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所在平台: Udemy |
课程主页: https://www.udemy.com/course/duckdb-fast-effortless-analytics/
课程评论：没有评论
课程名称：掌握DuckDB：实用指南 课程概述： 《掌握DuckDB - 快速、轻量化的现代数据工作流分析工具》是一个专为数据分析师、数据工程师和数据科学家设计的课程，旨在帮助您深入了解DuckDB这一现代高性能SQL OLAP数据库。DuckDB支持快速的数据分析，可在Jupyter笔记本或Python脚本中无缝运行，具备零设置、零服务器及几乎即时的性能，正在革新我们与本地数据的互动方式。 课程内容： 本课程将从基础到高级用法全面覆盖DuckDB，您将学习到： - DuckDB简介：了解DuckDB及其受欢迎的原因，OLAP与OLTP的区别以及DuckDB在其中的定位。 - DuckDB与SQLite、Pandas、Postgres及大数据工具的比较。 - 在多个平台（Windows、Mac、Linux）上安装DuckDB。 - 通过CLI、Python、Jupyter和SQL使用DuckDB。 学习内容包括： - 使用DuckDB进行SQL操作：创建数据库、运行查询、数据过滤、聚合、分组、连接与子查询等。 - 直接查询数据文件，无需导入：从磁盘直接查询CSV和大Parquet文件，使用Apache Arrow集成，读写JSON、Excel等格式，利用通配符组合多个文件为一个虚拟表。 - DuckDB与Python的集成：在Python环境中设置DuckDB，直接对DataFrames运行SQL查询，无需数据转换，优化数据处理。 - Jupyter笔记本中的DuckDB：使用魔术命令快速进行SQL查询。 性能优化与最佳实践：解释矢量化执行和列存储，DuckDB与Pandas或SQL数据库的使用时机，性能调优及内存管理。 先进功能与企业特性：实施DuckLake进行企业级数据管理，历史分析的时间旅行查询，复杂数据转换的lambda函数，云数据湖管理等。 适合人群： 本课程适合所有与数据打交道并希望寻找更快、更简单分析工具的人士，包括数据分析师、数据科学家、Python开发者、数据工程师及初学者。无需有DuckDB前期经验，基本的SQL或Python知识将帮助您更好地学习。 为何学习DuckDB： DuckDB正在成为现代数据架构中必不可少的工具，具有零设置、高性能、嵌入式与可移植性、SQL驱动等优点。现在学习DuckDB将使您在越来越多公司和团队开始采用它进行本地优先分析时，走在前沿。 课程收益： - 6小时以上的视频讲座 - 可下载的笔记本和数据集 - 实践项目与练习 - 理解测试测验 - 完成证书 准备好掌握DuckDB了吗？通过本课程，您将在数据项目中自信地使用DuckDB，无论是探索数据文件、构建ETL管道，还是将SQL与Python结合进行快速分析。加入我们，体验DuckDB如何让您的数据工作变得更快、更简单、更有趣！
Description: Mastering DuckDB - Fast, Lightweight Analytics for Modern Data WorkflowsDuckDB is a modern, high-performance SQL OLAP database designed for lightning-fast analytics, yet lightweight enough to run entirely within your application, Jupyter notebook, or Python script. With zero setup, zero servers, and near-instant performance, DuckDB is revolutionizing how we interact with local data.Whether you're a data analyst exploring CSV files, a data engineer building ETL pipelines, or a data scientist running experiments on structured data - DuckDB will save you time, effort, and frustration. This course is your complete guide to mastering DuckDB from scratch, with hands-on exercises, real-world projects, and expert insights.What You Will LearnThis course is designed to take you from the basics to advanced use cases with DuckDB. Here's a detailed overview of what you'll gain:Introduction to DuckDBWhat is DuckDB and why is it gaining popularity?OLAP vs OLTP - and where DuckDB fits inHow DuckDB compares to SQLite, Pandas, Postgres, and big data toolsInstalling DuckDB across platforms (Windows, Mac, Linux)Using DuckDB via CLI, Python, Jupyter, and SQLGetting Started with SQL in DuckDBCreating databases and running queriesFiltering, aggregations, group by, joins, and subqueriesWindow functions, CTEs (Common Table Expressions), and date/time functionsCreating views and temporary tablesUsing SQL for data exploration, profiling, and reportingQuerying Data Files Directly (No Import Required!)Querying CSV files directly from disk with SQLWorking with large Parquet files - efficiently and fastIntegrating with Apache ArrowUsing DuckDB to read/write JSON, Excel, and other formatsCombining multiple files into a single virtual table using wildcardsDuckDB + Python IntegrationSetting up DuckDB in a Python environmentRunning SQL queries on DataFrames without conversionWriting SQL queries as part of your Python data pipelineEfficient data transformations without loops or apply()DuckDB in Jupyter NotebooksMagic commands for fast SQL in notebooksExploring datasets directly in notebooks using SQL + Python togetherIdeal workflow for data science projectsPerformance, Best Practices & OptimizationVectorized execution and columnar storage explainedWhen to use DuckDB vs Pandas or SQL databasesPerformance tuning: batching, lazy evaluation, efficient file accessMemory management and handling large datasetsAdvanced Capabilities:Implement DuckLake for enterprise-grade data managementPerform time travel queries for historical analysisBuild robust error handling with TRY expressionsUse lambda functions for complex data transformationsOptimize memory usage and query performanceEnterprise Features:Set up cloud-based data lakes with AWS S3 integrationManage data versioning and snapshotsImplement ACID transactions across multiple tablesMonitor and debug using metadata tablesDesign scalable data architecturesWho This Course is ForThis course is for anyone who works with data and is looking for a better, faster, and simpler tool for analytics:Data Analysts: Tired of slow CSV loads or limited Excel capabilities? DuckDB will transform the way you explore and analyze data.Data Scientists: Quickly explore, clean, and process data with SQL directly in your notebook.Python Developers: Use SQL without a full database backend, right inside your script or application.Data Engineers: Simplify your pipelines by removing unnecessary database dependencies and using DuckDB to process raw files.Students/Learners: If you're new to databases or SQL, this is a great entry point with modern tooling and hands-on projects.No prior experience with DuckDB is required. Basic familiarity with SQL or Python will be helpful, but we start from the ground up.Tools & Technologies CoveredDuckDB CLI and embedded usageDuckDB with Python & PandasDuckDB in Jupyter NotebookCSV, Parquet, Arrow, JSON handlingSQL (basic to advanced)Optional: Integration with Streamlit for dashboardsWhy Learn DuckDB?DuckDB is rapidly becoming a must-have tool in the modern data stack. Here's why:Zero Setup: No server, no deployment, just run it and go.High Performance: Easily handle millions of rows locally.Embedded & Portable: Run inside notebooks, scripts, or even desktop apps.SQL-Powered: Ideal for analysts and anyone who loves SQL.File-Native: Work directly with Parquet, CSV, and more - no database needed.Open Source & Evolving: Constantly improving and growing with the community.Learning DuckDB now puts you ahead of the curve, as more companies and teams start to adopt it for local-first, scalable analytics.What You'll Get6+ hours of video lecturesDownloadable notebooks and datasetsHands-on projects and exercisesQuizzes to test your understandingCertificate of completionReady to Master DuckDB?By the end of this course, you'll be confident using DuckDB in your data projects - whether you're exploring data files, building ETL pipelines, or combining SQL with Python for fast analytics.Join us and learn how DuckDB can make your data work faster, easier, and more fun.Let's dive in and make analytics delightful again - with DuckDB!
