Model Online / On-Site
Duration
Modules 6 modules

Course Objective & Scope

When an LLM writes raw SQL directly against base tables for questions like "net revenue", "active customers" or "churn rate", it guesses differently each time on how to exclude refunds, tax or test accounts—producing numbers that disagree with official BI dashboards. This course solves metric drift by placing a governed Semantic Layer and canonical Metric Definitions between the LLM and the data warehouse.

Participants model dimensions, measures, canonical joins and business glossaries using Cube, dbt Semantic Layer / MetricFlow or structured YAML definitions, connecting LLMs to semantic APIs and curated data marts.

Who Should Attend?

  • Data and analytics engineers managing enterprise data warehouses, dbt projects and BI layers
  • AI engineers who need single-source-of-truth consistency in Text-to-SQL and analytics assistants
  • Data architects responsible for data governance, metric standardisation and platform design
  • Prerequisite: strong SQL, dimensional data modelling (star schema / data marts) and basic LLM familiarity

Key Takeaways

  • Eliminate metric drift between BI dashboards and AI assistants through a shared semantic layer
  • Model measures, dimensions, time grains, hierarchies and default filters as version-controlled YAML
  • Route LLM queries to structured semantic layer APIs (Cube, MetricFlow, MCP tools) instead of unconstrained SQL
  • Map business glossary terms, acronyms and department synonyms to canonical metrics
  • Design a hybrid architecture combining governed metric queries with curated data-mart SQL for ad-hoc exploration

Curriculum

Why Raw Text-to-SQL Fails on Enterprise KPIs
  • Metric drift, fan-out/chasm-trap JOIN bugs and undocumented business filter rules
  • The role of the semantic layer: moving business logic out of prompts into a governed model
Semantic Data Modelling: Entities, Measures, Dimensions and Joins
  • Defining additive, ratio, cumulative and period-over-period (YoY, MoM) metrics
  • Canonical join graphs, mandatory business filters and time-dimension granularities
Semantic Layer Platforms: dbt Semantic Layer, Cube and Warehouse Views
  • Comparing dbt MetricFlow, Cube and native warehouse semantic views
  • Exposing the semantic layer to LLMs via REST, SQL APIs and Model Context Protocol (MCP) tools
Text-to-Semantic-Query: Structured Metric Invocation with LLMs
  • Generating `{metrics, dimensions, filters, timeDimensions, order}` specs instead of unconstrained SQL
  • Deterministic compilation of semantic requests into guaranteed-valid warehouse SQL
Business Glossaries and Hybrid Analytical Routing
  • Mapping domain synonyms and internal acronyms to canonical metric definitions
  • Routing governed KPI questions to the semantic layer and ad-hoc deep dives to curated data marts
Hands-On: Building a Semantic Layer and Governed Analytics Assistant
  • Authoring YAML metric definitions for core internal KPIs and wiring them to an LLM agent
  • Benchmarking consistency against unconstrained raw Text-to-SQL

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