The phrase describes an architecture as much as a set of tools: extract data with managed connectors, load it raw into a cloud warehouse, transform it there with SQL, and serve it through a semantic layer to business intelligence tools. This lesson lays out the layers and why the shift from transform-before-load to transform-after-load mattered.
The layers
Sources, managed ingestion, cloud warehouse, transformation framework, orchestration, semantic layer and business intelligence, with quality, catalogue and observability across them.
Why load first
Cheap warehouse storage and elastic compute made it practical to land raw data and transform in place. Raw data is preserved, transformations are versioned SQL, and analysts can contribute.
Who it serves
Teams that want analytics quickly with modest engineering headcount, whose data is mostly structured and whose latency needs are minutes to hours.
Action Step
Draw the layered architecture for a stack you will build through this bootcamp, naming the source systems you will use and the consumers who will use the output.
This course is vendor-independent: it is not affiliated with, endorsed by or accredited by any tool vendor or certification body, names products only for identification, and issues no credential. Verify current documentation before applying anything in production.