AWS Glue and Glue DataBrew Essentials for Data Engineering

Transforming messy raw information into structured, analysis-ready datasets is a core requirement for modern data pipelines. This text-based course introduces you to the essentials of AWS Glue and Glue DataBrew, giving you the practical understanding needed to automate data preparation, catalog schemas, and run serverless ETL jobs. You will begin with essential cloud data concepts, terminology, and architectural patterns before moving into practical transformation techniques. By exploring visual data profiling in DataBrew alongside scalable data pipelines in Glue, you will learn how to design dependable data workflows from ingestion to analysis. What you'll learn: - Understand foundational data engineering concepts, serverless ETL mechanics, and data lake architectures - Profile, clean, and normalize datasets using AWS Glue DataBrew transformation recipes - Configure AWS Glue Crawlers to automatically detect schemas and populate the Data Catalog - Build and manage scalable ETL jobs to process structured and semi-structured datasets - Apply data quality rules and validation checks to maintain pipeline integrity - Integrate modern data lakehouse patterns and manage workflow triggers effectively The reading begins with core definitions and service overviews, then moves methodically into pipeline setup, schema management, and practical data transformation scenarios. This course is tailored for aspiring data engineers, data analysts, and cloud beginners seeking practical knowledge of cloud ETL. No prior experience with AWS data services is required. Start reading today to build reliable, automated data preparation workflows in the cloud.