Highlightdbt Labs

dbt measures are column aggregations, now migrating to simple metrics

dbt documentation for measures as aggregations on model columns, with deprecation toward type: simple metrics.

Measures aggregate columns and can stand alone or feed complex metrics. Parameters include name, agg (sum, max, min, average, median, count_distinct, percentile, sum_boolean), expr, non_additive_dimension, and related fields. The new spec deprecates measures in favor of simple metrics under metrics.

Based on: Measures | dbt Developer Hub · dbt Labs

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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

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dbt semantic models are MetricFlow graph nodes tied to DAG models

dbt docs explain semantic models as YAML-configured nodes and entities that MetricFlow uses for the Semantic Layer.

In dbt v1.12+, semantic models are the foundation for MetricFlow’s Semantic Layer: nodes linked by entities in a semantic graph, annotated on dbt models for metric use. Each DAG model maps to one semantic_model YAML block (or Apache Ossie docs); components include name, time dimension, and entities.

Based on: Semantic models | dbt Developer Hub · dbt Labs

HighlightGoogle Cloud

LookML puts join structure and query content in one reusable semantic model

Google Cloud docs introduce LookML as Looker’s modeling language for dimensions, aggregates, joins, and SQL generation.

LookML (Looker Modeling Language) is a dependency-style language for building semantic data models over SQL databases. Models and views define joins and calculations once; Looker generates dialect-independent SQL so analysts avoid repeating expressions and business users query without writing SQL.

Based on: Introduction to LookML | Looker | Google Cloud Documentation · Google Cloud

HighlightSnowflake

Cortex Analyst: managed text-to-SQL over Snowflake structure via REST

Snowflake docs on Cortex Analyst—an LLM feature for natural-language questions on structured data, exposed as a REST API.

Cortex Analyst is a fully managed Snowflake Cortex feature that answers business questions on structured Snowflake data in natural language without users writing SQL. It ships as a REST API for embedding in apps and aims to produce accurate text-to-SQL without teams building custom RAG stacks or managing GPUs. Snowflake recommends transitioning to Cortex Agents, which includes Analyst capabilities.

Based on: Cortex Analyst | Snowflake Documentation · Snowflake

HighlightCube Dev

Cube’s semantic layer as the shared surface for BI, embed, and AI agents

Cube docs: agentic analytics on an open-source semantic layer for internal BI and embedded analytics.

Cube is positioned as an agentic analytics platform on a semantic layer for internal BI and embedded analytics. Cube Core centralizes metrics, joins, access rules, and caching upstream of BI tools, apps, and agents. Agents query via Semantic SQL through the semantic layer runtime with validation and access policies, not by writing free-form warehouse SQL.

Based on: Introduction - Cube Documentation · Cube Dev

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Centralize metrics in dbt so every BI tool reads the same definitions

Overview of the dbt Semantic Layer: MetricFlow-backed metrics defined once in the modeling layer for consistent downstream use.

The dbt Semantic Layer, powered by MetricFlow, lets teams define metrics on existing models, handle joins, and expose those definitions to downstream tools. Moving metrics out of BI into the modeling layer keeps business units on the same definitions; a change in dbt refreshes everywhere the metric is invoked. Access permissions control who can use it; Starter or Enterprise-tier accounts are required.

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

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MetricFlow: YAML metrics and a semantic graph that generate the SQL

dbt Labs guide introducing MetricFlow as the engine behind the Semantic Layer for defining and querying metrics.

MetricFlow powers dbt’s Semantic Layer with opinionated abstractions for defining and managing metric logic. It builds SQL from YAML (and, from dbt v1.12, Ossie documents), linking semantic models and metrics in a semantic graph. It works with listed warehouses and dbt 1.6+, under Apache 2.0.

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