Data Engineering with AWS Glue

In the modern data landscape, organizations rely on seamless data pipelines to transform raw information into actionable insights. AWS Glue provides a powerful, serverless environment to catalog, clean, and move data at scale without the overhead of managing infrastructure. This course guides you from the fundamental concepts of data warehousing and ETL (Extract, Transform, Load) to designing robust, automated data integration workflows. You will start by mastering foundational data engineering terminology, understanding how the AWS Glue Data Catalog works, and learning to configure crawlers to automatically discover schema definitions. As you progress, you will explore modern data engineering practices, including writing PySpark ETL scripts, handling semi-structured data formats, and implementing partition strategies to optimize query performance. What you'll learn: - Understand foundational ETL concepts, serverless architecture, and AWS Glue core components - Configure the AWS Glue Data Catalog and run crawlers to automatically discover data schemas - Build and customize ETL jobs using PySpark and AWS Glue Studio conventions - Optimize data pipelines using modern partitioning techniques and efficient file formats like Parquet - Monitor, troubleshoot, and orchestrate complex data workflows securely This comprehensive text-based course is designed to take you from a beginner level to confidently orchestrating data pipelines. Through clear explanations and structured code walkthroughs, you will develop a practical understanding of serverless data integration. This course is perfect for aspiring data engineers, database administrators, and cloud beginners who want to build a solid foundation in modern AWS data services. No prior cloud engineering experience is required. Start reading today to master serverless data integration with AWS Glue.