analyticsdurgesh

analyticsdurgesh / StreamCommerce-Lakehouse-360

Public

Production-style real-time e-commerce lakehouse with Kafka, Airflow, Databricks, Medallion architecture, data quality, quarantine, Terraform, and Dash analytics.

airflow data-engineering data-quality databricks delta-lake
19
0
89% credibility
Found May 31, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

StreamCommerce Lakehouse 360 is a portfolio project that demonstrates how a complete e-commerce analytics platform works. It simulates realistic shopping events (orders, payments, inventory, shipments, customer behavior, and product changes), processes them through a three-stage data pipeline (called Bronze, Silver, and Gold layers), catches and quarantines bad data, and presents business insights through an interactive dashboard. The project includes everything needed to run locally for demonstration purposes, as well as patterns for deploying to cloud services.

How It Works

1
🔍 You discover an e-commerce analytics platform

Someone shares this project with you as an example of a complete data platform that handles everything from shopping events to business reports.

2
🚀 You launch the platform with one command

With a single command, you start up all the services: event generators, data processors, and the analytics dashboard all come to life automatically.

3
🛒 You watch fake shopping events flow through the system

Realistic e-commerce events appear - orders, payments, inventory changes, customer clicks - each one moving through the data pipeline automatically.

4
You see how data gets cleaned and trusted
✅
Clean data moves forward

Valid records continue through the pipeline, getting organized and prepared for analysis.

🚫
Bad data gets quarantined

Records with missing IDs, wrong prices, or invalid statuses are saved separately so they can be investigated later.

5
📊 You explore the business dashboard

A colorful dashboard shows revenue trends, top products, customer behavior, inventory health, and payment reliability - all updated as new data arrives.

🎉 You have a working analytics platform

You've seen end-to-end how a modern data platform works - from raw events to business insights - ready to demonstrate or learn from.

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Star Growth

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AI-Generated Review

What is StreamCommerce-Lakehouse-360?

This is a complete e-commerce analytics platform built in Python that simulates real-time shopping events (orders, payments, inventory, shipments, customer behavior) and processes them through a modern lakehouse architecture. Events flow through Kafka into object storage, get cleaned and validated through Bronze/Silver/Gold layers, and surface in a Plotly Dash dashboard for business users. The system includes data quality checks that quarantine bad records and alert when thresholds are breached.

Why is it gaining traction?

This stands out because it demonstrates the full modern data stack in one runnable project. Most tutorials show fragments; this shows end-to-end from Kafka producers to executive dashboards. The medallion architecture (Bronze/Silver/Gold) is industry standard but rarely shown with working code. The local Docker setup means developers can run the entire pipeline without cloud credentials, making it ideal for portfolio demonstration. The event simulator intentionally injects bad data to test quality controls, which proves the system handles real-world messiness.

Who should use this?

Data engineers building portfolio projects will find this valuable for demonstrating production thinking. Teams evaluating lakehouse patterns can use it as a reference architecture. Data analysts curious about how raw events become business KPIs will benefit from seeing the transformation logic. It's less suited for teams needing a production-ready system out of the box.

Verdict

The credibility score of 0.9% reflects an early-stage project with 19 stars, but the documentation is thorough and the architecture demonstrates real engineering thinking. For portfolio use or learning lakehouse patterns, this is a strong choice. For production workloads, expect to invest engineering effort to harden the system first.

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