than plain application/json. The draft is informational, authored by Wright, Andrews, Hutton, and Dennis, published 16 June 2022.

Based on: JSON Schema: A Media Type for Describing JSON Documents · JSON Schema (IETF-oriented draft)

HighlightMicrosoft

OneLake catalog centralizes find, govern, and secure for Fabric items

Overview of Microsoft Fabric’s OneLake catalog for discovering items, reviewing governance posture, and managing security roles.

OneLake catalog is a centralized Fabric place to find, explore, and use items and to govern owned data. It is reachable from the Fabric navigation pane and embedded in Teams, Excel, and Copilot Studio; metadata can also be searched via the Catalog Search REST API. Explore, Govern, and Secure tabs cover browsing with filters, governance posture insights, and unified workspace and OneLake security role management.

Based on: OneLake catalog overview - Microsoft Fabric · Microsoft

HighlightSnowflake

Snowflake Horizon Catalog pairs discovery with semantic context for agents

Snowflake documentation introducing Horizon Catalog as an interoperable catalog with governance, lineage, and semantic views for AI.

Horizon Catalog is described as an agentic catalog for data inside and outside Snowflake, open across engines, data, and clouds. It targets discoverability, business context for AI, and trust via protection, quality, lineage, and AI governance. Features include Iceberg interoperability, Internal Marketplace, semantic views, and column-level lineage spanning Snowflake, external systems, BI, and OpenLineage.

Based on: Snowflake Horizon Catalog | Snowflake Documentation · Snowflake

Highlightmartinfowler.com

Data mesh scales ownership and change, not only data volume

Zhamak Dehghani's essay on four data mesh principles and the logical architecture each one implies.

Dehghani argues that past technology fixed volume scale but not change, source proliferation, use-case diversity, or response speed. Data mesh answers with four principles: domain-oriented decentralized ownership, data as a product, self-serve data infrastructure as a platform, and federated computational governance. Each principle implies a corresponding logical architecture and organizational structure.

Based on: Data Mesh Principles and Logical Architecture · martinfowler.com

Highlightdbt Labs

manifest.json is the full parsed map of a dbt project's resources

Artifact reference for manifest.json: contents, producers, version mapping, and uses in docs and state comparison.

Commands that parse a dbt project write manifest.json under target/, except deps, clean, debug, and init. The file holds a full representation of resources and properties even when only some nodes run. dbt uses it for the docs site and state comparison; community tools use it for project-health checks. Top-level keys include nodes, sources, metrics, exposures, groups, macros, docs, and parent/child maps.

Based on: Manifest JSON file | dbt Developer Hub · dbt Labs

Highlightdbt Labs

Exposures name the dashboards and apps that depend on your DAG

dbt docs on declaring manual or automatic exposures that link downstream uses to models, sources, and metrics.

Exposures describe downstream uses of a dbt project such as dashboards, applications, or data science pipelines. Defining them lets you run and test feeding resources and publish consumer-facing pages in generated docs. They can be declared in YAML or created automatically for supported integrations and stored in dbt metadata. Required fields include name, type, and owner; depends_on lists refs, sources, and metrics.

Based on: Add Exposures to your DAG | dbt Developer Hub · dbt Labs

Highlightdbt Labs

Cross-project refs treat public models as a stable API, not a package

dbt Labs documentation on packages versus project dependencies that resolve public models through a metadata service.

dbt long supported installing other projects as packages, which pulls in full source code for macros and models. It also supports project dependencies that resolve on-the-fly refs to public models via a metadata service, so downstream teams do not parse or run upstream models. Those models are treated as an API whose maintainer guarantees quality and stability, with Enterprise prerequisites including public access and a production manifest.

Based on: Project dependencies | dbt Developer Hub · dbt Labs

Highlightdbt Labs

dbt Mesh is a multi-project pattern for governed cross-team data products

2023 docs introducing dbt Mesh: cross-project refs, Catalog, groups, access, versions, and contracts for independent yet aligned teams.

The page says a single dbt project struggles at scale to coordinate stakeholders, and Mesh addresses multi-project dependencies, governance, and workflows. Mesh is a pattern, not one product: Enterprise cross-project ref, Catalog lineage, governance, groups, access, model versions, and contracts. It recommends treating models as stable APIs when coordinating across teams and outlines when multi-project architecture becomes appropriate.

Based on: About dbt Mesh | dbt Developer Hub · dbt Labs

Highlightdbt Labs

dbt model access is group-scoped visibility, not user permissions

Docs distinguishing model access from user access, and explaining groups that mark models private or public for ref boundaries.

The page separates “model access” from dbt user permissions: developers in a project see private models; others may depend only on public ones. Groups give models a shared owner and make interface boundaries explicit. Private access restricts which models other groups may ref; public models are the intended dependency surface, including for future multi-project collaboration.

Based on: Model access | dbt Developer Hub · dbt Labs

Highlightdbt Labs

dbt Semantic Layer APIs put MetricFlow metrics behind governed query interfaces

dbt docs on Semantic Layer APIs: define metrics in code with MetricFlow and query governed assets from downstream tools, including via GraphQL.

The page argues that growth in the modern data stack fragments business logic across teams and tools. The Semantic Layer lets you define metrics in code with MetricFlow and dynamically generate and query datasets from dbt-governed metrics and models. It lists use cases such as BI, data quality, governance, discovery, and ML, and notes a GraphQL API for metrics and dimensions.

Based on: Semantic Layer APIs | dbt Developer Hub · dbt Labs

HighlightOpenMetadata

OpenMetadata docs cover discovery, lineage, contracts, and MCP for agents

OpenMetadata documentation hub for metadata discovery, lineage, governance, data contracts, and AI/MCP integrations.

OpenMetadata’s docs present a platform to document, discover, and govern data assets, with quick start, production, and upgrade paths. Guides span discovery, lineage, observability, data contracts, and governance; highlights include Context Center, MCP server and AI SDK for agents, new connectors, and dimensional data-quality validation.

Based on: OpenMetadata Documentation - OpenMetadata Documentation · OpenMetadata

HighlightAnthropic

Anthropic open-sources MCP as a universal AI-to-data connection standard

Anthropic announces the Model Context Protocol for two-way links between AI tools and data sources via servers and clients.

On 25 Nov 2024 Anthropic open-sourced MCP to connect AI assistants to content repos, business tools, and development environments under one protocol instead of per-source integrations. Release includes the spec and SDKs, local server support in Claude Desktop, an open-source server repo, and pre-built servers for systems such as Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer.

Based on: Introducing the Model Context Protocol · Anthropic