本课程是 Google Cloud 数据分析认证的第一门课程（共五门）。在本课程中，您将认识云数据分析领域，并了解云数据分析师在数据获取、存储、处理和可视化方面的角色和职责。您将探索 BigQuery 和 Cloud Storage 等基于 Google Cloud 的工具的架构，以及如何使用这些工具有效地设计数据结构，以及展示和报告数据。
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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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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在本课程中，您将了解 Google Cloud 数据工程、数据工程师的角色和职责，以及相关的 Google Cloud 产品和服务。您还将了解如何应对数据工程挑战。
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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam.
You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.
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This is the first of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll define the field of cloud data analysis and describe roles and responsibilities of a cloud data analyst as they relate to data acquisition, storage, processing, and visualization. You’ll explore the architecture of Google Cloud-based tools, like BigQuery and Cloud Storage, and how they are used to effectively structure, present, and report data.
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Google Cloud 数据分析功能简介
Earned Dec 10, 2025 EST
Serverless Data Processing with Dataflow: Foundations
Earned Feb 14, 2025 EST
Build Streaming Data Pipelines on Google Cloud
Earned Feb 10, 2025 EST
Build Batch Data Pipelines on Google Cloud
Earned Jan 24, 2025 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jan 9, 2025 EST
Google Cloud 数据工程简介
Earned Jan 4, 2025 EST
Build a Certification Study Guide: PDE Exam Prep
Earned Dec 29, 2024 EST
Introduction to Data Analytics in Google Cloud
Earned Dec 19, 2024 EST