ETL and ELT Fundamentals: Building Data Pipelines

Data movement is the backbone of analytics and machine learning, but setting up reliable pipelines can be challenging without the right foundation. Start mastering the essential techniques for collecting and preparing data for use. By the end of this course, you will understand the mechanics of ETL and ELT, enabling you to design and implement robust workflows that ensure data quality and availability. What you'll learn: * Understand the core concepts and historical differences between ETL (Transform before Load) and modern ELT (Load before Transform). * Apply foundational data extraction techniques from various common data sources, including files and APIs. * Practice defining transformation logic to clean, normalize, and aggregate data according to business rules. * Configure basic mechanisms for loading transformed data into target destinations like data warehouses or databases. * Implement fundamental data quality checks and validation steps within your pipeline workflows. * Learn foundational concepts of pipeline orchestration and scheduling to automate data flows reliably. The course begins with defining key terminology and data architecture concepts before moving into practical steps for building the Extract, Transform, and Load stages sequentially. We focus on written explanations and practical code examples to illustrate core pipeline logic. This course is specifically designed for absolute beginners in data engineering, data analysis, or software development interested in data infrastructure. No prior experience with ETL tools or data warehousing is required. Start building your data infrastructure expertise today.