In this section, you'll build a strong foundation in Google Cloud data storage and database services, learning how different storage and database solutions support modern data engineering workloads. You'll begin by exploring Google Cloud storage options and gain an understanding of how organizations select appropriate services based on data type, scalability, performance, and application requirements. As you progress, you'll explore key Google Cloud database services, including Cloud Spanner, Cloud SQL, Bigtable, and Firestore. You'll learn how these services support different relational, non-relational, transactional, and globally distributed application workloads. The section further introduces Hybrid Transactional/Analytical Processing (HTAP) databases, helping you understand how transactional and analytical workloads can be supported within modern data architectures. By comparing the capabilities of different Google Cloud database services, you'll develop the knowledge required to select suitable data storage solutions for various business scenarios. By the end of this section, you'll have a solid understanding of Google Cloud storage and database fundamentals, enabling you to identify and select appropriate data storage and database services for scalable and reliable data engineering solutions.

In this section, you'll build a strong foundation in Google Cloud data processing and ETL, learning how to design, deploy, automate, and manage data pipelines for modern data engineering workloads. You'll begin by exploring pipeline design and development concepts, followed by deployment practices and a practical demonstration of integrating Dataflow with Pub/Sub for data processing. As you progress, you'll explore Google Cloud Dataflow and Dataproc to understand how managed services support batch and stream data processing at scale. You'll also learn about Data Loss Prevention (DLP) and Cloud Dataprep for identifying sensitive information, preparing datasets, and improving data quality for analytics. The section further introduces data lifecycle management and Cloud Composer, enabling you to understand how data can be managed throughout its lifecycle and how Apache Airflow-based workflows can be orchestrated. Through practical demonstrations, you'll explore how Cloud Composer can integrate with Dataproc and Hadoop to automate complex data processing workflows. By the end of this section, you'll have a solid understanding of Google Cloud data processing, ETL, pipeline development, data preparation, and workflow orchestration, enabling you to build scalable and automated data engineering solutions.