Clear, owned outputs.
- Warehouse architecture and model design
- Implemented models and transformations
- Data dictionary and definitions
- Migration reconciliation reports
- Performance and cost recommendations
A data warehouse organizes data from many systems into a consistent structure designed for reporting and analysis. We design and build cloud data warehouses with clear models, tested transformations and access controls, so people can answer business questions quickly and trust the results.
The problems Data Warehousing solves.
Cloud warehouse design, dimensional modelling, migration and governed access.
The legacy warehouse is slow, expensive and hard to change.
Business definitions like 'customer' or 'revenue' differ between reports.
Analysts spend most of their time preparing data.
Warehouse costs are rising without visibility.
What Data Warehousing covers.
Warehouse design
Choosing and configuring the right cloud warehouse.
Dimensional modelling
Facts, dimensions and marts organized around business processes.
Transformation
Tested, documented transformations with dbt or native tools.
Legacy migration
Moving from on-premises warehouses with reconciliation.
Performance and cost
Partitioning, clustering and workload management.
Access and governance
Role-based access, masking and auditing.
What makes a warehouse easy to query.
A warehouse earns its keep when business users can answer questions without asking an engineer. That depends far more on the model than on the platform.
- Dimensional modelling
- Fact tables hold events — orders, visits, invoices — and dimension tables hold the context: customer, product, date. Most BI tools work best on this star shape.
- Grain
- Every fact table states what one row means: one order line, one daily balance. Mixing grains is the most common cause of double counting.
- Slowly changing dimensions
- Type 2 history keeps what a customer's region or segment was at the time of each sale, so past reports don't silently change.
- Incremental loads
- Only new or changed rows are processed each run, keeping costs and load windows predictable as volumes grow.
- Workload isolation
- Separate compute for loading, BI and data science so a heavy query doesn't slow the morning dashboards.
- Cost controls
- Auto-suspend, clustering or partitioning and query monitoring on Snowflake, BigQuery, Redshift or Synapse.
Why it's worth it.
Consistent numbers
Shared definitions end debates about whose figure is right.
Fast queries
Well-modelled data answers questions in seconds.
Lower maintenance
Modern platforms reduce infrastructure work.
When Data Warehousing is the right move.
- Replacing an ageing on-premises warehouse.
- A first central warehouse for a growing business.
- A warehouse for finance and operations reporting.
Technologies & platforms
- Snowflake
- Google BigQuery
- Amazon Redshift
- Azure Synapse
- Microsoft Fabric
- dbt
- SQL Server
How Data Warehousing work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Data Warehousing questions.
What's the difference between a data warehouse and a data lake?
A warehouse stores structured, modelled data for reporting. A lake stores raw data of any type at low cost. Lakehouses combine features of both.
Do you use dbt?
Often, yes. dbt makes transformations version-controlled, tested and documented. We also work with native tools where they suit better.
How do you migrate without disrupting reporting?
By running old and new in parallel, reconciling results and moving reports in planned phases.
Talk to us about data warehousing.
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.