Data warehousing
Analytics warehouses on Amazon Redshift, modelled around the decisions your team actually needs to make rather than everything you happen to collect.
Design principle
Questions
Modelled from decisions backward, not sources forward.
- Basis
- Fixed price per phase
- Target
- Amazon Redshift
- Includes
- Semantic layer
What we deliver
A warehouse people use.
The failure mode is a technically correct warehouse nobody queries. We start from the questions and work back to the sources.
Question inventory
The decisions the business needs to make, captured before any modelling.
Dimensional model
Facts and dimensions designed for the query patterns that follow.
Ingestion
Batch and incremental loads from source systems, monitored and alerting.
Semantic layer
Consistent metric definitions so two dashboards cannot disagree.
BI enablement
Self-serve access with the guardrails to keep query cost predictable.
How it runs
Model backward from decisions.
First phase delivers one subject area end to end — sources through dashboard — so value lands before the full model exists.
Frame the questions
Workshops with the teams who will use the numbers.
Model
Dimensional design for the first subject area, reviewed with analysts.
Build & load
Warehouse provisioned, pipelines running, data reconciled to source.
Enable
Semantic layer, dashboards, and training for self-serve use.
Technology
What we build it on.
- Redshift
- Glue
- S3
- Athena
- QuickSight
- Step Functions
Cost & AWS pricing
Consulting engagement priced per phase. Redshift run cost is modelled up front — including reserved-instance and serverless options — so the ongoing figure is known before build.
See funded AWS programsNext step
Build the warehouse people query.
Discovery comes first — a short, bounded review that ends in an architecture, a scope, and a price. You keep the output either way.