Juan Ramirez
成为会员时间：2022
青铜联赛
6910 积分
Google Cloud 带来信任与安全
Earned Jan 29, 2024 EST
借助 Google Cloud 人工智能进行创新
Earned Jan 29, 2024 EST
使用 Google Cloud Operations 进行扩缩
Earned Mar 24, 2023 EDT
使用 Google Cloud 进行基础设施和应用的现代化改造
Earned Mar 22, 2023 EDT
使用 Google Cloud 探索数据转换
Earned Mar 17, 2023 EDT
利用 Google Cloud 实现数字化转型
Earned Mar 17, 2023 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理
Earned Dec 5, 2022 EST
使用 BigQuery 构建数据仓库
Earned Dec 5, 2022 EST
在 Google Cloud 上为机器学习 API 准备数据
Earned Dec 2, 2022 EST
从 BigQuery 数据中挖掘数据洞见
Earned Nov 4, 2022 EDT
[DEPRECATED] Building Advanced Codeless Pipelines on Cloud Data Fusion
Earned Nov 2, 2022 EDT
Build a Certification Study Guide: PDE Exam Prep
Earned Oct 19, 2022 EDT
Serverless Data Processing with Dataflow: Operations
Earned Oct 17, 2022 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Oct 14, 2022 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Sep 12, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Sep 8, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Sep 6, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Sep 3, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Aug 26, 2022 EDT
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals
Earned Aug 16, 2022 EDT
Google Cloud 基础知识：核心基础设施
Earned Aug 4, 2022 EDT
Google Cloud 基础知识
Earned Jul 21, 2022 EDT
随着组织将数据和应用迁移到云端，他们必须妥善应对快速变化的安全挑战格局。本课程将探讨云安全基础知识、Google Cloud 基于安全性设计的基础设施的价值以及纵深防御策略，并重点介绍依托 AI 的运营和合规工具如何帮助组织满足严格的全球监管要求。
作为“Cloud Digital Leader 学习路线”的一部分，本课程旨在帮助个人提升职业素养，并为企业谋求未来发展。
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人工智能 (AI) 和机器学习 (ML) 代表着信息技术领域的重要演变，正在迅速改变各行各业。《利用 Google Cloud 人工智能进行创新》探讨了组织如何使用 AI 和机器学习来转变业务流程。
本课程是“Cloud Digital Leader 学习路线”的一部分，旨在帮助个人在自己的角色中不断成长，并为企业谋求未来发展。
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各种规模的组织都在利用云的强大功能和灵活性来改变运营方式。然而，有效管理和扩缩云资源可能是一项复杂的任务。本课程探讨了云中现代运营、可靠性和弹性方面的基本概念，以及 Google Cloud 如何帮助支持这些工作。
作为 Cloud Digital Leader 学习路线的一部分，本课程旨在帮助个人在自己的岗位上成长，并为企业谋求未来发展。
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许多传统企业都在使用无法满足现代客户期望的旧式系统和应用。业务领导者往往需要在维护老旧的 IT 系统和投资新产品与服务之间做出选择。本课程探讨了这些挑战，并提供了利用云技术克服这些挑战的解决方案。
作为 Cloud Digital Leader 学习路线的一部分，本课程旨在帮助个人在工作中成长，并为企业打造美好未来。
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云技术是一项强大的资产，与数据相结合后，它将成为创新和提升客户体验的催化剂。《使用 Google Cloud 探索数据转换》课程探讨了组织如何利用云技术让数据更易于访问、更具实用价值和商业价值。
作为“Cloud Digital Leader 学习路线”的一部分，本课程旨在帮助个人提升职业素养，并为企业谋求未来发展。
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数字化转型是现代组织的必经之路，而扎实掌握云计算基础知识，是推动实质性创新的第一步。《利用 Google Cloud 实现数字化转型》课程将讲解核心技术与战略框架，助力组织实现运营现代化。本课程介绍关于云的基本概念、全球网络基础设施和责任共担模式，帮助领导者从容推进上云进程。
作为 Cloud Digital Leader 学习路线的一部分，本课程旨在帮助学员提升岗位能力，从而为企业打造美好未来。
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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。
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完成中级技能徽章课程使用 BigQuery 构建数据仓库，展示以下技能： 联接数据以创建新表、排查联接故障、使用并集附加数据、创建日期分区表， 以及在 BigQuery 中使用 JSON、数组和结构体。
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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Managed Service for Apache Spark 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
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完成入门级技能徽章课程“从 BigQuery 数据中挖掘数据洞见”，展示您在以下方面的技能： 编写 SQL 查询、查询公共表、将示例数据加载到 BigQuery 中、 在 BigQuery 中使用查询验证器排查常见的语法错误，以及通过连接到 BigQuery 数据在 Data Studio 中 创建报告。
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This advanced-level Quest builds on its predecessor Quest, and offers hands-on practice on the more advanced data integration features available in Cloud Data Fusion, while sharing best practices to build more robust, reusable, dynamic pipelines. Learners get to try out the data lineage feature as well to derive interesting insights into their data’s history.
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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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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.
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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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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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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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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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“Google Cloud 基础知识：核心基础设施”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。
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在本入门级课程中，您将了解 Google Cloud 的基础工具和服务。此课程提供了可选视频， 旨在帮助您深入了解和回顾实验中涉及的概念。Google Cloud 基础知识是推荐给 Google Cloud 学员的第一门课程 - 即使您几乎没有云相关知识，也能从中获得实践 经验，并将其直接运用于您的首个 Google Cloud 项目。从编写 Cloud Shell 命令和部署您的第一个虚拟机，到在 Kubernetes Engine 上运行应用 或者使用负载均衡，“Google Cloud 基础知识”都是您了解该平台 基本功能的首选入门级课程。
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