Google Cloud Storage, Databases & Analytics

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Overview

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Google Cloud Storage, Databases & Analytics is designed to provide learners with a strong understanding of Google Cloud storage, data lakes, managed database services, data warehousing, and analytics capabilities. The course focuses on how Google Cloud services can be used to securely store, manage, optimize, and analyze data at scale. The course is divided into three modules, with each module further segmented into lessons and video lectures. Learners will explore cloud storage and data lake architectures, evaluate managed database solutions for different workloads, and use BigQuery for scalable data warehousing and analytics. Course Modules: Module 1: Cloud Storage & Data Lakes Module 2: Managed Database Services Module 3: BigQuery & Data Analytics By the end of this course, a learner will be able to: Understand Google Cloud Storage and data lake concepts, including data discovery, security, monitoring, and cost optimization. Evaluate and select appropriate managed database services based on workload, connectivity, scalability, performance, and cost requirements. Optimize database solutions across services such as Cloud SQL, Cloud Spanner, AlloyDB, Bigtable, and Firestore. Understand BigQuery capabilities for cloud data warehousing, analytics, monitoring, and query processing. Analyze and visualize data using BigQuery and Looker Studio while understanding data warehouse migration and hybrid multi-cloud concepts. This course is intended for learners who want to develop practical knowledge of Google Cloud storage, databases, data lakes, data warehousing, and analytics and build the skills required to work with modern cloud data solutions.

Syllabus

Cloud Storage & Data Lakes
- In this section, you'll build a strong foundation in Google Cloud storage and data lake solutions, learning how to store, manage, secure, and optimize data for modern analytics workloads. You'll begin by exploring Google Cloud Storage and its integration capabilities, gaining an understanding of how object storage supports scalable and reliable data lake architectures. As you progress, you'll explore cost optimization techniques for Google Cloud Storage and data lakes, along with Google Cloud data warehouse concepts. You'll also learn how to configure data discovery, manage access and encryption, and apply cost controls to maintain secure and efficient data environments. The section further introduces data lake monitoring and Memorystore, including Memorystore for Redis Cluster. You'll learn how monitoring helps maintain data lake health and performance, while high availability and replication capabilities help improve the resilience and reliability of Redis-based applications. By the end of this section, you'll have a solid understanding of Google Cloud storage, data lake management, security, cost optimization, monitoring, and caching solutions, enabling you to design efficient, secure, and highly available data storage architectures.
Managed Database Services
- In this section, you'll build a strong foundation in Google Cloud managed database services, learning how to select, connect, optimize, and manage database solutions for different application and workload requirements. You'll begin by exploring Cloud SQL and its supported database engines, along with key considerations for database connectivity, access management, and application integration. As you progress, you'll learn how to evaluate appropriate Google Cloud database solutions and optimize cost and performance across services such as Cloud SQL, Cloud Spanner, and AlloyDB. You'll explore capacity planning and usage considerations to ensure database resources are aligned with application requirements and business needs. The section further introduces Bigtable and Firestore, helping you understand their connectivity, performance optimization, and common application use cases. You'll learn how to select and configure managed database services based on scalability, performance, connectivity, and workload requirements. By the end of this section, you'll have a solid understanding of Google Cloud managed database services, enabling you to evaluate database options, manage connectivity, optimize performance and costs, and select appropriate solutions for modern applications.
BigQuery & Data Analytics
- In this section, you'll build a strong foundation in Google Cloud BigQuery and data analytics, learning how BigQuery supports large-scale data warehousing, querying, monitoring, and business intelligence. You'll begin by exploring BigQuery fundamentals and understand how its serverless architecture enables organizations to analyze large datasets efficiently. As you progress, you'll explore BigQuery analytics and monitoring capabilities, including views and different query types. Through guided demonstrations, you'll gain practical experience running interactive and batch query jobs and using Looker Studio to visualize and present analytical insights. The section further introduces data warehouse migration and hybrid multi-cloud concepts, helping you understand how organizations can modernize existing data platforms and integrate analytics workloads across multiple cloud environments. By the end of this section, you'll have a solid understanding of BigQuery and its analytics capabilities, enabling you to query, visualize, monitor, and manage data warehouse workloads across modern cloud environments.