BI Sense: ETL and data warehousing case study

Overview

BI Sense was a data warehousing and ETL project for a business that relied on daily batch reports to make operational and strategic decisions. The existing reporting setup was slow, expensive, and increasingly unreliable as data volumes grew.

LovelyPixel built a custom ETL pipeline and structured data warehouse that replaced the existing system with a faster, cheaper, and more reliable solution — one that could scale alongside the business.

Challenge

The client's existing data pipeline had several critical issues:

• Reports took the better part of a day to refresh — too slow for operational decisions
• Third-party API services were expensive and lacked the flexibility needed
• Data quality was inconsistent — errors often went undetected until they surfaced in dashboards
• The system couldn't scale to handle growing data volumes without significant cost increases

Solution

We designed and built a complete ETL pipeline and data warehousing solution from scratch, tailored to the client's specific data sources, reporting needs, and budget constraints.

What we built

• Custom ETL pipeline with incremental sync and scheduled refresh
• Structured data warehouse optimised for Power BI dashboards
• Built-in error recovery and data validation at every stage
• Monitoring, automated alerts, and clear documentation
• Architecture designed to scale without proportional cost increases

Results

Architecture at a glance

Source systems (operational databases and third-party platforms) land in a staging schema via scheduled Python ETL jobs. A modelled warehouse layer handles cleansing, deduplication and history, and Power BI semantic models sit on top. Incremental refresh, row-level security and clear lineage are baked in from day one.

Data refresh cadence

Scheduled incremental loads every 10 minutes during business hours, with full reconciliation overnight. Dashboards are always within one refresh cycle of live operational data.

Data volume handled

Multi-million row fact tables across orders, transactions and activity logs — growing month on month without the pipeline slowing down or costs spiking.

Why custom ETL beat API-heavy reporting

The previous setup paid per-API-call for data the business needed every hour. Custom ETL into a proper warehouse shifted the cost curve from "scales with traffic" to "scales with storage" — dramatically cheaper at this volume, and faster.

Tech stack

Next steps

The client continues to expand the data sources feeding into the warehouse. The architecture was specifically designed for this — new data sources can be added without rebuilding the existing pipeline. Ongoing monitoring ensures reliability as data volumes continue to grow.