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 |  موضوع: كتاب 97 Things Every Data Engineer Should Know الثلاثاء 22 نوفمبر 2022, 4:47 am | |
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أخواني في الله
أحضرت لكم كتاب
97 Things Every Data Engineer Should Know
Tobias Macey
و المحتوى كما يلي :
Table of Contents
Preface xiii
1. A (Book) Case for Eventual Consistency . 1
Denise Koessler Gosnell, PhD
2. A/B and How to Be . 3
Sonia Mehta
3. About the Storage Layer . 5
Julien Le Dem
4. Analytics as the Secret Glue for Microservice
Architectures 7
Elias Nema
5. Automate Your Infrastructure . 9
Christiano Anderson
6. Automate Your Pipeline Tests 11
Tom White
7. Be Intentional About the Batching Model in Your
Data Pipelines 13
Raghotham Murthy
8. Beware of Silver-Bullet Syndrome . 17
Thomas Nield
iii9. Building a Career as a Data Engineer 19
Vijay Kiran
10. Business Dashboards for Data Pipelines 21
Valliappa (Lak) Lakshmanan
11. Caution: Data Science Projects Can Turn into the
Emperor’s New Clothes . 23
Shweta Katre
12. Change Data Capture 26
Raghotham Murthy
13. Column Names as Contracts 28
Emily Riederer
14. Consensual, Privacy-Aware Data Collection 30
Katharine Jarmul
15. Cultivate Good Working Relationships with Data
Consumers 32
Ido Shlomo
16. Data Engineering != Spark 34
Jesse Anderson
17. Data Engineering for Autonomy and Rapid
Innovation . 36
Jeff Magnusson
18. Data Engineering from a Data Scientist’s Perspective . 38
Bill Franks
19. Data Pipeline Design Patterns for Reusability and
Extensibility . 40
Mukul Sood
20. Data Quality for Data Engineers 42
Katharine Jarmul
iv Table of Contents21. Data Security for Data Engineers 44
Katharine Jarmul
22. Data Validation Is More Than Summary Statistics 46
Emily Riederer
23. Data Warehouses Are the Past, Present, and Future 48
James Densmore
24. Defining and Managing Messages in Log-Centric
Architectures . 50
Boris Lublinsky
25. Demystify the Source and Illuminate the
Data Pipeline 52
Meghan Kwartler
26. Develop Communities, Not Just Code . 54
Emily Riederer
27. Effective Data Engineering in the Cloud World 56
Dipti Borkar
28. Embrace the Data Lake Architecture 58
Vinoth Chandar
29. Embracing Data Silos 61
Bin Fan and Amelia Wong
30. Engineering Reproducible Data Science Projects 63
Dr. Tianhui Michael Li
31. Five Best Practices for Stable Data Processing 65
Christian Lauer
32. Focus on Maintainability and Break Up Those
ETL Tasks . 67
Chris Moradi
Table of Contents v33. Friends Don’t Let Friends Do Dual-Writes 69
Gunnar Morling
34. Fundamental Knowledge 71
Pedro Marcelino
35. Getting the “Structured” Back into SQL . 73
Elias Nema
36. Give Data Products a Frontend with Latent
Documentation . 76
Emily Riederer
37. How Data Pipelines Evolve 78
Chris Heinzmann
38. How to Build Your Data Platform like a Product . 80
Barr Moses and Atul Gupte
39. How to Prevent a Data Mutiny 83
Sean Knapp
40. Know the Value per Byte of Your Data 85
Dhruba Borthakur
41. Know Your Latencies 87
Dhruba Borthakur
42. Learn to Use a NoSQL Database, but Not like
an RDBMS . 89
Kirk Kirkconnell
43. Let the Robots Enforce the Rules 91
Anthony Burdi
44. Listen to Your Users—but Not Too Much 93
Amanda Tomlinson
45. Low-Cost Sensors and the Quality of Data . 95
Dr. Shivanand Prabhoolall Guness
vi Table of Contents46. Maintain Your Mechanical Sympathy 97
Tobias Macey
47. Metadata ≥ Data . 99
Jonathan Seidman
48. Metadata Services as a Core Component of the Data
Platform 101
Lohit VijayaRenu
49. Mind the Gap: Your Data Lake Provides No ACID
Guarantees . 103
Einat Orr
50. Modern Metadata for the Modern Data Stack . 105
Prukalpa Sankar
51. Most Data Problems Are Not Big Data Problems . 107
Thomas Nield
52. Moving from Software Engineering to Data
Engineering 109
John Salinas
53. Observability for Data Engineers . 111
Barr Moses
54. Perfect Is the Enemy of Good . 114
Bob Haffner
55. Pipe Dreams . 116
Scott Haines
56. Preventing the Data Lake Abyss 118
Scott Haines
57. Prioritizing User Experience in Messaging Systems 120
Jowanza Joseph
Table of Contents vii58. Privacy Is Your Problem 122
Stephen Bailey, PhD
59. QA and All Its Sexiness . 124
Sonia Mehta
60. Seven Things Data Engineers Need to Watch Out for
in ML Projects . 126
Dr. Sandeep Uttamchandani
61. Six Dimensions for Picking an Analytical Data
Warehouse . 128
Gleb Mezhanskiy
62. Small Files in a Big Data World 131
Adi Polak
63. Streaming Is Different from Batch 134
Dean Wampler, PhD
64. Tardy Data . 136
Ariel Shaqed
65. Tech Should Take a Back Seat for Data Project
Success . 138
Andrew Stevenson
66. Ten Must-Ask Questions for Data-Engineering
Projects 140
Haidar Hadi
67. The Data Pipeline Is Not About Speed . 143
Rustem Feyzkhanov
68. The Dos and Don’ts of Data Engineering 145
Christopher Bergh
69. The End of ETL as We Know It 148
Paul Singman
viii Table of Contents70. The Haiku Approach to Writing Software 151
Mitch Seymour
71. The Hidden Cost of Data Input/Output 153
Lohit VijayaRenu
72. The Holy War Between Proprietary and Open Source
Is a Lie 155
Paige Roberts
73. The Implications of the CAP Theorem 157
Paul Doran
74. The Importance of Data Lineage 159
Julien Le Dem
75. The Many Meanings of Missingness . 161
Emily Riederer
76. The Six Words That Will Destroy Your Career 163
Bartosz Mikulski
77. The Three Invaluable Benefits of Open Source for
Testing Data Quality 165
Tom Baeyens
78. The Three Rs of Data Engineering 167
Tobias Macey
79. The Two Types of Data Engineering and
Data Engineers 169
Jesse Anderson
80. The Yin and Yang of Big Data Scalability 171
Paul Brebner
81. Threading and Concurrency in Data Processing 173
Matthew Housley, PhD
Table of Contents ix82. Three Important Distributed Programming Concepts 175
Adi Polak
83. Time (Semantics) Won’t Wait . 177
Marta Paes Moreira and Fabian Hueske
84. Tools Don’t Matter, Patterns and Practices Do 179
Bas Geerdink
85. Total Opportunity Cost of Ownership 181
Joe Reis
86. Understanding the Ways Different Data Domains
Solve Problems 183
Matthew Seal
87. What Is a Data Engineer? Clue: We’re Data Science
Enablers 185
Lewis Gavin
88. What Is a Data Mesh, and How Not to Mesh It Up 187
Barr Moses and Lior Gavish
89. What Is Big Data? . 189
Ami Levin
90. What to Do When You Don’t Get Any Credit . 191
Jesse Anderson
91. When Our Data Science Team Didn’t Produce Value 193
Joel Nantais
92. When to Avoid the Naive Approach 195
Nimrod Parasol
93. When to Be Cautious About Sharing Data . 197
Thomas Nield
94. When to Talk and When to Listen 199
Steven Finkelstein
x Table of Contents95. Why Data Science Teams Need Generalists, Not
Specialists 201
Eric Colson
96. With Great Data Comes Great Responsibility . 203
Lohit VijayaRenu
97. Your Data Tests Failed! Now What? 205
Sam Bail, PhD
Contributors . 207
Index
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