warehouse & BIBeginner

SQL & Analytics

Query the lakehouse from the SQL editor, model gold tables and views for BI, and understand what a SQL warehouse costs.

0/30|30 written|~30 h
To doIn progressDonePlannedSwipe the map, tap a concept
Stage 1 · Foundations
Delta Lake, the lakehouse table formatUnity Catalog, the governance layerThe SQL editorGenie CodeSQL warehouse sessionsQuery parameters and session variables
Stage 2 · Model for BI
Gold objects: tables, views, materialized views, streaming tablesMedallion architecture: bronze, silver, goldMetric viewsModelling data inside a dashboardDomains and PagesDashboard filters, parameters and variablesDashboard schedules and subscriptions
Stage 3 · Query and serve
Sizing a SQL warehouseReading the query profileAI/BI dashboardsGenie AgentsThe Genie knowledge storeGenie benchmarks, feedback and monitoringStandalone materialized views in Databricks SQLThe Genie OntologyStreaming tables from Databricks SQLSQL alertsUsing Genie outside the UI: API, embedding and agentsWhat an alert costs to runSQL warehouse types and channelsQuery performance insightsQuery caching layersTuning a Genie Agent for correct answersGenie One

The analyst’s half of the platform. You do not need to know Spark internals to be useful here, but you do need to know what a warehouse costs, why a query is slow, and which object to build for BI.

If you already run pipelines, jump straight to the Query and serve stage: the modelling concepts overlap with Data Engineering.

Resources for this path

Courses, books and repos that cover the whole map go here. None have been added yet; per-concept resources appear on each concept page.