Runaway Complexity in Big Data Systems and a Plan to Stop It

Runaway Complexity in Big Data Systems and a Plan to Stop It

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Runaway Complexity in Big Data Systems and a Plan to Stop It

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  1. 1 Introduction
  2. 2 What is a data system
  3. 3 Common sources of complexity
  4. 4 Human fault tolerance
  5. 5 Design for human error
  6. 6 Data loss
  7. 7 Mutability
  8. 8 Immutability
  9. 9 Normalization vs Denormalization
  10. 10 Denormalization
  11. 11 Schemas
  12. 12 Schemas are bad
  13. 13 Schemas are confusing
  14. 14 What is a schema
  15. 15 What is structural integrity
  16. 16 Preventing corruption
  17. 17 Detecting corruption
  18. 18 Preventing mistakes
  19. 19 Learning from experience
  20. 20 Why schemas are painful
  21. 21 My ideal schema tool
  22. 22 Apache Thrift
  23. 23 New Sequel
  24. 24 No Sequel
  25. 25 How would you build a better data system
  26. 26 What do we actually use data systems for
  27. 27 Data Systems
  28. 28 Example
  29. 29 Realtime Queries
  30. 30 Pre Computation
  31. 31 Pre Computation Example
  32. 32 Architecture
  33. 33 Functions
  34. 34 View
  35. 35 Batch Processing
  36. 36 MapReduce
  37. 37 BatchView Databases
  38. 38 BatchView Properties
  39. 39 BatchView Architecture
  40. 40 Batch Computation
  41. 41 RealTime Views
  42. 42 Lambda Architecture
  43. 43 Cap Theorem
  44. 44 Eventually Accurate
  45. 45 Maximizing Value
  46. 46 Tools
  47. 47 Land Architecture
  48. 48 Movement Mistakes
  49. 49 Normalization Personalization
  50. 50 The Future
  51. 51 Book
  52. 52 Performance

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