Summary
Full Transcript
https://github.com/knodax-labs-demo/aws-data-and-ml-labs/blob/main/13-handling-data-quality-issues-with-glue-databrew.md In this hands-on AWS tutorial, you'll learn how to clean and prepare data using AWS Glue DataBrew. We walk through fixing missing values, correcting invalid entries, removing stop words, publishing a DataBrew recipe, and running a cleaning job — all using a real dataset stored in Amazon S3. This video is perfect for data engineers, analysts, and beginners preparing for: AWS Certified Data Engineer – Associate (DEA-C01) AWS Certified Machine Learning – Specialty (MLS-C01) AWS Certified Solutions Architect / Developer / CloudOps 🔍 What You Will Learn in This Video ✔ Uploading the dataset to Amazon S3 ✔ Creating a DataBrew dataset and project ✔ Identifying missing and invalid data ✔ Filling missing numeric values using aggregates ✔ Fixing corrupt email formats ✔ Removing stop words using text transformations ✔ Publishing a DataBrew recipe ✔ Running a DataBrew job to generate cleaned output ✔ Reviewing the final results in S3 ✔ Understanding DataBrew cost considerations 🧹 Issues Fixed in the Dataset Missing Age, Name, PurchaseAmount Invalid email entry missing “.com” Stop words inside user feedback Overall data quality enhancements 👍 If You Found This Helpful Please Like, Subscribe, and Share to support the channel! More AWS Data Engineering tutorials are coming soon.
Continue this lesson in the app
Install CourseHive on Android or iOS to keep learning while you move.
