成为会员时间：2023
Cloud Hero BigQuery Skills
Earned Aug 14, 2024 EDT
Analyzing and Visualizing Data in Looker
Earned Aug 10, 2024 EDT
Google Cloud 数据分析功能简介
Earned Aug 1, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned Oct 10, 2023 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned Sep 22, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 10, 2023 EDT
Master Google Cloud skills through hands-on labs and friendly competition! Cloud Hero challenges you to conquer a series of Cloud Skills Boost labs, putting your newfound knowledge to practice. Earn points for completing labs accurately, and rack up bonus points for speed. The leaderboard lets you see how you stack up against your peers – can you rise to the top? Remember to click "End" after finishing each lab to claim your well-deserved points.
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.
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。
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.
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.
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.