Welcome to Week 1. This week, we’ll begin by exploring Cloud SQL, its supported database engines (MySQL, PostgreSQL, and SQL Server), and how it simplifies traditional relational database management in the cloud. You’ll also get familiar with Cloud Spanner as well as Cloud Bigtable. Next, we’ll explore BigQuery and its key features, Google’s powerful serverless data warehouse. You’ll learn how to manage and monitor your data analytics pipelines, build dashboards with Looker Studio. We’ll then shift focus to NoSQL databases like Firestore, and cover best practices for database capacity planning and performance optimization. You’ll also explore Cloud Dataflow, Cloud Dataproc, and Cloud DataPrep—essential tools for data processing, ETL pipelines, and workflow orchestration. Finally, we’ll introduce some of Google’s AI-powered APIs including Cloud Speech-to-Text, Vision API, and Translate API. By the end of this module, you’ll have a well-rounded understanding of Google Cloud’s database and analytics ecosystem, and how to architect data-driven applications that are scalable, cost-effective, and intelligent.

Welcome to Week 2. This week, we’ll explore Google Cloud’s storage solutions and services, starting with an overview of the different storage options available on GCP, including object, block, and file storage. Through hands-on demonstrations, we’ll walk through setting up Cloud Storage buckets, uploading objects, and managing access policies. You'll also be introduced to Storage Transfer Service, which enables you to efficiently migrate data from on-premises or other cloud providers to Google Cloud. We’ll discuss Transfer Appliance, a secure, high-capacity hardware solution designed for large-scale data transfers when network limitations exist. By the end of this module, you’ll have practical experience with storage provisioning and migration, and you’ll be equipped to choose the right GCP storage solution for your use case while ensuring reliable and cost-effective data transfer strategies.