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Spark 2.0

Spark 2.0

Scala Days Conferences via YouTube Direct link

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Classroom Contents

Spark 2.0

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  1. 1 Intro
  2. 2 What is Apache Spark?
  3. 3 A Large Community
  4. 4 Apache Spark Users
  5. 5 Original Spark Vision
  6. 6 Motivation: Unification
  7. 7 Motivation: Concise API
  8. 8 How Did the Vision Hold Up?
  9. 9 Libraries Built on Spark
  10. 10 Which Libraries Do People Use?
  11. 11 Top Applications
  12. 12 Main Challenge: Functional API
  13. 13 Which API Call Causes Most Tickets?
  14. 14 Example Problem
  15. 15 Challenge: Data Representation
  16. 16 Why Structure?
  17. 17 DataFrames and Datasets
  18. 18 Execution Steps
  19. 19 DataFrame API
  20. 20 Why DataFrames?
  21. 21 What Structured APIs Enable
  22. 22 Performance
  23. 23 Dataset API Details
  24. 24 Data Sources
  25. 25 Data Source API
  26. 26 Examples
  27. 27 Hardware Trends
  28. 28 Project Tungsten
  29. 29 Tungsten's Compact Encoding
  30. 30 Space Efficiency
  31. 31 Runtime Code Generation
  32. 32 Long-Term Vision
  33. 33 Versioning in Spark
  34. 34 Major Features in 2.0
  35. 35 Background
  36. 36 Structured Streaming High-level streaming API built on DataFrames/Datasets
  37. 37 Structured Streaming API
  38. 38 Example: Batch Aggregation
  39. 39 Example: Continuous Aggregation
  40. 40 Incrementalized By Spark
  41. 41 Release Timeline
  42. 42 Conclusion
  43. 43 Want to Learn Apache Spark?

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