Data warehousing the business can report from

• Dimensional models: conformed dimensions and fact tables designed around the questions the business actually asks.
• Incremental loads using watermarks or change tracking, so the nightly run moves what changed rather than everything.
• Slowly changing dimensions, so a customer's old region stays attached to their old sales.
• Data quality checks inside the pipeline: row counts, totals and key integrity reconciled on every load.
• Power BI semantic models built on the warehouse, with each measure defined once.

A warehouse is only as good as what reaches it. The sources that matter most are often the ones with no API, so ingestion uses the same techniques as our extraction work: traced databases, parsed exports and internal endpoints, each load reconciled against the source.

Extraction techniques

Azure Synapse Analytics, Azure Databricks and Data Lake Storage, with Azure Data Factory for orchestration, or SQL Server on premises. The choice follows the volumes, the skills of whoever will run it day to day, and the budget.

• Reports that disagree with each other usually share a root cause in the model. We find it and fix it at source.
• Pipelines that fail quietly get logging, alerts and a reconciliation step.
• Slow models and refreshes get profiled before anything is rewritten.

In one recent case a margin report spread annual salary across booked days instead of hours worked, a bug nobody had spotted. We found it while reconciling data for a migration and documented the fix.

Based in Glossop, about 15 miles east of Manchester. Remote across the UK, and on site in Manchester and the North West when it helps.

• Pipelines, deployed and scheduled.
• A warehouse model with a data dictionary and lineage from source to report.
• Power BI semantic models and reports.
• Handover documentation and a walkthrough for your team.