Fundamentals of ETL and ELT Data Pipelines

Need to move data from various sources into a central repository for analysis? Understanding how to build robust data pipelines is a fundamental skill for any data professional. This course provides a practical introduction to the core principles of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). You will learn the complete lifecycle of data integration, from extracting data from different systems to loading it into a data warehouse, ready for analysis. You'll gain the foundational knowledge to design and implement your own simple data pipelines, making you more effective in any data-driven role. What you'll learn: - Understand the key differences between ETL and ELT and when to use each approach. - Learn the three core stages: extracting from APIs and databases, transforming data for quality and consistency, and loading into a data warehouse. - Practice designing a basic data pipeline schema for a common business scenario. - Explore the concepts behind modern tools for orchestration (like Apache Airflow) and transformation (like dbt). - Apply fundamental data quality checks to ensure your data is reliable and trustworthy. - Build a complete, simple data pipeline from source to destination through written exercises. - Grasp the basics of working with cloud data platforms and warehouses. The course begins with essential terminology and the architectural concepts behind data integration. You will then progress through guided explanations of each pipeline stage, applying what you learn to construct a functional workflow. This course is designed for absolute beginners. It is ideal for aspiring data engineers, data analysts who want to understand data flow, and anyone new to the field of data integration. No prior experience with data pipelines is necessary. Start your journey into data engineering and learn to manage the flow of data today.