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Beyond Data Discovery The Value Unlocked by Modern Data Modeling
November 5, 2015
Presented by
— This webinar will be recorded
— We will take questions at the end
Welcome & Webinar Overview
Zach TaylorProduct Manager
Talal AssirSenior Data Analyst
Meet our presenters
Data Modeling
Transforming data into understanding
is hard
Agenda
11 Concepts
2 Live Modeling
3 NYC Taxi Cab Data
Less on this...
Traditional Approach
Restricted Q&AELT - Heavy Transformation
END USERBI TEAMETL TEAM EDW TEAM
A B
?C
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G
J
M
H
N
F
L
D
K
Q
BZ C
P
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S
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X
EB
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EVENT DATA
TRANSACTIONAL DATA
OLAP / Data Summaries
SUMMARY
Build Incrementally
Reusability
Content Creator
Model Builder
Consumer Complexity Tradeoff
● Get it to people quickly
● Crowdsource
● Have a shared view
Enable Learning
The Alternative
● Feedback loop is broken
● Data brawls
● Reusability is lost
● Modular
● Interconnected
● Light architecture
Fast Iteration Cycles
Fast Iteration Cycles
Make Change Easy
measure: daily_usage_minutessql: SUM(daily_usage.approximate_usage_in_minutes)
measure: approximate_usage_in_minutestype: sumsql: ${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)
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Build a Model
Check out NYC Taxi data
Building a Model
TripDriver IDPickup TimeDropoff TimeFare Amount
Building a Model
TripDriver IDPickup TimeDropoff TimeFare Amount
Next Pickup TimeIdle DistanceIdle Time
Building a Model
TripDriver IDPickup TimeDropoff TimeFare Amount
Next Pickup TimeIdle DistanceIdle Time
ShiftStart TimeEnd TimeUtilization
Building a Model
TripDriver IDPickup TimeDropoff TimeFare Amount
Next Pickup TimeIdle DistanceIdle Time
ShiftStart TimeEnd TimeUtilization
DriverDriver IDLifetime tripsAverage shift lengthAverage trips/shift
Q & A
Thank you
• This webinar will be posted on looker.com tomorrow.
Thank you
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