📄️ Architecture
PDQ combines advanced automation with end-to-end active metadata management. It is the foundation for data operations that are efficient, transparent and fully automated.
📄️ Platform guide
How the three layers connect, the storage zones a delivery passes through, and where to find the deep dive on each layer.
📄️ INGEST layer
How PDQ pulls data out of source systems - connection types, export strategies, scheduling, column selection, transformations and destination paths.
📄️ DLS layer
The storage zones a delivery passes through, how they are configured, the DLS pipeline and the publish patterns that produce query-ready tables.
📄️ DWA layer
Modelling, source-to-target mapping, transformations and relationships - plus QPI, the quality engine whose checks run against the data DWA loads.
📄️ Data management capabilities
A single list of what PDQ does for data management, grouped by the job it does rather than by the component that does it. Use it to check whether a capability exists, then follow the link to where it is documented in detail.
📄️ What lands in the target environment
Most automation tools deliver rows. PDQ delivers a described, constrained and classified schema — and the rows.
📄️ Differences between target platforms
PDQ generates SQL for six target platforms from one model. The model does not change when the target does — the same objects, attributes, relationships, business keys and classifications produce the same warehouse shape everywhere.