End-to-End Clickstream Data Engineering Project Explained ( Spark + AWS + SQL)

If you haven't built a data engineering project yet, start here. This is Part 1 of my end-to-end clickstream data engineering project series. In this video, I explain the flow from raw clickstream events landing in S3, through Spark processing, into curated tables and SQL-based business insights. Part 2 breaks down the full architecture visually so you can explain this project clearly in interviews. In this video, We cover: 1. how raw clickstream/user event data lands in S3 2. why the raw layer matters 3. what Spark actually does in the processing layer 4. how to handle duplicates, late events, and sessionization 5. what curated output tables should look like 5. how SQL turns the pipeline into business insights 6. what makes this kind of project feel real in interviews This project is designed to help you in 3 ways: 1. understand how a real DE pipeline fits together 2. build a stronger GitHub/portfolio project 3. explain it clearly in interviews The architecture is simple: events - raw storage - Spark processing - curated tables - SQL insights And that one flow can become: 1. a strong project 2. a better resume bullet 3. and a cleaner interview story If you want Part 2, comment ARCHITECTURE and I'll do the next video on: 1. folder structure 2. table design 3. sample GitHub README 4. how to explain this project in interviews Watch next: 👉 https://youtu.be/VTFCb7JazCY Subscribe for more: Data Engineering projects, Spark, SQL, AWS, interviews, and practical DE career content. #DataEngineering #ApacheSpark #AWS #BigData #DataEngineeringProject #DataEngineerRealProject #BigDataRealProject #HowtoexplainProject #realbigdataproject #clickstreamdataengineeringproject #endtoenddataengineeringproject #dataengineeringprojectarchitecture #sparkawssqlproject #clickstreampipelinearchitecture #userbehavioranalyticspipeline #awss3sparksql #sparkprojectfordataengineers #dataengineeringportfolioproject #dataengineeringgithubproject #sessionizationspark #curatedtablesdataengineering #rawlayers3 #clickstreamanalyticspipeline #dataengineerinterviewproject #endtoendpipelinesparkaws #sqlanalyticsproject #realworlddataengineeringproject #dataengineeringarchitectureexplained #dataengineeringprojectforresume Chapters 00:00 Intro 00:43 The full clickstream architecture in one line 02:20 First Layer - raw S3 layer and why it matters 02:45 Spark processing 03:49 Curated tables and why they matter 04:07 SQL analytics and business output 04:28 Configuration that makes the project feel real 04:53 What actually makes this project hard 05:10 How to explain this project in interviews 05:41 Where most people mess this up 05:57 Final architecture recap + Part 2