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Case Study — Automation / Data

BI Sense: ETL & data warehousing

A custom ETL pipeline and warehouse solution that transformed how a business accesses its data — from slow, expensive daily reports to near real-time dashboards.

Category
Automation & Data
Services
ETL, Data Warehousing, Reporting
Key result
~30x speed improvement
Scale
Multi-million rows

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

~30x
Dashboard speed improvement
~10 min
Refresh cadence (down from daily)
Millions
Rows processed reliably at scale
Reduced
Cost compared to API services

Tech stack

Python SQL Server Power BI ETL Pipeline Data Warehousing API Integration Scheduled Jobs

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.

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