MetricFlow

"MetricFlow: Building a Metrics Engine for Consistent BI and Embedded Analytics"
When KPIs disagree across dashboards, teams, and embedded analytics surfaces, the problem is rarely “bad SQL”—it’s the absence of a durable semantic contract. This book is for experienced analytics engineers, data platform teams, and BI/embedded analytics builders who need metrics that stay correct as data models evolve, organizations scale, and consumption patterns multiply. You’ll learn how a metrics engine prevents metric drift, fanout errors, and duplicated business logic—without pushing complexity onto every dashboard author.
You’ll develop a rigorous mental model of MetricFlow’s architecture: how semantic metadata is resolved into a joinable graph, planned into correct aggregation strategies, and compiled into auditable warehouse SQL. The book goes deep on physical modeling prerequisites (grains, keys, conformed dimensions, SCDs), semantic authoring (entities, dimensions, measures vs. metrics), and time correctness (metric_time, grains, time spines, filter semantics). Advanced chapters tackle ratio safety, semi-additive and cumulative logic, funnels, and period offsets—along with how group-bys and filters shape query plans and result grains across warehouses.
In addition to design, this is an operational guide: metrics-as-code governance, continuous correctness validation, performance and cost optimization, BI/embedded integration patterns, security enforcement, and upgrade/compatibility management—so MetricFlow becomes a dependable production platform, not just a modeling layer.