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數(shù)據(jù)工程基礎(chǔ)（影印版）
數(shù)據(jù)工程基礎(chǔ)（影印版）
Joe Reis, Matt Housley 著
出版時(shí)間：2023年03月
頁數(shù)：422
“數(shù)據(jù)世界已經(jīng)演變了有一段時(shí)間。首先是設(shè)計(jì)師，然后是數(shù)據(jù)庫管理員，接著是首席信息官和數(shù)據(jù)架構(gòu)師。本書標(biāo)志著該行業(yè)演變和成熟的下一步。對(duì)那些忠于自己職業(yè)和事業(yè)的人來說，這是一本必讀之書。”
——Bill Inmon
數(shù)據(jù)倉庫的創(chuàng)建人
“本書很好地介紹了數(shù)據(jù)移動(dòng)、處理和操作。我強(qiáng)烈將它推薦給任何想要快速掌握數(shù)據(jù)工程或分析的讀者，或者想要填補(bǔ)理解上的空白的現(xiàn)有從業(yè)者?！?br /> ——Jordan Tigani
MotherDuck 創(chuàng)始人兼首席執(zhí)行官，
BigQuery的創(chuàng)始工程師和共同創(chuàng)始人

數(shù)據(jù)工程在過去十年間發(fā)展迅速，許多軟件工程師、數(shù)據(jù)科學(xué)家和分析師都在尋找相關(guān)實(shí)踐的全面觀點(diǎn)。通過這本實(shí)踐用書，你將學(xué)習(xí)如何通過評(píng)估數(shù)據(jù)工程生命周期框架中可用的最佳技術(shù)來規(guī)劃和構(gòu)建系統(tǒng)，以滿足你的組織和客戶的需求。
作者Joe Reis和Matt Housley將為你介紹數(shù)據(jù)工程的生命周期，向你展示如何綜合運(yùn)用各種云技術(shù)，以滿足下游數(shù)據(jù)消費(fèi)者的需求。你將理解如何應(yīng)用數(shù)據(jù)生成、攝取、編排、轉(zhuǎn)換、存儲(chǔ)和治理的概念，無論底層技術(shù)是什么，這些概念在任何數(shù)據(jù)環(huán)境中都至關(guān)重要。
本書將幫助你：
● 簡要了解整個(gè)數(shù)據(jù)工程領(lǐng)域
● 使用端到端的最佳實(shí)踐框架評(píng)估數(shù)據(jù)工程問題
● 在選擇數(shù)據(jù)技術(shù)、架構(gòu)和流程時(shí)避開市場營銷炒作
● 使用數(shù)據(jù)工程生命周期來設(shè)計(jì)和構(gòu)建穩(wěn)健的架構(gòu)
● 在數(shù)據(jù)工程生命周期中融入數(shù)據(jù)治理和安全性
  1. Preface
  2. Part I. Foundation and Building Blocks
  3. 1. Data Engineering Described
  4. What Is Data Engineering?
  5. Data Engineering Skills and Activities
  6. Data Engineers Inside an Organization
  7. Conclusion
  8. Additional Resources
  9. 2. The Data Engineering Lifecycle
  10. What Is the Data Engineering Lifecycle?
  11. Major Undercurrents Across the Data Engineering Lifecycle
  12. Conclusion
  13. Additional Resources
  14. 3. Designing Good Data Architecture
  15. What Is Data Architecture?
  16. Principles of Good Data Architecture
  17. Major Architecture Concepts
  18. Examples and Types of Data Architecture
  19. Who’s Involved with Designing a Data Architecture?
  20. Conclusion
  21. Additional Resources
  22. 4. Choosing Technologies Across the Data Engineering Lifecycle
  23. Team Size and Capabilities
  24. Speed to Market
  25. Interoperability
  26. Cost Optimization and Business Value
  27. Today Versus the Future: Immutable Versus Transitory Technologies
  28. Location
  29. Build Versus Buy
  30. Monolith Versus Modular
  31. Serverless Versus Servers
  32. Optimization, Performance, and the Benchmark Wars
  33. Undercurrents and Their Impacts on Choosing Technologies
  34. Conclusion
  35. Additional Resources
  36. Part II. The Data Engineering Lifecycle in Depth
  37. 5. Data Generation in Source Systems
  38. Sources of Data: How Is Data Created?
  39. Source Systems: Main Ideas
  40. Source System Practical Details
  41. Whom You’ll Work With
  42. Undercurrents and Their Impact on Source Systems
  43. Conclusion
  44. Additional Resources
  45. 6. Storage
  46. Raw Ingredients of Data Storage
  47. Data Storage Systems
  48. Data Engineering Storage Abstractions
  49. Big Ideas and Trends in Storage
  50. Whom You’ll Work With
  51. Undercurrents
  52. Conclusion
  53. Additional Resources
  54. 7. Ingestion
  55. What Is Data Ingestion?
  56. Key Engineering Considerations for the Ingestion Phase
  57. Batch Ingestion Considerations
  58. Message and Stream Ingestion Considerations
  59. Ways to Ingest Data
  60. Whom You’ll Work With
  61. Undercurrents
  62. Conclusion
  63. Additional Resources
  64. 8. Queries, Modeling, and Transformation
  65. Queries
  66. Data Modeling
  67. Transformations
  68. Whom You’ll Work With
  69. Undercurrents
  70. Conclusion
  71. Additional Resources
  72. 9. Serving Data for Analytics, Machine Learning, and Reverse ETL
  73. General Considerations for Serving Data
  74. Analytics
  75. Machine Learning
  76. What a Data Engineer Should Know About ML
  77. Ways to Serve Data for Analytics and ML
  78. Reverse ETL
  79. Whom You’ll Work With
  80. Undercurrents
  81. Conclusion
  82. Additional Resources
  83. Part III. Security, Privacy, and the Future of Data Engineering
  84. 10. Security and Privacy
  85. People
  86. Processes
  87. Technology
  88. Conclusion
  89. Additional Resources
  90. 11. The Future of Data Engineering
  91. The Data Engineering Lifecycle Isn’t Going Away
  92. The Decline of Complexity and the Rise of Easy-to-Use Data Tools
  93. The Cloud-Scale Data OS and Improved Interoperability
  94. “Enterprisey” Data Engineering
  95. Titles and Responsibilities Will Morph...
  96. Moving Beyond the Modern Data Stack, Toward the Live Data Stack
  97. Conclusion
  98. A. Serialization and Compression Technical Details
  99. B. Cloud Networking
  100. Index
書名：數(shù)據(jù)工程基礎(chǔ)（影印版）
作者：Joe Reis, Matt Housley 著
國內(nèi)出版社：東南大學(xué)出版社
出版時(shí)間：2023年03月
頁數(shù)：422
書號(hào)：978-7-5766-0551-8
原版書書名：Fundamentals of Data Engineering
原版書出版商：O'Reilly Media
Joe Reis
 
Joe Reis是一名“恢復(fù)中的數(shù)據(jù)科學(xué)家”，也是一名數(shù)據(jù)工程師和架構(gòu)師。
 
 
Matt Housley
 
Matt Housley是一名數(shù)據(jù)工程顧問和云專家。
 
 
The animal on the cover of Fundamentals of Data Engineering is the white-eared puffbird (Nystalus chacuru).
So named for the conspicuous patch of white at their ears, as well as for their fluffy plumage, these small, rotund birds are found across a wide swath of central South America, where they inhabit forest edges and savanna.
White-eared puffbirds are sit-and-wait hunters, perching in open spaces for long periods and feeding opportunistically on insects, lizards, and even small mammals that happen to come near. They are most often found alone or in pairs and are relatively quiet birds, vocalizing only rarely.
The International Union for Conservation of Nature has listed the white-eared puffbird as being of least concern, due, in part, to their extensive range and stable population。
購買選項(xiàng)
定價(jià)：136.00元
書號(hào)：978-7-5766-0551-8
出版社：東南大學(xué)出版社