Serverless Data Processing with Dataflow: Foundations
Earned May 24, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 22, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned May 18, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Apr 28, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Mar 25, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jan 24, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jan 15, 2023 EST
在 Google Cloud 上为机器学习 API 准备数据
Earned Jan 10, 2023 EST
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Earned Jan 9, 2023 EST
构建安全的 Google Cloud 网络
Earned Jan 7, 2023 EST
Google Cloud Computing Foundations: Networking & Security in Google Cloud - Locales
Earned Jan 6, 2023 EST
在 Google Cloud 上设置应用开发环境
Earned Jan 4, 2023 EST
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Earned Jan 4, 2023 EST
为 Compute Engine 实现云负载均衡
Earned Jan 1, 2023 EST
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Earned Dec 29, 2022 EST
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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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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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.
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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 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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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。
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This course, Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud. The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Googl…
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完成构建安全的 Google Cloud 网络课程，赢取技能徽章。在此课程中，您将了解与网络有关的众多 资源，以便在 Google Cloud 上构建、扩缩和保护自己的应用。
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This course, Google Cloud Computing Foundations: Networking & Security in Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Google Cloud Computing Foundations: Networking & Security in Google Cloud.
The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Dat…
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完成“在 Google Cloud 上设置应用开发环境”课程，赢取技能徽章；通过该课程，您将了解如何使用以下技术的基本功能来构建和连接以存储为中心的云基础设施： Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。
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This course, Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Google Cloud Computing Foundations: Infrastructure in Google Cloud. The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google C…
This course, Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Google Cloud Computing Foundations: Cloud Computing Fundamentals .The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
…
Serverless Data Processing with Dataflow: Foundations
Earned May 24, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned May 22, 2023 EDT
Build Streaming Data Pipelines on Google Cloud
Earned May 18, 2023 EDT
Build Batch Data Pipelines on Google Cloud
Earned Apr 28, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Mar 25, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Jan 24, 2023 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned Jan 15, 2023 EST
在 Google Cloud 上为机器学习 API 准备数据
Earned Jan 10, 2023 EST
Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud - Locales
Earned Jan 9, 2023 EST
构建安全的 Google Cloud 网络
Earned Jan 7, 2023 EST
Google Cloud Computing Foundations: Networking & Security in Google Cloud - Locales
Earned Jan 6, 2023 EST
在 Google Cloud 上设置应用开发环境
Earned Jan 4, 2023 EST
Google Cloud Computing Foundations: Infrastructure in Google Cloud - Locales
Earned Jan 4, 2023 EST
为 Compute Engine 实现云负载均衡
Earned Jan 1, 2023 EST
Google Cloud Computing Foundations: Cloud Computing Fundamentals - Locales
Earned Dec 29, 2022 EST