HighlightMicrosoft Learn

Purview and Fabric as one path from source to Power BI lineage

Microsoft Learn article on using Microsoft Purview with Microsoft Fabric for estate-wide governance, classification, and end-to-end lineage.

Microsoft Purview and Microsoft Fabric are presented as parts of the Microsoft intelligent data platform for storing, analyzing, and governing data together. Combined, they are said to cover the estate and lineage from data source to Power BI report without stitching multiple vendors. Purview is described as governance, risk, and compliance coverage across Microsoft 365, on-premises, multicloud, and SaaS.

Based on: Use Microsoft Purview to Govern Microsoft Fabric - Microsoft Fabric · Microsoft Learn

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

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

Highlightdbt Labs

MetricFlow centralizes metric specs and SQL construction in dbt

dbt intro to defining metrics with MetricFlow as the Semantic Layer component that builds SQL and enforces consistency.

MetricFlow in dbt centrally defines metrics and builds SQL from semantic models and metric specs. It aims to cut duplicative coding, support governance of company metrics, and keep consumer results consistent; defined metrics can be queried in development and, on higher plans, in downstream tools.

Based on: Build your metrics | dbt Developer Hub · dbt Labs