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عدد المساهمات : 18745 التقييم : 34763 تاريخ التسجيل : 01/07/2009 الدولة : مصر العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
| 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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