Full-Stack Development and Application of Python Big Data Architecture

Name: Full-Stack Development and Application of Python Big Data Architecture
ISBN: 9787121453038

宋天龙 / 张伟松Publishing House of Electronics Industry2023

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Word count: 607 words
Chapters: 13
ISBN: 9787121453038

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Summary

This book explains how to use Python for full-stack development, design, and programming of enterprise-grade big data and artificial intelligence. It is organized around the actual workflow of enterprise data work: starting from overall data architecture design, it first explains the eight considerations of architecture design (applicability, extensibility, security, ease of use, high performance, cost, application requirements, operations and maintenance management) and four core elements (physical architecture, logical architecture, technical architecture, and data flow architecture), and traces the evolution from simple database architecture, traditional data warehouses, and traditional big data to streaming big data, stream-batch unified processing, and storage-compute separated stream-batch unified processing; it then covers data sources and data collection, data synchronization, message queues, relational databases, NoSQL databases, batch processing, stream processing, graph computing, and artificial intelligence and data product development. Each chapter follows a path of "principles—technology selection—hands-on code—case study": it explains the basic principles of technical components, compares the suitability of different solutions in different scenarios and gives selection conclusions, demonstrates installation and configuration, basic examples, and advanced usage with Python code, and ties the implementation process together with real business cases (B2B enterprise architecture selection, B2C e-commerce data source structure, user profile tag construction, real-time trending topics, social behavior analysis, recommendation system construction and evolution, internal tag products, and more). The book also includes numerous "Common Questions" sections and two appendices on Docker and EMR cluster setup, so readers can learn by doing as they read.

Editor's review

市面上的 Python 书大多停留在语法、框架与入门示例，读者容易「会写代码，但不会做架构」。这本书的定位正相反：它站在高级开发者与架构师的视角，先问场景和需求是什么、哪种组件最合适、如何在性能、稳定性、开发效率、运维便利性与成本之间取得平衡，再落到代码。三个突出特点：一是「选型意识」贯穿全书，几乎每个技术领域都给出多套方案对比表和选型因素清单，读者可以照着这些清单在自己的项目里做决策；二是细节到位，从版本建议、安装路径到命令原文、代码与运行结果都有交代，适合当作案头工作手册；三是案例完整，除第1章外每章都配综合性案例，覆盖 B2B、B2C、O2O、传统零售、内容平台等业态，另有大量场景化小案例，弥补了数据类图书「概念多、实例少」的通病。如果你已经能写 Python，却在大数据项目里说不清为什么这样选型，这本书能帮你补齐从组件到架构的那一段认知。

Who it's for

A good fit for

已掌握 Python 基础语法、希望进阶到架构设计层面的开发者
数据仓库、数据平台、数据开发方向的工程师与 DBA
需要为团队做技术选型与架构决策的技术负责人、数据架构师
数据分析师、业务分析师等希望自建数据处理链路的数据运营人员
高年级本科生与研究生，用于数据工程方向的项目实践参考

Not a good fit for

零编程基础的读者，书中不系统讲解 Python 语法入门
只想学某个单一框架（如仅学 Spark）而不关心整体架构的读者
寻找纯理论教材或学术研究方法的读者，本书偏工程实践

Reading tips

第一次通读建议按章节顺序进行，因为内容依企业数据工作流程编排，后文会引用前文概念。
已有完整数据链路经验的读者可按需跳读，但注意相同内容不在各章重复，遇到陌生术语需回翻前章。
每章读完立刻照做安装配置与基本示例，把「常见问题」小节当作自查清单使用。
动手前先按附录A配置Docker、按附录B搭建集群环境，可避免后续章节反复卡在环境问题上。

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

Chapter 1 Overall Data Architecture Design
Chapter 2 Data Sources and Data Collection
Chapter 3 Data Synchronization
Chapter 4 Message Queues
Chapter 5 Relational Databases
Chapter 6 NoSQL Databases
Chapter 7 Batch Processing
Chapter 8 Stream Processing
Chapter 9 Graph Computing
Chapter 10 Artificial Intelligence
Chapter 11 Data Product Development
Appendix A Docker Installation and Use
Appendix B Building a Hadoop Big Data Cluster with EMR

FAQ

这本书需要什么前置基础？

需要具备 Python 基本语法与常用库的使用经验，了解数据库和 SQL 更好。书中不讲解 Python 入门，重点在架构设计、组件原理、选型与工程落地，零基础读者建议先补一门 Python 基础课。

书中的技术栈会不会已经过时？

本书 2023 年出版，主体覆盖 Hadoop、Hive、Spark（含 Structured Streaming）、Flink、Kafka、Redis、Elasticsearch、Neo4j、MongoDB、PySpark ML、Django 等组件。这些组件的核心概念与选型逻辑依然成立，但具体版本与安装步骤建议结合官方文档核对。

这本书和一般的 Python 数据分析书有什么区别？

一般数据分析书聚焦 pandas、可视化与统计建模；本书聚焦企业级数据链路的全栈工程，包括采集、同步、消息队列、存储、批处理、流处理、图计算、机器学习与数据产品开发，更接近数据平台架构视角。

全书页数与篇幅如何？

电子工业出版社书目信息显示全书约 607 千字、430 余页，16 开本，共 11 章加两个附录，属于偏厚的工具型专著，适合分主题逐步推进而非一次读完。

书中案例是虚构的还是真实的？

案例基于 B2B、B2C 电商、菜谱网站、内容平台等典型业态的真实业务场景展开，含架构图与代码，但由于商业保密，企业名称做了脱敏处理，重点在于呈现可复用的方法论与实现路径。

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About the author

宋天龙 Author

Public information about Song Tianlong is limited, and his nationality, dates of birth and death, and educational background cannot be confirmed. Judging from the three works held in the collection, his writing centers on computer technology fields such as big data, artificial intelligence, and programming design, with particular attention to the practical application of AIGC technologies. His representative works include AIGC-Assisted Data Analysis and Mining: Methods and Practice Based on ChatGPT, AIGC-Assisted Data Analysis and Data-Driven Operations: Scenario-Based Solutions and Case Studies, and Full-Stack Development and Application of Python Big Data Architecture. These works show that he excels at combining generative tools such as ChatGPT with data analysis, data-driven operations, and full-stack development, pairing methodological explanations with scenario-based cases for technical staff and business readers seeking to use AI to improve data work efficiency. Overall, his writing leans toward practical application and scenario-based solutions and offers some reference value in the categories of general computing and artificial intelligence.

张伟松 Author · China

Zhang Weisong is a Chinese author of books on programming and design. Publicly available information about him is limited, and his detailed biography and educational background cannot be confirmed. One of his works is held in the collection: Full-Stack Development and Application of Python Big Data Architecture. The book falls under the category of programming and design and centers on the Python technology stack, covering full-stack development and practical application in big data architecture. Judging from its subject matter, the author focuses on the complete chain of using Python to build data systems and put engineering into practice, with an emphasis on hands-on operation and an overall grasp of architecture. It is suitable for readers who wish to move from development toward architecture. Given the limited available material, his writing style and industry influence are difficult to assess comprehensively, and readers may refer to the book's specific chapters to understand his technical focus and approach.