Introduction to Data Engineering: Designing and Building Scalable Pipelines

Data fuels modern decision-making, but it needs to be structured, cleaned, and moved reliably to be useful. This text-based course guides you through the core principles of building robust data pipelines from the ground up. By working through this foundational material, you will progress from understanding core database concepts to designing efficient ETL/ELT workflows, leveraging cloud resources, and implementing reliable automation patterns. What you'll learn: * Learn foundational data engineering terminology, storage architectures, and database design principles. * Write advanced SQL queries to filter, aggregate, and transform complex datasets. * Build and automate reliable ETL/ELT pipelines using modern orchestration workflows. * Configure cloud-based data storage and processing pipelines using major platforms like AWS. * Practice handling pipeline failures, monitoring performance, and securing sensitive data. * Understand modern data engineering trends, including high-performance dataframe libraries and vector databases for AI integration. The course begins with essential terminology and database modeling before guiding you through hands-on SQL optimization, cloud architectures, and pipeline automation. You will read detailed explanations, analyze clear code snippets, and complete written conceptual exercises to solidify your skills. This course is designed for absolute beginners, aspiring data professionals, and software developers looking to transition into data engineering, with no prior pipeline experience required. Start your journey toward building reliable data infrastructure today.