Cloud Data Engineering Fundamentals with BigQuery and Dataflow

As organizations generate massive amounts of data daily, the demand for skilled professionals who can design robust pipelines is higher than ever. This course introduces you to the core principles of data engineering on modern cloud platforms, focusing on scalable storage and processing. You will transition from understanding basic database concepts to designing and executing production-ready data workflows. Through clear written explanations and practical scenarios, you will learn how to ingest, transform, and store data efficiently using tools like BigQuery, Pub/Sub, and Dataflow, while incorporating modern practices like schema management and pipeline orchestration. What you'll learn: Understand foundational data engineering concepts, including ETL/ELT patterns and storage types; Configure scalable data warehouses and run optimized queries using BigQuery; Build streaming and batch ingestion pipelines with Pub/Sub and Dataflow; Apply modern data lakehouse architecture concepts to organize unstructured and structured data; Design clean, structured schemas that ensure data quality and governance across your pipelines; Practice writing SQL transformations and pipeline logic through step-by-step written exercises. The course begins with essential terminology and structural concepts before guiding you through hands-on pipeline scenarios. You will progress from basic data storage setup to designing fully automated, serverless data workflows. This course is designed for beginners, aspiring data engineers, and analysts looking to transition to cloud infrastructure. No prior cloud experience is required, though a basic familiarity with SQL is helpful. Start building your cloud data engineering foundation today.