Google Cloud Foundations & Data Processing

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Overview

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Google Cloud Foundations & Data Processing is designed to provide learners with a strong foundation in Google Cloud concepts and data engineering capabilities. The course introduces essential Google Cloud services and infrastructure concepts before progressing into data storage, database technologies, data processing, ETL, and workflow automation. The course is divided into three modules, with each module further segmented into lessons and video lectures. Learners will develop an understanding of how Google Cloud services can be used to store, process, transform, protect, and manage data workloads through scalable cloud-based solutions. Course Modules: Module 1: Introduction to Google Cloud Module 2: Data Engineering Fundamentals Module 3: Data Processing & ETL By the end of this course, a learner will be able to: Understand Google Cloud fundamentals, services, regions, zones, and the Google Cloud Console. Identify appropriate Google Cloud storage and database solutions for different data workloads. Understand data processing and ETL concepts using Google Cloud services. Design and deploy data processing pipelines using services such as Cloud Dataflow and Cloud Dataproc. Apply data preparation, protection, lifecycle management, and workflow orchestration concepts using Google Cloud services. This course is intended for learners who want to build foundational knowledge of Google Cloud and develop practical understanding of data engineering, data processing, ETL, and workflow automation capabilities.

Syllabus

Introduction to Google Cloud
- In this section, you'll build a strong foundation in Google Cloud and learn how its cloud computing platform supports modern applications, infrastructure, and business workloads. You'll begin with a course and exam overview before exploring what Google Cloud is and how its services help organizations build, deploy, and manage solutions in the cloud. As you progress, you'll discover Google Cloud services and learn how regions and zones provide flexible options for deploying resources with high availability and resilience. You'll also explore the key features, benefits, and common use cases of Google Cloud to understand how organizations leverage cloud technologies to improve scalability, performance, and operational efficiency. The section further introduces the Google Cloud Console, providing a guided overview of its interface and essential capabilities. You'll learn how to navigate the console and become familiar with the tools used to manage Google Cloud resources and services. By the end of this section, you'll have a solid understanding of Google Cloud fundamentals, including its services, global infrastructure, key benefits, and management console, enabling you to confidently begin working with Google Cloud environments.
Data Engineering Fundamentals
- 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.
Data Processing & ETL
- 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.