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在 Google Cloud 控制台中运用您的技能

ALEJANDRO GARCIA CRUZ

成为会员时间：2023

Analyzing and Visualizing Data in Looker Earned Aug 18, 2024 EDT
Google Cloud 数据分析功能简介 Earned Aug 3, 2024 EDT
Preparing for your Professional Data Engineer Journey Earned Jan 17, 2024 EST
Serverless Data Processing with Dataflow: Foundations Earned Oct 12, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud Earned Oct 4, 2023 EDT
Build Streaming Data Pipelines on Google Cloud Earned Sep 22, 2023 EDT
Build Batch Data Pipelines on Google Cloud Earned Sep 13, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 7, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals Earned Sep 6, 2023 EDT
Preparing for your Professional Data Engineer Journey Earned Sep 3, 2023 EDT

In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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在本新手级课程中，您将了解 Google Cloud 数据分析工作流，以及可用于探索、分析和直观呈现数据并与相关人员共享发现结果的工具。结合案例研究、实操实验、讲座和测验/演示，本课程展示了如何将原始数据集转化为纯净数据，进而转化为实用的可视化图表和信息中心。无论您是已经在从事数据工作并想了解如何通过 Google Cloud 取得成功，还是在寻求职业发展，都可以借助本课程迈出第一步。几乎所有在工作中执行或使用数据分析的人都可以从本课程中受益。

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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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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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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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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.

了解详情