~/talk

Improve your App UX with one model processing insights from batch and streams

Talkat Voxxed Days Ticino 2017 · on Saturday, 2017-05-06· [data-engineering, data-visualization]
linkedin:kubisrobert
slides:none on file

Shows a concrete pipeline that uses Apache Beam and the Cloud Dataflow runner with Pub/Sub input and BigQuery storage to run the same code for both batch and streaming. Implements a live demo game (four buttons, assigned color), processes ~41,000 events, windows at five minutes, emits early firings every 10 seconds, allows 10-minute late arrivals and uses accumulation for refinements. Deployment hits a pipeline-name collision but succeeds; batch job reuses identical transforms, proving one model handles correctness, latency and live visualization.

AI-written summary · about

  1. 0:00Introduction
  2. 1:04Steps to improve user experience
  3. 2:10History of data processing at Google
  4. 3:31What is MapReduce
  5. 4:10The problem with MapReduce
  6. 4:55From Java
  7. 5:43The problem
  8. 6:44Artificial splitting
  9. 7:15Un unbounded data
  10. 7:29Delays
  11. 8:08How to deal with delays
  12. 8:19MillVia
  13. 8:55Timebased windows
  14. 9:54Session windows
  15. 10:06Event vs processing time
  16. 11:16Stream vs Batch
  17. 11:41Billing Pipeline
  18. 12:53User Experience
  19. 13:47Abuse Detection
  20. 14:43Historical Systems
  21. 15:57Apache Beam
  22. 16:55Dataflow Example
  23. 17:44Four Questions
  24. 19:33MapReduce
  25. 22:48When to omit results
  26. 23:03Create a window
  27. 23:25Wait for results
  28. 23:58When to trigger
  29. 24:30Triggers in Beam
  30. 25:13Demo
  31. 26:30refinements
  32. 29:14how
  33. 32:36what just happened
  34. 33:19cancel pipeline
  35. 33:54run on
  36. 34:52update pipeline
  37. 35:27QR code
  38. 36:04Assign color
  39. 36:33Running the pipeline
  40. 38:10Patch pipeline
  41. 38:55BigQuery
  42. 39:18Color Smash
  43. 41:17Hit Ratio
  44. 41:31Aggregate
  45. 42:49Back to the slides
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