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GitHub: https://github.com/knodax-labs-demo/aws-data-and-ml-labs/blob/main/12-managing-ml-focused-data-transformation-pipelines-glue-stepfunctions.md In this hands-on AWS tutorial, you’ll learn how to build a complete end-to-end ETL workflow using AWS Glue and AWS Step Functions. We walk through uploading data to Amazon S3, cataloging it using a Glue Crawler, cleaning duplicate records, performing aggregations, and orchestrating all Glue jobs using a Step Functions workflow. This video is perfect for AWS beginners, data engineers, analysts, and anyone preparing for AWS certifications such as: AWS Certified Data Engineer – Associate (DEA-C01) AWS Certified Machine Learning – Specialty AWS Certified Solutions Architect AWS Certified Developer and CloudOps 🔍 What You Will Learn in This Video ✔ Upload customer churn data to Amazon S3 ✔ Create a Glue Data Catalog Database ✔ Build and run an AWS Glue Crawler ✔ Create a Glue ETL job to remove duplicate records ✔ Create another ETL job to calculate AVG(TotalSpent) grouped by Country ✔ Publish cleaned and aggregated data back to S3 ✔ Orchestrate both ETL jobs using AWS Step Functions ✔ Configure .sync to wait for job completion ✔ Add error handling and workflow logic ✔ Validate outputs from each stage ✔ Clean up resources to avoid AWS charges 👍 If You Found This Helpful Please Like, Subscribe, and Share to support the channel! More AWS Data Engineering and Machine Learning tutorials are coming soon.
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