Data Engineering & Warehousing
Data warehouse design and development
A data warehouse should make reporting faster, simpler and consistent — not become another system nobody understands. We design staging, warehouse and presentation layers with dimensional models built around the questions your business actually asks.
When teams bring us in
Signs you need Data Warehousing support
- Every report calculates the same measure differently
- Reports query source systems directly and slow them down
- The warehouse has grown table by table without a model behind it
- History is lost because dimensions overwrite changes
What's included
Our Data Warehousing work
- 01
Layered architecture
Clear staging, warehouse and presentation layers, each with a defined job and ownership.
- 02
Dimensional modelling
Star schemas, conformed dimensions and slowly changing dimensions where history matters.
- 03
Performance design
Indexing, partitioning and load strategies sized for your data volumes and reporting patterns.
- 04
Semantic consistency
Shared definitions for key measures, so Power BI and other tools report the same numbers.
FAQ
Data Warehousing — common questions
- Should we modernise our existing warehouse or start again?
- It depends on how sound the underlying model is. We assess it first; in many cases the right answer is to restructure and extend it rather than rebuild from scratch.
- Do you build warehouses on SQL Server or on Azure?
- Both — on SQL Server on-premises, Azure SQL and hybrid set-ups. Platform choice follows your data volumes, skills, cost constraints and existing investment.
- How do you avoid breaking existing reports during a redesign?
- We map lineage from warehouse tables to views, datasets and reports before changing anything, and phase changes so dependent reports are updated in step.
Related Dataventra solutions
Guides
- Guide · 7 min readHow to find everything that depends on a SQL Server table before you change itFind views, procedures, SSIS packages and Power BI datasets that depend on a SQL Server table — with sys.dm_sql_referencing_entities, recursive dependency queries and what they miss.
- Guide · 8 min readMonitoring SQL Agent job and SSIS package failures: a practical guideQuery msdb and SSISDB to find failed SQL Agent jobs and SSIS executions, slow runs, missing rows and stale data — and turn the queries into monitoring.
- Guide · 6 min readAutomatically creating Jira tickets for failed SQL Server Agent jobsDetect SQL Agent and SSIS failures, capture diagnostics and create Jira issues through the REST API — with deduplication, routing and secure credentials.
More in Data Engineering & Warehousing
- Data Engineering & WarehousingData EngineeringDesign and build the pipelines that move data from source systems into a platform your business can trust.
- Data Engineering & WarehousingSQL Server & SSISDeep, hands-on work in SQL Server, SSMS and SSIS — the platforms many enterprises still run on.
- Data Engineering & WarehousingAzure Data PlatformMove and extend on-premises SQL workloads onto Azure without losing control of cost or reliability.
Talk to us about Data Warehousing.
Tell us what you're running and what's getting in the way. We'll suggest a sensible first step.