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.
了解详情
完成 使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL 技能徽章入門課程，證明您具備下列技能： 使用「資料庫遷移服務」中各種可用的工作類型和連線選項， 將 MySQL 資料遷移至 Cloud SQL，以及在執行「資料庫遷移服務」工作時 遷移 MySQL 使用者資料。
「Google Cloud 基礎知識：核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室，介紹並比較 Google Cloud 的多種運算和儲存服務，同時提供重要的資源和政策管理工具。
了解详情
This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
了解详情
This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
了解详情
In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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.
了解详情
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.
了解详情
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.
了解详情
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.
了解详情
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.
了解详情
完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。
了解详情
完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Dataproc 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
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.
了解详情
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.
了解详情
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.
建立及管理 AlloyDB 執行個體
Earned Aug 4, 2023 EDT
建立及管理 Bigtable 執行個體
Earned Jul 28, 2023 EDT
建立及管理 PostgreSQL 適用的 Cloud SQL 執行個體
Earned Jul 27, 2023 EDT
Enterprise Database Migration
Earned Jul 25, 2023 EDT
使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL
Earned Jul 17, 2023 EDT
建立及管理 Cloud Spanner 執行個體
Earned Jul 11, 2023 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Jul 6, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 26, 2023 EDT
透過 BigQuery 建構資料倉儲
Earned Jun 19, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned Jun 19, 2023 EDT
Developing Data Models with LookML
Earned Mar 19, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Mar 16, 2023 EDT
在 Looker 應用進階 LookML 概念
Earned Mar 15, 2023 EDT
Understanding LookML in Looker
Earned Mar 6, 2023 EST
為 Looker 資訊主頁和報表準備資料
Earned Mar 6, 2023 EST
Serverless Data Processing with Dataflow: Operations
Earned Mar 3, 2023 EST
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Feb 28, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned Oct 4, 2022 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Sep 20, 2022 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Sep 4, 2022 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Aug 21, 2022 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Aug 19, 2022 EDT
在 Compute Engine 導入 Cloud Load Balancing
Earned Aug 17, 2022 EDT
Build Batch Data Pipelines on Google Cloud
Earned Aug 16, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Aug 6, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jul 6, 2022 EDT