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所在平台: Udemy |
课程主页: https://www.udemy.com/course/dp-700-implementing-data-engineering-solutions-using-fabric/
课程评论：没有评论
**课程名称：Microsoft DP-700 备考：Fabric 数据工程师关联认证** **课程概述：** 本课程旨在帮助您准备 Microsoft DP-700 "Fabric 数据工程师关联" 认证考试，内容基于《使用 Microsoft Fabric 实现分析解决方案》的学习指南。本课程同样适用于以下 Microsoft 应用技能： * APL-3008 "使用 Microsoft Fabric 实现实时智能解决方案" * APL-3009 "在 Microsoft Fabric 中实现 Lakehouse" * APL-3010 "在 Microsoft Fabric 中实现数据仓库" **免责声明：** 本课程并非由 Microsoft 官方认可、赞助或 affiliated。 **学员评价：** 学生们普遍认为本课程内容详实、讲解清晰，从基础概念循序渐进地引入复杂知识点，并能有效解答常见问题。许多学员表示，相比官方培训，本课程提供了更深入的学习体验。课程的实践活动被认为非常有益于知识的应用。讲师能够将广泛的 Microsoft Fabric 平台内容以易于理解的方式呈现，并对学员的问题反应迅速。 **课程内容：** 本课程将涵盖以下关键技能和知识点： 1. **Microsoft Fabric 概览：** 快速浏览 Fabric 平台。 2. **Dataflow Gen2 和数据管道：** * 数据摄取和复制 * 数据管道的调度和监控 3. **Notebook 数据操作：** * 使用 PySpark 和 SQL 进行数据操作 * 使用 Notebook 加载和保存数据 * 数据帧（DataFrames）的操作：列和行的选择 * 数据类型的转换、数据帧的聚合和排序 4. **Lakehouse 数据转换：** * 在 Lakehouse 中转换数据 * 数据合并与连接 * 识别缺失数据或 NULL 值 * 优化 Notebook 性能和自动化 Notebook * 对象创建：快捷方式和文件分区 5. **数据仓库实践：** * 数据转换 * 创建增量数据加载 * 数据仓库管理和优化 6. **Eventhouse 操作：** * 使用 KQL（Kusto Query Language）进行数据转换 * 数据选择、筛选和聚合 * 字符串、数字、日期时间和时间间隔函数的应用 * 数据转换、合并与连接等高级操作 7. **实时数据流处理：** * 数据摄取和转换 * 复习 DP-600 考试中的 KQL 知识 * 工作区设置和监控 **先修知识：** 本课程**不假设任何先修知识**。所有语言和概念将从零开始讲解。然而，具备 PySpark、SQL 或 KQL 的基础知识将会更有帮助。 **学习成果：** 完成本课程后，您将熟练掌握使用 Notebook 通过 PySpark 进行数据操作。通过进一步的实践和对其他主题的学习，您将能够应对 DP-700 "Microsoft Certified: Fabric Data Engineer Associate" 认证考试，为您的简历增添一份有价值的认证。 **加入课程，开启您的学习之旅！**
This course covers the content required for the Microsoft DP-700 "Fabric Data Engineer Associate" certification exam, using the Study Guide for "Implementing Analytics Solutions Using Microsoft Fabric".This course is also useful for the following Microsoft Applied Skills:APL-3008 "Implement a Real-Time Intelligence solution with Microsoft Fabric"APL-3009 "Implement a lakehouse in Microsoft Fabric"APL-3010 "Implement a data warehouse in Microsoft Fabric"Please note: This course is not affiliated with, endorsed by, or sponsored by Microsoft.What do students like you say about this course?Kin says: "Thank you so much for this content, very helpful. I love the flow of how you explain things starting from the most simple scenario and add complexity gradually, and how you anticipate common questions and address them. I learned more from this than the official Microsoft instructors I got from my workplace.Very well done!"David says: "Philip takes time to explain things on a basic level so a novice to the topic can start building knowledge from scratch, following it up by in-depth explanations and details. The practice activities are also a very nice touch!"Warren says: "This course is absolutely phenomenal in preparation for the DP-700 but also learning if you have zero experience. Microsoft Fabric is quite a large platform, and the exam can be very broad. Phillip does an outstanding job of covering an incredible amount of content in an easy-to-understand manner. The practice activities are well thought out and actually quite helpful in applying what is being taught.There are other resources out there, but this tops them all. Phillip is also incredibly responsive to any questions out there. I cannot understand why anyone would not give this course 5 stars."What will you learn in this course?Following a quick look around Fabric, we will look at using Dataflow Gen2 and pipelines - ingesting and copying data, and scheduling and monitoring data pipeline runs.Next we'll manipulate data using PySpark and SQL in a notebook.We'll have a look at loading and saving data using notebooks.We'll then manipulating dataframes, by choosing which columns and rows to show.We'll then convert data types, aggregating and sorting dataframes,We will then be transforming data in a lakehouse, merging and joining data, together with identifying missing data or null values.We will then be improving notebook performance and automate notebooks, together with creating objects, such as shortcuts and file partitioning.Following this, we'll look at using a data warehouse - transforming data, creating an incremental data load, and managing and optimizing them.We'll then create an eventhouse, and find out how to transform data using KQL:We'll select, filter and aggregate data.We'll manipulate data using string, number, datetime and timespan functions.We'll end these sections by transforming data, merging and joining data and more.Finally, we will look at ingesting and transforming streaming data, including revising KQL knowledge from the DP-600 exam, workspace settings and monitoring.No prior knowledge is assumed. We will start from the beginning for all languages and items, although any prior knowledge of PySpark, SQL or KQL is useful.Once you have completed the course, you will have a good knowledge of using notebooks to manipulate data using PySpark. And with some practice and knowledge of some additional topics, you could even go for the official Microsoft certification DP-700 - wouldn't the "Microsoft Certified: Fabric Data Engineer Associate" certification look good on your CV or resume?I hope to see you in the course - why not have a look at what you could learn?
