In this webinar we will discuss Looker’s novel approach to data modeling and how it powers a data exploration environment with unprecedented depth and agility. Some topics we will cover: -A new architecture beyond direct connect -Language-based, git-integrated data modeling -Abstractions that make SQL more powerful and more efficient

1. Beyond Data Discovery
The Value Unlocked by Modern Data Modeling
November 5, 2015
Presented
by

2. — This webinar will be recorded
— We will take questions at the end
Welcome & Webinar Overview

3. Zach Taylor
Product Manager
Talal Assir
Senior Data Analyst
Meet our presenters

4. Data Modeling
Transforming data
into understanding
is hard

5. Agenda
1
1 Concepts
2 Live Modeling
3 NYC Taxi Cab Data

6. Less on this...

7. More on this...

8. Traditional Approach
Restricted Q&AELT - Heavy Transformation
END USERBI TEAMETL TEAM EDW TEAM
A B
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Z
P
OLAP / Data Summaries
SUMMARY

9. Build Incrementally

10. Reusability
Content Creator
Model Builder
Consumer Complexity
Tradeoff

11. ● Get it to people
quickly
● Crowdsource
● Have a shared view
Enable Learning

12. The Alternative
● Feedback loop is
broken
● Data brawls
● Reusability is lost

13. ● Modular
● Interconnected
● Light architecture
Fast Iteration Cycles
Fast Iteration Cycles

14. Make Change Easy
measure: daily_usage_minutes
sql: SUM(daily_usage.approximate_usage_in_minutes)
measure: approximate_usage_in_minutes
type: sum
sql: ${TABLE}.approximate_usage_in_minutes
dimension: license_health
sql: |
CASE
WHEN ${approximate_usage_in_minutes} = 0 THEN 0
ELSE (
(CASE
WHEN ${approximate_usage_in_minutes} < 400 THEN 0
WHEN ${approximate_usage_in_minutes} < 1000 THEN 10
ELSE 25 END)
1
3
2

15. Build a Model

16. Check out NYC Taxi data

17. Building a Model
Trip
Driver ID
Pickup Time
Dropoff Time
Fare Amount

18. Building a Model
Trip
Driver ID
Pickup Time
Dropoff Time
Fare Amount
Next Pickup Time
Idle Distance
Idle Time

19. Building a Model
Trip
Driver ID
Pickup Time
Dropoff Time
Fare Amount
Next Pickup Time
Idle Distance
Idle Time
Shift
Start Time
End Time
Utilization

20. Building a Model
Trip
Driver ID
Pickup Time
Dropoff Time
Fare Amount
Next Pickup Time
Idle Distance
Idle Time
Shift
Start Time
End Time
Utilization
Driver
Driver ID
Lifetime trips
Average shift length
Average trips/shift

21. Q &
A

22. Thank you
• This webinar will be posted on
looker.com tomorrow.

23. Thank you