Modelling and code generation
The data model is the source. Load logic, historisation and structures are generated from it — consistently, auditably, and without hand-written ETL that drifts apart over time.
Platform
PDQ — the core of a unified data platform
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.
By integrating PDQ into your infrastructure you ensure that modelling, quality, versioning, integration, code generation and orchestration hold together as one unit — not as a collection of loosely coupled tools.
PDQ by Simplitics
Architecture
From source to delivered value
Ingest, Data Lake Storage and Data Warehouse Automation in one coherent flow — with catalogue, monitoring, orchestration, automation, observability and traceability across the whole chain.
Data catalogueMonitoringOrchestration PDQ by Simplitics AutomationObservabilityTraceability
Data sources
- Files
- APIs
- Streams
- Databases
Ingest
Ingest Agent
DLS
Landing Raw Archive
Trusted Profile
DWA
Published Integrated Business
Delivery
- Reports & BI
- APIs
- AI & ML
- SQL access
Any database
- Databricks
- Redshift
- SQL Server
- Fabric
- Synapse
- Snowflake
Any storage
- Azure Blob
- Amazon S3
- Google Cloud Storage
- MinIO
Any cloud
- Azure
- AWS
- Google Cloud
PDQ's reference architecture. Every step is driven by active metadata, which makes the flow traceable from data source to delivered report.
Capabilities
What the platform actually does
Quality and profiling
Profiling and quality rules run as part of the flow, not as an afterthought. Anomalies are caught where they occur and stop what needs stopping.
Orchestration and operations
Dependencies, run order, re-runs and alerting are handled centrally. Production-ready from day one instead of months of scaffolding.
Active metadata
The metadata does not just describe the system — it runs it. Lineage, versions and compliance are by-products of execution, not separate projects.
Layers
DLS and DWA — what happens where
- Ingest
Into the platform
The ingest agent pulls data from files, APIs, streams and databases and lands it in Landing without altering it.
- DLS
Data Lake Storage
Landing → Raw Archive → Trusted, with Profile alongside. Raw data is preserved unchanged; quality assurance happens on the way to Trusted.
- DWA
Data Warehouse Automation
Published → Integrated → Business. From quality-assured source data to an integrated, historised model and finally a business-facing delivery layer.
- Delivery
Out to the business
Reports, APIs, dashboards, AI and ML workflows and direct SQL access — all from the same quality-assured foundation.
Want to see PDQ on real data?
We would rather show than tell. A walkthrough takes an hour and starts from your stack, not our demo.
Book a walkthrough