成为会员时间：2024
透過 BigQuery 建構資料倉儲
Earned Jul 7, 2024 EDT
開始使用 Pub/Sub
Earned Jul 4, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned Jul 2, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned May 16, 2024 EDT
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。
完成「開始使用 Pub/Sub」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。
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.
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.