Getting Started with Apache Kafka and Confluent Platform on Google Cloud
Earned Apr 9, 2026 EDT
While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.
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完成 使用 Gemini 和 Streamlit 開發生成式 AI 應用程式 技能徽章中階課程，即可證明您具備下列技能： 生成文字、透過 Python SDK 和 Gemini API 呼叫函式，以及運用 Cloud Run 部署 Streamlit 應用程式。 您將瞭解如何以不同方式透過提示請 Gemini 生成文字、使用 Cloud Shell 測試及疊代 Streamlit 應用程式，隨後封裝成 Docker 容器並在 Cloud Run 中部署。
Organizations around the world rely on Apache Kafka to integrate existing systems in real time and build a new class of event streaming applications that unlock new business opportunities. Google and Confluent are in a partnership to deliver the best event streaming service based on Apache Kafka and to build event driven applications and big data pipelines on Google Cloud Platform. In this course, you will first learn how to deploy and create a streaming data pipeline with Apache Kafka, then try out the different functionalities of the Confluent Platform.
Build Data Lakes and Data Warehouses on Google Cloud
Earned May 27, 2026 EDT
Cloud SQL
Earned May 19, 2026 EDT
使用 Gemini 和 Streamlit 開發生成式 AI 應用程式
Earned May 4, 2026 EDT
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識
Earned May 3, 2026 EDT
Gemini 和 Imagen 實務應用：建構 AI 應用程式
Earned Apr 28, 2026 EDT
透過 IAM 設定特殊存取權
Earned Apr 27, 2026 EDT
透過 Google AI Studio 開發 AI 輔助原型
Earned Apr 25, 2026 EDT
建立您的第一個 Gemini Enterprise 應用程式
Earned Apr 24, 2026 EDT
在 Agent Platform 探索生成式 AI
Earned Apr 24, 2026 EDT
使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL
Earned Apr 21, 2026 EDT
使用 Agent Development Kit (ADK) 建造 AI 代理：工程設計
Earned Apr 20, 2026 EDT
為 Google Cloud 設定服務帳戶和 IAM 角色
Earned Apr 20, 2026 EDT
使用 Gemini 多模態功能和多模態 RAG 檢查複合型文件
Earned Apr 19, 2026 EDT
建立及管理 AlloyDB 執行個體
Earned Apr 16, 2026 EDT
建立及管理 Bigtable 執行個體
Earned Apr 13, 2026 EDT
建立及管理 Cloud Spanner 執行個體
Earned Apr 12, 2026 EDT
建立及管理 PostgreSQL 適用的 Cloud SQL 執行個體
Earned Apr 10, 2026 EDT
Getting Started with Apache Kafka and Confluent Platform on Google Cloud
Earned Apr 9, 2026 EDT