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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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 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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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.
雲端架構：設計、實作與管理
Earned Aug 12, 2024 EDT
Google Cloud 基礎知識：核心基礎架構
Earned Jun 10, 2024 EDT
在 Google Cloud 為機器學習 API 準備資料
Earned May 26, 2024 EDT
在 Compute Engine 導入 Cloud Load Balancing
Earned May 23, 2024 EDT
透過 BigQuery 建構資料倉儲
Earned May 23, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned May 19, 2024 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Nov 6, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned Oct 26, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Oct 25, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Oct 8, 2023 EDT