Databricks Performance Optimization - Mandarin Chinese

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。

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Professional

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Prerequisites

本课程的内容是为具备以下技能、知识和能力的学员开发的:

• 导航 Databricks workspace 并运行笔记本

• 编写中级 SQL，包括连接和聚合

• 使用 DataFrame 转换和操作编写 PySpark

• 使用 Delta Lake 表及 Unity Catalog 的核心概念，包括目录、架构、表和托管表

• 理解 Spark 执行的核心概念，包括分区、转换和惰性求值

• 基本了解 Lakeflow Jobs，包括调度和将笔记本工作负载作为作业运行

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Data Warehousing Practitioner

Get Started with Databricks for Data Warehousing - Mandarin Chinese

本课程提供了对 Databricks 现代数据仓库方法的全面实践概述，重点介绍数据湖仓架构如何将传统数据仓库的优势与云平台的灵活性和可扩展性相结合。通过与虚构零售商（Vintage Audio Co.）的端到端实践，您将浏览 workspace，查询受治理的 Unity Catalog 表，保持 Unity Catalog 托管表的分析读取高效性，使用 Auto Loader 增量加载数据，构建星型模式，并通过 Unity Catalog metric views 提供一致的洞察——所有这些均在单一受治理的环境中完成，并全程内置 AI 驱动功能（Predictive Optimization、Genie Code）。

Databricks Performance Optimization

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.

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Professional

Vibe Coding a Databricks App - Mandarin Chinese

Databricks Apps 让您能够直接在 Databricks 平台上构建和部署交互式数据与 AI 应用程序。但成功的 AI 辅助开发不仅仅是生成代码，更在于建立有效的工作流。

在本课程中，您将学习如何以 Genie Code 作为 AI 开发助手，从零开始构建一个基于 Lakebase 的 Databricks App。在此过程中，您将掌握一套实用且可复用的 AI 辅助开发方法：在生成代码之前清晰表达意图，以小步骤、可验证的方式推进工作，并将 AI 生成的输出视为需要审查和完善的内容，而非直接接受。

您将创建并部署一个 Databricks App，并使用 Genie Code 加速常见的开发任务。完成本课程后，您不仅将拥有一个可运行的应用程序，还将掌握一套在未来软件项目中与 AI 高效协作的实用框架。

Paid & Subscription

Lab

Professional