Whizlabs

Google Cloud Storage, Databases & Analytics

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Whizlabs

Google Cloud Storage, Databases & Analytics

Whizlabs Instructor

Instructor: Whizlabs Instructor

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

August 2026

Assessments

6 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Google Cloud Certified Professional Data Engineer Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 3 modules in this course

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.

What's included

8 videos2 readings2 assignments1 discussion prompt

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.

What's included

13 videos1 reading2 assignments

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.

What's included

8 videos2 readings2 assignments

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