Fundamentals of Data Engineering on the Cloud

As organizations generate massive amounts of information, the ability to build reliable systems that collect, store, and analyze this data is more critical than ever. This course introduces you to the core principles of data engineering on modern cloud infrastructure. You will transition from understanding basic data concepts to designing scalable pipelines that power business intelligence and machine learning. Through clear, written explanations and structured examples, you will learn how to select the right storage solutions, process streaming and batch data, and maintain clean data architectures. What you'll learn: 1. Understand the core concepts, terminology, and roles within cloud data engineering. 2. Compare and select appropriate cloud storage options, from data lakes to relational databases. 3. Design batch and streaming data pipelines using Pub/Sub and Dataflow. 4. Query and manage large-scale analytical datasets using BigQuery. 5. Integrate modern data lakehouse patterns and dbt concepts for efficient data transformation. 6. Implement essential data governance, access controls, and pipeline observability. The course starts with essential terminology and foundational architecture concepts before moving into practical pipeline design. You will progress step-by-step from data ingestion and storage selection to processing, transformation, and analytical querying. This course is designed for aspiring data engineers, database administrators, and technology enthusiasts looking for a beginner-friendly introduction to cloud data systems. No prior cloud or data engineering experience is required. Start building your foundation in cloud data engineering today.