SQL Transformations and AWS Lambda Data Pipelines

Modern data engineering relies on efficient, scalable pipelines to transform and move massive datasets without managing heavy server infrastructure. This course guides you from the foundational concepts of cloud databases to building fully serverless, orchestrated pipelines using Redshift, AWS Lambda, and EFS. You will learn to write optimized SQL transformations, control serverless compute execution, and handle large-scale files. What you'll learn: - Learn the core principles of data warehousing, serverless orchestration, and cloud-based ETL pipelines. - Master Redshift SQL transformations using stored procedures and the MERGE command for efficient upsert operations. - Understand how to trigger and coordinate database operations using the Redshift Data API. - Configure AWS Lambda functions to manage high concurrency and process data pipelines reliably. - Apply EFS integration to Lambda to bypass local storage limits and handle large-scale datasets. - Practice modern serverless patterns, including environment variable management and structured logging for pipeline observability. You will start by mastering essential definitions and foundational terms before exploring step-by-step written explanations of SQL scripting, API orchestration, and storage configurations. This course is designed for beginners, data enthusiasts, and aspiring data engineers who have a basic grasp of SQL and want to learn cloud-native data pipeline design. Start reading today to build scalable, serverless data pipelines from scratch.