Fast Data Architectures for Streaming Applications

Fast Data Architectures for Streaming Applications

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Intro

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1 of 47

Intro

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Fast Data Architectures for Streaming Applications

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  1. 1 Intro
  2. 2 Riley Report
  3. 3 Context
  4. 4 Hadoop Classic
  5. 5 Hadoop Architecture
  6. 6 Time vs Money
  7. 7 Streaming Data Architecture
  8. 8 Streaming Engines
  9. 9 Latency Requirements
  10. 10 RealTime
  11. 11 Latency
  12. 12 Windowing
  13. 13 Machine Learning
  14. 14 Batch Jobs
  15. 15 Volume
  16. 16 HighScale Problems
  17. 17 Partitioning Data
  18. 18 Integration
  19. 19 Sequels
  20. 20 Spark vs Kafka
  21. 21 Dynamic ML
  22. 22 Event Processing
  23. 23 Individual Event Processing
  24. 24 Google Stream Processing
  25. 25 Apache Beam
  26. 26 Stream Processing
  27. 27 Flink
  28. 28 akkaStreams
  29. 29 KafkaStreams
  30. 30 Spark
  31. 31 Lambda
  32. 32 Microservices and Fast Data
  33. 33 Microservices
  34. 34 Single Responsibility
  35. 35 Micro Services
  36. 36 Reactive Programming
  37. 37 Twitter
  38. 38 Big Data vs Microservices
  39. 39 Light Ben
  40. 40 Merging streams
  41. 41 Runner
  42. 42 Storm
  43. 43 Knife
  44. 44 Beam
  45. 45 Legacy Data
  46. 46 Batch vs Stream
  47. 47 Sharing Models

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