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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-google-cloud-bigquery/
课程评论：没有评论
课程名称：Google Cloud BigQuery 入门 课程概述：BigQuery 是一种流行的数据仓库服务，使您能够轻松处理 PB 级数据。本课程旨在帮助您快速熟悉 BigQuery，并高效地使用图形用户界面、命令行工具及编程语言查询和分析数据。如果您熟悉基本的数据库概念（如表），那么您可以开始学习这个重要的数据分析平台。课程内容包括：首先讲解如何注册 Google Cloud 及使用 BigQuery 图形用户界面（GUI），然后介绍 BigQuery 的 SQL，随后讲述数据加载及如何通过命令行和 Python 使用 BigQuery。 课程的重点在于理解 BigQuery 与其他数据库的不同之处，以及如何有效和经济地使用 BigQuery。您将学习如何探索数据、表和数据集，使用 BigQuery 提供的提示和格式帮助高效编写查询。此外，您还将掌握使用 SELECT 语句，包括创建 FROM、WHERE、GROUP BY、HAVING 和 ORDER BY 子句等。课程中还会教授如何处理多个表及使用联接（joins）的知识。 您将使用 BigQuery 中的生产力工具，如保存查询、数据导出和执行详情，来提高查询性能。学习如何创建表和数据集，并通过 Cloud Storage 或直接在 BigQuery 中加载数据。尽管 BigQuery 控制台是与 BigQuery 进行交互的优秀工具，有时我们需要重复运行相同的查询或操作生成报告或下载数据，本课程将教您如何使用 bq 命令行工具，从命令行查询数据和处理数据集。 如果您偏爱使用 Python 或其他编程语言，可以使用 BigQuery 客户端库直接在程序和脚本中运行查询和其他作业，甚至无需在设备上安装 Python，因为我们将使用 Google 提供的免费服务 CoLab。课程中的测验和作业可以帮助您在学习过程中检验理解程度。 为了有效使用 BigQuery，您需要理解其设计。构建数据仓库和在 BigQuery 中设计数据模型与在 Oracle、SQL Server 和 PostgreSQL 等关系型数据库中有根本不同。本课程将讨论 BigQuery 的架构及其对数据结构和查询的影响。 授课讲师 Dan Sullivan 拥有数十年的数据工作经验。他是首席数据架构师，撰写了多本书籍和众多关于数据库及 Google Cloud 的文章。他还是 Google Cloud 专业数据工程师学习指南的作者，并为 Google Cloud 相关课程开发了课程。
BigQuery is a popular data warehouse service that allows you to easily work with petabytes of data. Learn how to quickly get up to speed with BigQuery and start querying and analyzing data efficiently using the BigQuery graphical user interface, command line utilities, and even programming languages. If you are familiar with basic database concepts, like tables, you are ready to start learning one of the most important data analytics platforms available. While some courses will focus just on using SQL with BigQuery, this course starts with the basics of signing up for Google Cloud and working the BigQuery graphical user interface (GUI), introduces SQL for BigQuery, and then moves to loading data and working with BigQuery using the command line and Python. Perhaps most importantly, you will learn how BigQuery is different from other databases and how to use that knowledge to use BigQuery efficiently and cost effectively.You will learn how to explore data, tables, and datasets. Write queries efficiently using BigQuery hints and formatting helps. Work with SELECT statements, including creating FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses to create queries that answer driving questions you have about your data. If you are not familiar with working with multiple tables and using joins, that's no problem, you will learn that in this course. Use features of BigQuery designed to make you more productive, like Saved Queries, Exporting Data, and Execution Details that help you improve the performance of your queries. Learn how to create tables and data sets and load data into BigQuery directly and by using Cloud Storage, Google Cloud's large scale object storage system.While the graphical user interface, known as the BigQuery console, is an excellent tool for interactive work with BigQuery, sometimes we need to run the same queries or operations repeatedly to generate reports or download data. In this course you will learn about the bq command line utility that lets you query data and work with datasets from the command line. If you prefer to work with Python or other programming languages, you can use the BigQuery client libraries for running queries and other jobs right from your programs and scripts. You don't even need to have Python installed on your device because we'll use CoLab, a free Google service for working with Python notebooks.Quizzes and assignments in this course allow you to check your understanding as you progress through the course by answering questions and writing queries.To use BigQuery effectively though, you need to understand how BigQuery is designed. Building a data warehouse and designing data models in BigQuery is fundamentally different than building and modeling in relational databases like Oracle, SQL Server and PostgreSQL. In this course, you will learn about BigQuery's architecture and how it influences how we structure and query data.Learn insights from an instructor with decades of experience in working with data. Dan Sullivan is a Principal Data Architect and author of books and numerous articles on databases and Google Cloud. Dan is the author the Official Google Cloud Professional Data Engineer Study Guide as well as study guides for the Google Cloud Professional Cloud Architect and Associate Cloud Engineer certifications. He has developed courses for Google Cloud, data modeling, data science, exploratory data analysis, machine learning, DevOps, and more. His courses can be found on Udemy and LinkedIn Learning.
