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GitHub: https://github.com/knodax-labs-demo/aws-data-and-ml-labs/blob/main/02-aws-glue-data-quality-tutorial.md Designing Data Repositories and Storage-Solutions for ML and DE In this step-by-step tutorial, you’ll learn how to evaluate data quality using AWS Glue Data Quality rules — a crucial step in building reliable data engineering and machine learning pipelines on AWS. We’ll walk through how to create a data quality job, define rules, interpret the results, and ensure your data is accurate, complete, and ready for downstream analytics. ⚠️ Important: AWS Glue is not free. You’re charged based on Data Processing Units (DPUs) used and runtime, billed per second with a 1-minute minimum. If you want to avoid charges, you can simply watch this tutorial without following along in your own AWS account. 🔍 What You’ll Learn: How to create a data quality evaluation job in AWS Glue How to define and apply data quality rules (e.g., ColumnCount, RowCount, IsUnique) How to interpret the evaluation results How to ensure your data is clean, complete, and ready for ML workflows By the end of this video, you’ll know how to use AWS Glue Data Quality to improve data reliability and streamline your ETL and ML pipelines — an essential skill for AWS Certified Data Engineer – Associate (DEA-C01) and Machine Learning – Specialty (MLS-C01) exam prep. ⚠️ Important: AWS Glue is not free. You’re billed based on DPU usage and runtime, charged per second with a 1-minute minimum. If you want to avoid charges, simply watch the tutorial instead of following along. And remember to delete any resources created during this demo to avoid ongoing costs. 📚 More AWS Tutorials & Exam Prep: 👉 Subscribe to the KnoDAX YouTube channel for more AWS hands-on labs, certification guides, and cloud projects. 👉 Explore our full AWS certification books and study guides: https://www.amazon.com/stores/SK-Singh/author/B09L6H39XM #AWSGlue #DataQuality #AWSTutorial #S3 #DataEngineer #MachineLearning #KnoDAX #AWSCertification #CloudComputing #DataPipeline
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