Dimensional modeling and the star schema are some of the most important ideas in the history of analytics and data management. They provided a common language and set of patterns that allowed a broad class of users to analyze business processes and spawned an entire ecosystem. With the rise of enterprise data hubs that allow us to combine ETL, search, SQL, and machine learning in a single platform, we need to extend the principles of dimensional modeling to support new and diverse analytical workloads and users. We'll illustrate these concepts by walking through the design of a customer-centric data hub that uses all of the components of an EDH to enable everyone to understand the way that customers experience a company. Presenter: Josh Wills, Senior Director Data Science Updated: October 6, 2014

1. 1
What Comes After the
Star Schema?
Dimensional Modeling for Enterprise Data Hubs
Josh Wills
Senior Director of Data Science

2. About Me
2

3. Two Fish
3

4. 4
Once Upon A Time…

5. Our data looked like this…
5

6. …but the data analyst wanted this.
6

7. The Impedance Mismatch
7

8. The Unicorn Solution
8

9. The Great Compromise
9

10. Ease of Use
10

11. Division of Labor
11

12. Given Away Freely
12

13. The Central Idea of an Entire Industry
13

14. Modern BI and Reporting
14

15. 15
The Next Hard Problem

16. Operationalizing Insights
16

17. The Analytics Maturity Curve
17

18. Example: Modeling Risk
18

19. The Impedance Mismatch, Redux
19

20. The Unicorn Solution, Redux
20

21. This All Feels Very Familiar
21

22. It’s Time To Find A New Compromise
22

23. Data Science 101
23

24. 24
Introducing Supernovas

25. A Simple Star Schema for Search
25

26. The Cartesian Explosion
26

27. A Supernova Schema for Search
27

28. Beyond Analytic SQL Functions:
Nested SQL Sessions
28

29. Event Series Analytics
29

30. http://github.com/jwills/exhibit
30

31. 31
Supernova for
BI and Reporting

32. A New Kind of Data Model
32
Context/Backend RDBMS NoSQL
Operational Entity-Relational Generic Objects
Analytical Star Supernova

33. From Star To Supernova
33

34. Supernova 101: Affinity Analysis
34

35. Supernova Design Rules
1. Identify the root dimension.
2. Identify the facts about the root dimension that we
want to analyze.
3. De-normalize critical child dimensions.
4. Define the time window.
35

36. Creating a Supernova
36

37. Injecting Child Dimensions
37

38. Taking Advantage of Multi-Insert
38

39. 39
Supernova for
Operational Analytics

40. A New Kind of Data Model
40
Context/Backend RDBMS NoSQL
Operational Entity-Relational Generic Objects
Analytical Star Supernova

41. Data Science and the Holy Grail
41

42. Replicating the Online Environment
42

43. Feature Engineering: Within / Across
1. Generate normalization and segmentation
features.
2. Generate normalization constants and segments
using the result of Step 1.
3. Generate input features using the original data
and the result of Step 2.
4. Generate a model using the result of Step 3.
43

44. Integrating New Signals
44

45. Building a Wall
45

46. Driving Innovation
46

47. Thank you!
Josh Wills, Senior Director of Data Science, Cloudera @josh_wills