Topic
semantic-layer
26 posts tagged “semantic-layer”.
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Semantic Model Interchange Matrix, November 2026: Which Platforms Ingest LookML, MetricFlow, Semantic Views, Metric Views and Ossie
Which platforms ingest LookML, MetricFlow, Snowflake and Databricks semantic models and Ossie, with the losses each converter documents.
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Release Management for Data Agents: Shipping Prompts, Tools, Models, and Metrics Without Breaking Answers
Ship prompts, tools, models, and metric changes without breaking answers: agent manifests, shadow mode, canaries, and rollback.
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Semantic Caching for Data Agents Without Serving Stale or Leaked Numbers
Cache data agent results safely with canonical query keys, Iceberg snapshot freshness checks, and permission-aware cache keys.
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Small Language Models for Data Agents: Where They Win and Where They Fail
Where small language models win inside a data agent and where they fail, plus a routing architecture that escalates only when needed.
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Turning an Analytics Question Into a Verified Agentic Graph
A complete AGS 1.0 graph for governed metric questions, with verification gates, deterministic check scripts, and reconciliation against a semantic layer.
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Twelve Queries That Pass Every Schema Check and Still Return the Wrong Number
Twelve query patterns that pass schema validation and still return wrong numbers, with detectors, required metadata, and tested code for each.
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How Apache Ossie Is Deciding What Agents and BI Tools Can Ask a Semantic Layer
How Apache Ossie's layered query design gives AI agents both a constrained dimensional interface and a grain-safe SQL interface for semantic layers.
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Agentic Data Architecture
A six-layer reference architecture for agents on company data: planners, tool boundaries, identity, the semantic layer, and what breaks when a layer is missing.
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Context Engineering for Data Agents
Why text-to-SQL accuracy collapses on enterprise schemas, the five kinds of context an agent needs, where each one hides, and how to make the semantics legible.
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Apache Ossie and Apache Polaris: Putting Semantic Models in the Open Catalog
Apache Ossie and Polaris put metric definitions in the open catalog. What the spec covers, what Polaris stores, and what is still unfinished.
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Metric Contracts in Code: Testing, Versioning, and Serving Business Logic to Multi-Agent Systems
Metric contracts in code let teams test, version, and serve business logic to multi-agent systems without each agent inventing its own SQL.
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Semantic Layer Federation: One Logical Model Over Data on Three Clouds
One logical model over Iceberg and databases on three clouds. Pushdown, egress, Reflections, and where semantic federation still breaks.
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The Five Layers of an Agentic Lakehouse
The five layers of an agentic lakehouse: Storage, Catalog, Semantic, Gateway, and Agent Surface, and how one question travels through all of them.
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Metric Contracts in 2026: Standardizing Business Logic Across Multi-Agent Frameworks
Metric contracts in 2026: versioned, testable definitions of business logic that let multi-agent frameworks compute revenue identically, with OSI interchange.
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Semantic Layer Federation: One Meaning for Data That Lives Everywhere
Build a federated semantic layer across multi-cloud data so one set of governed metric definitions serves BI tools, dashboards, and AI agents identically.
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Why Agentic AI Needs a Governed Semantic Layer Behind the Model Context Protocol
Why agentic AI needs a governed semantic layer behind the Model Context Protocol: metric consistency, access control, Apache Ossie for portable.
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Metric Contracts as the Interface AI Agents Actually Need
Metric contracts as the interface AI agents need: calculation, inclusion rules, grain, temporal semantics, ownership, semantic versioning, and testing.
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Why AI Agents Fail on Raw Data, and What to Give Them Instead
Agents fail on raw lake data because business rules live in people's heads. Data products with semantic contracts fix this at the source.
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The Five Layers Between Your Lakehouse and a Trustworthy Agent
Agent reliability is a property of the stack the model sits on. Five layers with distinct owners and failure modes turn the agent is unreliable.
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Governing What Agents Cost You
Agents break the four assumptions analytics platforms were built on. A practical guide to identity, budgets, semantic layers, caching, and instrumentation.
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Semantic View Autopilot for AI Governance
An in-depth exploration of semantic view autopilot for ai governance
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The State of Agentic AI Standards in 2026: MCP, A2A, WebMCP, OSI, and the Protocol Stack Taking Shape
The agentic AI protocol stack is solidifying in 2026, MCP for tools, A2A for agents, WebMCP for the web, OSI for semantics, payments, identity.
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Composable Analytics Beats Metric Catalogs
Metric catalogs define what terms mean. Composable analytics defines how terms combine, transform, and relate.
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Implementing MCP in the Lakehouse
How to build a Model Context Protocol (MCP) server that exposes lakehouse tables and semantic views as AI-accessible tools, with Python implementation.
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SaaS Buyers Now Inspect Your Semantic Layer
Enterprise SaaS procurement in 2026 evaluates how platforms expose data to AI agents. Semantic layers have become a decision criterion alongside.
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Semantic View Autopilot in Snowflake Semantic Studio
Snowflake Semantic View Autopilot automates semantic view creation from query history and BI assets.