Frame the problem
Data warehouses / Lakehouse planning
QUANTEX
Data platforms, big data & business intelligence
Create a shared definition of what happened.
When departments disagree about a metric, the problem may be upstream of the dashboard. We map sources, transformations and business definitions before designing reports. Data pipelines include quality checks and lineage so a number can be traced back to its origin rather than accepted because it looks precise.
Capabilities and deliverables
- Data warehouses
- Lakehouse planning
- ETL and ELT
- Streaming pipelines
- Data quality
- Metric definitions
- BI models
- Dashboards
- Lineage
- Access governance
A delivery model built around the task
Build the proof
ETL and ELT / Streaming pipelines
Validate the release
A source-to-report model, tested pipelines and documented metrics with ownership and refresh expectations.
The constellation
Map the decisions, people and information that shape the experience.
Data warehousesThe connection
Give every hand-off a clear contract, owner and recovery path.
Lakehouse planningThe launch
Test the complete journey before it becomes everyday operation.
ETL and ELTWhat you take away
A source-to-report model, tested pipelines and documented metrics with ownership and refresh expectations.
A question worth asking
Do we need a big-data platform?
The architecture should follow volume, latency and access needs. A smaller warehouse or well-designed database can be sufficient and less expensive to operate.