Case study
E‑commerce sales analytics pipeline
End‑to‑end ETL, warehouse, and Tableau reporting
A production-style pipeline that ingests e‑commerce API data on a schedule, lands it in PostgreSQL, and powers Tableau dashboards for sales, carts, and engagement.
Read on MediumIndustry
E‑commerce / Data engineering
Focus
Data pipeline design • Warehouse modeling • Dashboards & KPIs +
Product gallery
Key screens, flows, and UI states from E‑commerce sales analytics pipeline. Designed to feel like a real, working product—not just a Dribbble shot.
A reproducible daily DAG, documented loaders with validation, and a Tableau layer tied to the warehouse so metrics update with each run—summarised in a Medium walkthrough with architecture and runbook notes.
The problem
Why this product needed to exist
Stakeholders needed more than a one-off spreadsheet: they wanted repeatable ingestion from APIs, clean relational tables, and dashboards that stay fresh as new data arrives.
The product we built
Our approach and the experience we designed
We modeled a Dockerized stack with Airflow orchestrating Python extract/load tasks into PostgreSQL (normalized products, users, carts), then connected Tableau for category sales, top purchasers, abandonment, and revenue trends. The approach mirrors how modern teams separate ingestion, storage, and visualization layers.
Data flow
Public JSON sources → Python loaders → Airflow DAG (ordered tasks) → PostgreSQL → Tableau workbooks for product, user, and cart insights.
What the dashboards answer
Sales by category, standout purchasers, cart abandonment patterns, revenue by geography from user addresses, and daily or weekly revenue trajectories.