Building Batch Data Pipelines with BigQuery and Dataproc

Modern organizations rely on steady, reliable data flows to make critical business decisions. Understanding how to extract, transform, and load batch data efficiently is a foundational skill for any aspiring data professional. This text-based course guides you through the core paradigms of data processing, helping you choose and implement the right pipeline architecture for your projects. You will transition from understanding basic data concepts to designing production-ready batch pipelines using industry-standard cloud technologies. What you'll learn: * Understand the fundamental differences between ETL, ELT, and EL paradigms to select the best approach for your data. * Build scalable data warehouses and run analytical queries using BigQuery. * Process large-scale datasets by running Spark jobs on managed Dataproc clusters and serverless environments. * Design visual, code-free pipeline workflows using Cloud Data Fusion. * Apply modern data governance and schema management practices to ensure pipeline reliability. You will start with core data engineering concepts and terminology before advancing to pipeline design. Through structured written explanations and practical SQL and Spark code scenarios, you will explore how to orchestrate diverse data tools to build cohesive batch processing systems. This course is designed for beginners, aspiring data engineers, and database administrators looking to transition to cloud-based data workflows. No prior cloud engineering experience is required. Start building reliable, automated data pipelines today.