Semantic modelling tools create an abstraction layer between the raw data warehouse schema and the BI consumers, defining business concepts (entities, metrics, dimensions) in a vendor-neutral or product-specific language. LookML (Looker's semantic model language) and Cube.js (an open-source semantic layer framework) are the two most widely used dedicated semantic modelling approaches. Both serve the same purpose as dbt's Semantic Layer (MetricFlow) but operate as standalone services between the warehouse and BI tools, making them compatible with any BI tool rather than being tied to dbt's ecosystem.
LookML defines business logic in a hierarchical YAML-like language: Models (groups of Explores), Explores (join relationships between views), Views (column definitions mapping to database tables), Dimensions (non-aggregated columns), Measures (aggregated columns like SUM, COUNT, AVERAGE), and Derived Tables (virtual tables computed by a SQL subquery within LookML). Every Looker dashboard chart is generated by selecting combinations of Dimensions and Measures from an Explore — Looker constructs the SQL, executes it, and renders the result, ensuring the same metric definitions are used regardless of who built the chart.
Analogy🏏Cricket
🏏 Think of it like cricket: OLTP is the IPL's live ticketing counter — it handles thousands of simultaneous seat reservations, each requiring a precise single-seat record update with immediate confirmation. Speed per transaction and data consistency under concurrent updates are everything. OLAP is the IPL's season statistics department — it runs complex analytical queries across every ball bowled in every match of every season to produce the published rankings, economy rates, and historical comparisons. No one books a seat through the statistics department, and no broadcaster calls the ticketing counter for Bumrah's career economy rate. The two workloads demand completely different systems. Just as the ticketing counter is built for speed and correctness on one seat at a time and would buckle if asked to tally a decade of attendance mid-sale, an OLTP row-store excels at single-record writes but chokes on full-table aggregation; and just as the statistics department pores over millions of past deliveries but would be hopeless at booking a live seat under contention, the OLAP columnar engine sweeps billions of rows yet is the wrong tool for a fast single-row update. The physical design of each — row-oriented for the counter, columnar for the stats desk — is what makes it superb at its own job and unfit for the other's.
🏏 Showing the Cricket analogy — a Cricket version isn’t available for this concept yet.