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Full-Stack Integration Case Study
Business Analytics Platform
Central dashboard aggregating POS and ad network data for financial trend analytics.
22% ad spendbudget Reclaimed
+15% returnretention
10M+data Points
Project Overview
A business intelligence suite that queries database transaction records to compile operational reports, cashflows, and forecasting charts.
The Problem
Analyzing product profits required manually extracting data from three software tools, causing financial metrics updates to lag by weeks.
The Solution
Designed a analytics engine that ingests data from ad nodes and sales systems, compiling margins and trends.
System Architecture
Celery cron workers download daily ad updates, run Python aggregation modules, and save calculations to PostgreSQL tables for instant React rendering.
[React Charts UI] <== (JSON Aggregates) ==> [Django Analytics API]
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[Ad Node Aggregators] ==> [Celery Engine] ===+===> [PostgreSQL Data Warehouse]Key Engineering Features
- ◆Sales performance visualization metrics with calendar filtering.
- ◆Customer lifetime value cohort calculators.
- ◆Predictive revenue trends using simple linear modeling.
- ◆Automated CSV/PDF business performance report compiler.
Technologies Utilized
ReactPythonDjangoPostgreSQLChartJS
Repository StateCommercial (Proprietary)
Deployment LevelProduction / Live