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Dhruvi Shah
Member since 2024
Silver League
31535 points
Serverless Data Processing with Dataflow: Operations
Earned Sep 15, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Sep 14, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Sep 14, 2024 EDT
Build Streaming Data Pipelines on Google Cloud
Earned Sep 13, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned Sep 13, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Aug 27, 2024 EDT
Build a Certification Study Guide: PDE Exam Prep
Earned Aug 2, 2024 EDT
使用 API Gateway 部署及保護無伺服器 API
Earned Jul 31, 2024 EDT
使用 Dataplex 整理及管理資料
Earned Jul 31, 2024 EDT
實現事件驅動的即時訊息與自動化工作流
Earned Jul 31, 2024 EDT
Google Cloud Compute 基本操作
Earned Jul 31, 2024 EDT
Gemini 和 Imagen 實務應用：建構 AI 應用程式
Earned Jul 30, 2024 EDT
使用預訓練 API 實現語音和語言解決方案
Earned Jul 30, 2024 EDT
在 Google Cloud 儲存、處理與管理資料 - 控制台
Earned Jul 30, 2024 EDT
以串流方式將分析資料傳入 BigQuery
Earned Jul 29, 2024 EDT
透過 BigQuery 建構資料倉儲
Earned Jul 28, 2024 EDT
從 BigQuery 資料取得深入分析結果
Earned Jul 28, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned Jul 28, 2024 EDT
使用 BigQuery ML 為預測模型進行資料工程
Earned Jun 20, 2024 EDT
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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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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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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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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完成「使用 API Gateway 部署及保護無伺服器 API」課程，即可獲得技能徽章。 本課程將說明如何使用 API Gateway，透過全代管閘道部署、保護及管理 API。
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完成「使用 Dataplex 整理及管理資料」技能徽章入門課程，即可證明您具備下列技能：建立 Dataplex 資產、建立切面類型，以及將切面套用至 Dataplex 中的項目。 技能徽章課程透過實作實驗室和挑戰評量，檢驗學員對於特定產品的實作知識。 完成課程或直接進行挑戰實驗室，即可取得徽章。徽章可證明您的專業能力、提升專業形象，開創更多職涯發展機會。前往個人資料頁面，追蹤您獲得的徽章。
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完成「實現事件驅動的即時訊息與自動化工作流」任務，即可獲得 技能徽章。 技能徽章課程，您將瞭解如何透過 Cloud 控制台使用 Pub/Sub、 如何使用 Cloud Scheduler 工作節省心力，並透過 Pub/Sub Lite 節省擷取大量事件 的費用。
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完成「Google Cloud Compute 基本操作」任務， 學習如何在 Compute Engine 中使用虛擬機器 (VM)、永久磁碟 和網路伺服器，即可獲得技能徽章。
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完成「Gemini 和 Imagen 實務應用：建構 AI 應用程式」技能徽章入門課程，即可證明您具備下列技能：圖片辨識、自然語言處理、
使用 Google 強大的 Gemini 和 Imagen 模型生成圖片，以及在 Vertex AI 平台上部署應用程式。
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完成使用預訓練 API 實現語音和語言解決方案課程以獲得技能徽章，了解如何使用與語音相關的 API 工具來合成和轉錄語音。
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Cloud Storage、Cloud Functions 和 Cloud Pub/Sub 都是 Google Cloud Platform 服務， 可用於儲存、處理及管理資料。您可以整合運用這三種服務， 打造各式各樣的資料導向應用程式。在這個技能徽章課程中， 您將使用 Cloud Storage 儲存圖片、透過 Cloud Functions 處理， 並利用 Cloud Pub/Sub 將圖片傳送至其他應用程式。
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完成「以串流方式將分析資料傳入 BigQuery」課程，即可獲得技能徽章。 在此課程中，您將綜合應用 Pub/Sub、Dataflow 和 BigQuery，並以串流方式傳送 資料進行分析。
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完成 透過 BigQuery 建構資料倉儲 技能徽章中階課程，即可證明您具備下列技能： 彙整資料以建立新資料表、排解彙整作業問題、利用聯集附加資料、建立依日期分區的資料表， 以及在 BigQuery 使用 JSON、陣列和結構體。
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完成 從 BigQuery 資料取得深入分析結果 技能徽章入門課程，即可證明您具備下列技能： 撰寫 SQL 查詢、查詢公開資料表、將樣本資料載入 BigQuery、使用 BigQuery 的查詢驗證工具 排解常見語法錯誤，以及在 Data Studio 中 透過連結 BigQuery 資料建立報表。
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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 Managed Service for Apache Spark 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。
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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。
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