Real-Time E-Commerce Data Engineering Project with Kafka

Video Description

🔥 Real-Time E-Commerce Data Engineering Project using Apache Kafka, Python & Snowflake 🔥 In this video, I build a production-style, end-to-end real-time data engineering project using Apache Kafka, Python, and Snowflake. This project demonstrates how Kafka is actually used in real systems — including message keys, partitioning, streaming data validation, offset management, and warehouse-first analytics — not just basic producers and consumers. We follow a Medallion Architecture (Bronze → Silver → Gold) approach, where: Kafka handles real-time ingestion and data quality Python handles stream processing Snowflake handles analytics and business logic using SQL If you’re preparing for Data Engineering, Streaming, or Kafka-focused roles, this is a strong, interview-defensible portfolio project. 🚀 What You’ll Learn In this hands-on project, you’ll learn how to: • Generate continuous real-time events using Python • Design Kafka topics with message keys and partitions • Understand how Kafka partitioning and ordering actually work • Build a Bronze → Silver streaming pipeline • Clean and validate streaming data using Python consumers • Manage Kafka offsets safely (no data loss) • Load real-time data into Snowflake efficiently • Separate streaming logic from analytics logic • Implement Gold-layer transformations using Snowflake SQL • Think like a production data engineer, not a tutorial follower 🧠 Project Use Case This project simulates a real-time e-commerce event pipeline, where user activity data is continuously ingested, validated, and made analytics-ready. The goal is not to overuse streaming for analytics, but to show: Where Kafka is the right tool Where SQL-based warehouses like Snowflake should take over How modern data platforms are actually designed in practice 🧰 Tech Stack Used • Apache Kafka • Python • Snowflake • Kafka Producers & Consumers • Real-time Stream Processing • Medallion Architecture (Bronze → Silver → Gold) • Docker & Docker Compose • SQL Analytics (Snowflake) 📊 Project Highlights • Continuous real-time event generation • Proper Kafka message key & partition usage • Streaming data validation (Silver layer) • No fake CSVs or static datasets • Clean separation of streaming and analytics • Production-style offset management • Analytics-ready Snowflake data model • Fully portfolio-ready architecture 🗂 GitHub Repository https://github.com/Jay61616/kafka-ecommerce-analytics 🕒 Timestamps (recommended structure) 0:00 Intro 0:37 Project Overview 8:57 Infra Setup 33:00 Produce Data Stream 1:35:58 Bronze Layer Data 1:46:28 Stream Processing 2:15:16 Silver Layer Data 2:21:16 Create Snowflake Table 2:31:31 Ingest Data into Snowflake 3:12:00 Gold Transformation 3:24:58 Dashboard Creation 3:36:40 Revise Project Credits: Icons: https://www.flaticon.com/ 📣 Let’s Connect LinkedIn: https://linkedin.com/in/jayachandrakadiveti GitHub: https://github.com/Jay61616 Website: https://jay.unaux.com 🎯 Hashtags #ApacheKafka #E-CommerceProject #KafkaProject #DataEngineering #RealTimeData #StreamingData #Snowflake #Python #KafkaConsumer #KafkaProducer #MedallionArchitecture #DataPipeline #PortfolioProject