Data Engineering Best Practices: Architecture tools and techniques for the data analytics lifecycle (English Edition)
暫譯: 數據工程最佳實踐：數據分析生命周期的架構工具與技術（英文版）

Ramanna, Chandan, F. Dos Santos, Luiz Fernando

Data Engineering Best Practices: Architecture tools and techniques for the data analytics lifecycle (English Edition)
  • 出版商: BPB Publications
  • 出版日期: 2026-01-30
  • 售價: $1,630
  • 貴賓價: 9.5 折 $1,548
  • 語言: 英文
  • 頁數: 356
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9365894611
  • ISBN-13: 9789365894615
  • 相關分類: Data-visualization

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

Data engineering is the backbone of modern business intelligence, yet navigating the complexities of roles and tools can be challenging for new and experienced professionals alike. However, data engineering sits at the core of modern analytics. As organizations scale their use of data, they need robust architecture, reliable pipelines, and strong governance to turn raw data into trusted insights.

This book follows the journey of data from source to insight. It defines the data engineering role, presents reference architectures, and explains how to model, secure, and govern data for analytics. Subsequent chapters cover CI/CD, ETL versus ELT, infrastructure operations, data quality, operations, AI, and supporting processes.

By the end of this book, the readers will possess the competency to build, design, and operate end-to-end data platforms, collaborate effectively with analysts and data scientists, and apply repeatable patterns to build secure, scalable, and high-quality data solutions.

What you will learn

● Grasp the core responsibilities of modern data engineers.

● Design practical analytics and data platform architectures.

● Model data for performance, clarity, and governance.

● Secure, test, and automate pipelines with CI/CD.

● Design agnostic models and analyze topologies.

● Apply data operations to analytics, AI, and daily operations.

Who this book is for

This book is designed for data engineers, analysts, BI developers, and scientists building analytics platforms and pipelines, and it also guides the professionals responsible for data strategy, governance, and reliable data-driven decisions.

Table of Contents

1. Data Engineering's Role

2. Reference Architectures

3. Data Models

4. Permission Management

5. Governance and Cataloguing

6. Continuous Integration and Deployment

7. ETL and ELT

8. Infrastructure Operations

9. Quality Assurance

10. DataOps and AI

11. Additional Processes

12. Popular Technologies

商品描述(中文翻譯)

資料工程是現代商業智慧的基石，但對於新手和經驗豐富的專業人士來說，導航角色和工具的複雜性可能會很具挑戰性。然而，資料工程位於現代分析的核心。隨著組織擴大對資料的使用，他們需要穩健的架構、可靠的管道和強大的治理，以將原始資料轉化為可信的見解。

本書追蹤資料從來源到見解的旅程。它定義了資料工程師的角色，呈現參考架構，並解釋如何為分析建模、保護和治理資料。隨後的章節涵蓋持續整合/持續部署（CI/CD）、ETL 與 ELT、基礎設施運營、資料品質、運營、人工智慧（AI）及支援流程。

在本書結束時，讀者將具備建立、設計和運營端到端資料平台的能力，能夠與分析師和資料科學家有效合作，並應用可重複的模式來構建安全、可擴展和高品質的資料解決方案。

您將學到的內容：
● 理解現代資料工程師的核心責任。
● 設計實用的分析和資料平台架構。
● 為性能、清晰度和治理建模資料。
● 使用 CI/CD 來保護、測試和自動化管道。
● 設計無關模型並分析拓撲。
● 將資料操作應用於分析、人工智慧和日常運營。

本書的對象：
本書旨在為資料工程師、分析師、商業智慧（BI）開發人員和構建分析平台及管道的科學家而設，並指導負責資料策略、治理和可靠資料驅動決策的專業人士。

目錄：
1. 資料工程的角色
2. 參考架構
3. 資料模型
4. 權限管理
5. 治理與目錄管理
6. 持續整合與持續部署
7. ETL 與 ELT
8. 基礎設施運營
9. 品質保證
10. DataOps 與人工智慧
11. 附加流程
12. 流行技術

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