Batch Data Pipeline Engineering with Dataflow and Dataproc

Efficiently processing massive datasets is the backbone of modern business intelligence and reporting. This course teaches you how to transition from simple data scripts to designing robust, automated batch pipelines that handle large-scale transformations with ease. You will gain the skills needed to manage data movement and transformation at scale using industry-standard cloud technologies. What you'll learn: - Understand foundational data engineering concepts including ETL/ELT patterns and batch processing architectures - Build serverless data processing jobs using Apache Beam on Dataflow for unified data handling - Configure Dataproc Serverless to run Spark applications without the need to manage underlying infrastructure - Apply data quality checks and observability patterns to ensure pipeline reliability and accuracy - Orchestrate complex workflows and manage dependencies between various data processing stages - Implement modern monitoring and alerting to proactively identify and resolve pipeline failures The course begins with core definitions and architectural principles before moving into practical implementation strategies using SQL and Python-based logic. You will read through detailed explanations of pipeline design and explore how to structure code for maintainability and performance. This program is designed for beginners in data engineering who have a basic understanding of SQL and Python and are ready to apply those skills to cloud-scale data processing. Start building production-ready data pipelines today.