Get Started with Databricks for Data Warehousing - Mandarin Chinese

在本课程中，您将通过动手演示和综合实验室，浏览 Unity Catalog 并了解 Databricks 上的细粒度访问控制。您将学习表类型、目录和架构配置、基于组的访问管理以及访问控制迁移策略。本课程包含以下演示：应用基于行级安全和列掩码的细粒度访问控制、基于属性的访问控制、组合控制、迁移控制，以及一个用于全面治理实施的实验室。

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Databricks Performance Optimization 课程教导数据工程师和分析师如何在 Databricks Data Intelligence Platform 上诊断、衡量和修复性能瓶颈，以及如何将性能提升与成本关联起来。本课程遵循"先衡量、先平台"的工作流：让平台的自动优化功能承担繁重工作，通过 Query Profile 和系统表验证已应用的优化，再在必要时进行手动调优。

学员将以 Query Profile、Performance Insights 和查询历史记录系统表为基础，学习如何衡量性能。在此基础上，他们将探索关键优化技术，包括数据布局与自调优托管表、liquid clustering、缓存与中间结果、shuffle、数据倾斜、溢出、行爆炸、驱动程序性能、Python UDF、serverless compute、Photon 以及成本归因。

在整个课程中，学员将针对合成零售数据中刻意设计的慢查询进行探索，观察不同优化技术对性能的影响。他们将利用文件裁剪、任务执行时间、Photon 覆盖率和成本等依据来评估优化效果。两个基于场景的实验室将强化从诊断到验证的完整优化工作流。

注意：Databricks Academy 正在将 Databricks 环境中的课堂课程转为基于 notebook 的形式，并停止在讲座中使用幻灯片。您可以在 Vocareum 实验环境中访问课程 notebook。

Databricks Performance Optimization teaches data engineers and analysts how to diagnose, measure, and fix performance bottlenecks on the Databricks Data Intelligence Platform, and how to tie performance improvements back to cost. The course follows a measurement-first, platform-first workflow: let the platform's automatic optimizations do the heavy lifting, verify what was applied using Query Profile and system tables, then manually tune where needed.

Learners build a foundation for measuring performance with Query Profile, Performance Insights, and query history system tables. From there, they explore key optimization techniques, including data layout and self-tuning managed tables, liquid clustering, caching and intermediate results, shuffle, skew, spill, row explosions, driver performance, Python UDFs, serverless compute, Photon, and cost attribution.

Throughout the course, learners explore deliberately slow queries over synthetic retail data and see how different optimization techniques affect performance. They use evidence such as file pruning, task execution time, Photon coverage, and cost to evaluate improvements. Two scenario-based labs reinforce the optimization workflow from diagnosis through validation.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.