成为会员时间：2024
使用 BigQuery 构建数据仓库
Earned Jul 7, 2024 EDT
Pub/Sub 使用入门
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。
完成 Pub/Sub 使用入门技能徽章课程，赢取技能徽章。 在这门课程中，您将学习如何通过 Cloud 控制台使用 Pub/Sub、如何使用 Cloud Scheduler 作业减轻工作量，以及在哪些用例中可以使用 Pub/Sub Lite 来节省大量事件提取的开支。
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.