Cloud Data Engineering with Spark and Airflow

Are you looking to enter the dynamic field of data engineering or enhance your skills in managing large datasets? Understanding how to build robust and scalable data pipelines is essential for any modern data professional. This course provides a foundational understanding of cloud-based data engineering, equipping you with the practical knowledge to design, implement, and monitor efficient data processing workflows. You will gain hands-on experience with key technologies used to extract, transform, and load data at scale, preparing you for real-world data challenges. What you'll learn: Learn core data engineering concepts, principles, and common architectures. Build and orchestrate data pipelines using Apache Airflow for scheduling and monitoring. Process and transform large datasets efficiently with Apache Spark on cloud infrastructure. Understand fundamental AWS services for data storage, compute, and networking. Apply basic containerization principles for deploying pipeline components. Implement foundational practices for data quality and pipeline observability. The course begins with foundational data engineering concepts, progressing through practical implementations of data pipelines using cloud-based services. You will move from understanding individual components to integrating them into a cohesive data workflow. This course is designed for beginners with no prior data engineering experience. No specific technical prerequisites are required, just a willingness to learn how data moves and transforms in modern systems. Start your journey into data engineering and build the skills needed to manage the flow of information.