Foundations of Data Pipelines, ETL, and Spark

In today's data-driven world, efficiently moving and transforming vast amounts of information is crucial for informed decision-making. This course equips you with the foundational knowledge and practical skills to understand, design, and implement essential components of modern data ecosystems. You will learn how to build robust data pipelines, apply fundamental ETL (Extract, Transform, Load) principles, and leverage Spark for scalable data processing, preparing you for entry-level data engineering tasks. What you'll learn: * Understand the core components and architecture of a data ecosystem. * Apply fundamental ETL (Extract, Transform, Load) principles for data transformation. * Utilize Spark for efficient processing and analysis of large datasets. * Design and implement basic data pipelines for automated data flow. * Explore foundational concepts of data quality and monitoring within pipelines. * Grasp the principles of data orchestration for managing complex workflows. The course begins with an introduction to data ecosystem concepts and data types, progressing through the stages of ETL and practical application of Spark for data manipulation. You will then learn to construct simple data pipelines and explore key considerations for data quality and workflow management. This course is designed for absolute beginners with no prior experience in data engineering or programming, providing a clear path into the field. Begin your journey into data engineering by mastering these essential skills.