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Ranganath Prabhakaran

成为会员时间：2023

青铜联赛

33945 积分
Build a Certification Study Guide: PMLE Earned Jan 19, 2025 EST
Serverless Data Processing with Dataflow: Foundations Earned Jan 7, 2024 EST
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Dec 20, 2023 EST
Build Streaming Data Pipelines on Google Cloud Earned Dec 15, 2023 EST
Build Batch Data Pipelines on Google Cloud Earned Dec 11, 2023 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned Dec 9, 2023 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Dec 3, 2023 EST
Google Cloud 数据分析基础知识 Earned Nov 1, 2023 EDT
创建和管理 AlloyDB 实例 Earned Oct 22, 2023 EDT
创建和管理 Bigtable 实例 Earned Oct 21, 2023 EDT
创建和管理 Cloud Spanner 实例 Earned Oct 13, 2023 EDT
创建和管理 Cloud SQL for PostgreSQL 实例 Earned Oct 7, 2023 EDT
用　Database Migration Service　将 MySQL 数据迁移至 Cloud SQL Earned Oct 5, 2023 EDT
[DEPRECATED] Enterprise Database Migration Earned Oct 2, 2023 EDT
Google Cloud 基础知识：核心基础设施 Earned Sep 29, 2023 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Sep 23, 2023 EDT

Learn how to use Gemini Notebook to create a personalized study guide for the Professional Machine Learning Engineer (PMLE) certification exam. You'll review Gemini Notebook features, create a notebook, and use the study guide to practice for a certification exam.

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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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了解如何在 Google Cloud 上将原始数字转化为清晰、以数据为依据的决策。通过实操实验和实际应用场景，本新手级课程将引导您在 BigQuery 中查询数据、在 Looker 中探索趋势，以及在数据洞察中制作可共享的交互式报告。

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完成入门级技能徽章课程创建和管理 AlloyDB 实例，展示您在以下方面的技能：执行核心 AlloyDB 操作 和任务、从 PostgreSQL 迁移到 AlloyDB、管理 AlloyDB 数据库，以及 使用 AlloyDB 列式引擎加速分析查询。

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完成入门级技能徽章课程创建和管理 Bigtable 实例，展示以下方面的技能：创建实例、设计架构、 查询数据，以及在 Bigtable 中执行管理任务，包括监控性能、配置节点自动扩缩和复制。

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完成创建和管理 Cloud Spanner 实例 这一入门级技能徽章课程，展示您在以下方面的技能： 创建 Cloud Spanner 实例和数据库并与之互动； 使用各种方法加载 Cloud Spanner 数据库； 备份 Cloud Spanner 数据库；定义架构并了解查询计划； 部署连接到 Cloud Spanner 实例的现代 Web 应用。

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完成“创建和管理 Cloud SQL for PostgreSQL 实例”这一入门级的技能徽章课程，展示您在以下方面的技能： 迁移、配置和管理 Cloud SQL for PostgreSQL 实例及数据库。

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完成用　Database Migration Service　将 MySQL 数据迁移至 Cloud SQL 这一入门级的技能徽章课程，展示您在以下方面的技能： 使用 Database Migration Service 中提供的不同作业类型和连接选项，将 MySQL 数据迁移到 Cloud SQL； 以及在运行 Database Migration Service 作业时 迁移 MySQL 用户数据。

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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.

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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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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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