Collecting and Making Sense of Diverse Data at WayUp

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Harlan Harris

my 20-minute talk at DataEngConf 2017 in NYC, in the Startup track.

Dados e análise

Collecting and Making Sense of
Diverse Data at WayUp
Harlan D. Harris, PhD
Director of Data Science
DataEngConf 2017
Thanks to:
JJ Fliegelman (CTO)
WayUp Engineers!

Why we built WayUp...
The leading digital platform for
employers to reach, recruit, and
engage candidates in an authentic way.
… with the focus on college students and recent grads.
One of thirty innovative
companies changing the
world.
2

Talking about Choices
● Where we focus effort
○ Event Collection & Data Refinement
● Tech stack
○ Segment, Redshift, dbt, Periscope
● Warehouse table design
○ ELT, layers & abstractions
● We’re Hiring!
4

Why We Warehouse
Support Business Analytics and Product (Data Science)
● Clean, Normalized Tables
● Abstract over Changes in Systems
● Right Type of Domain Knowledge
6
Data Reflects
the World
Decisions & Products
Reflect the World

Tech Stack for Analytics
● Segment
● Amazon Redshift
● S3/Spectrum
● dbt
● Periscope +
targeted tools
7
Avoid Vendor Lock-in;
Design to Minimize
Downstream Impact

Event Tracking
● Heap approach
○ Developers don’t make choices
○ Automatically get every load and click
○ UI changes can lose continuity
● Traditional approach
○ Developers choose what to track
○ Can miss stuff -- requires communication!
○ Can keep semantic continuity across
changes
○ Less lock-in
8
“Actions with
Meaning”

Redshift and Spectrum
● Value of familiarity, broad support
● Sweet spot in scale, room to grow
● Spectrum
○ External tables on S3 CSV
○ Query and join like internal tables
○ Avoid or delay loading until needed
○ Use Transform tools to load
9

The ELT Pattern
● “Data Lake” in columnar database
● Piped in via Segment data loader
● Transform on-database vs. in-transit
● Requires compute power, but space is cheap
● Can be more agile, “schema on read”
10
“most data transformation use cases can be much more
effectively handled in-database rather than in some
external processing layer” -dbt

dbt
11
https://blog.fishtownanalytics.com/what-exactly-is-dbt-47ba57309068

Abstraction Layers
12
Raw
LookupStaging
Analytics
Reporting Product
(Spectrum)

$Dimension Tables and Activity Streams 13 hist_user now_userdim_user act_ user actor ts action object ob_type properties Alice Nov 2nd viewed sales-123 listing { pos: 3 } fact table with specific, consistent structure (see WeWork talk!)$

What We’ve Learned; Where We’re Going
● Pay close attention to
what you store, and
how you refine data
● Tools now are amazing
● Design with empathy
and creativity
14
● Grow it with the
business!
● Build insights &
products to help our
users and customers!

Thanks!
Data Scientist (Recommender Systems)
Data Engineer (this stuff!)
FS & BE Engineers (Python)
harlan@wayup.com, @harlanh
We’re Hiring!

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1. Collecting and Making Sense of Diverse Data at WayUp Harlan D. Harris, PhD Director of Data Science DataEngConf 2017 Thanks to: JJ Fliegelman (CTO) WayUp Engineers!

2. Why we built WayUp... The leading digital platform for employers to reach, recruit, and engage candidates in an authentic way. … with the focus on college students and recent grads. One of thirty innovative companies changing the world. 2

4. Talking about Choices ● Where we focus effort ○ Event Collection & Data Refinement ● Tech stack ○ Segment, Redshift, dbt, Periscope ● Warehouse table design ○ ELT, layers & abstractions ● We’re Hiring! 4

5. Data Sources 5

6. Why We Warehouse Support Business Analytics and Product (Data Science) ● Clean, Normalized Tables ● Abstract over Changes in Systems ● Right Type of Domain Knowledge 6 Data Reflects the World Decisions & Products Reflect the World

7. Tech Stack for Analytics ● Segment ● Amazon Redshift ● S3/Spectrum ● dbt ● Periscope + targeted tools 7 Avoid Vendor Lock-in; Design to Minimize Downstream Impact

8. Event Tracking ● Heap approach ○ Developers don’t make choices ○ Automatically get every load and click ○ UI changes can lose continuity ● Traditional approach ○ Developers choose what to track ○ Can miss stuff -- requires communication! ○ Can keep semantic continuity across changes ○ Less lock-in 8 “Actions with Meaning”

9. Redshift and Spectrum ● Value of familiarity, broad support ● Sweet spot in scale, room to grow ● Spectrum ○ External tables on S3 CSV ○ Query and join like internal tables ○ Avoid or delay loading until needed ○ Use Transform tools to load 9

10. The ELT Pattern ● “Data Lake” in columnar database ● Piped in via Segment data loader ● Transform on-database vs. in-transit ● Requires compute power, but space is cheap ● Can be more agile, “schema on read” 10 “most data transformation use cases can be much more effectively handled in-database rather than in some external processing layer” -dbt

11. dbt 11 https://blog.fishtownanalytics.com/what-exactly-is-dbt-47ba57309068

12. Abstraction Layers 12 Raw LookupStaging Analytics Reporting Product (Spectrum)

13. Dimension Tables and Activity Streams 13 hist_user now_userdim_user act_ user actor ts action object ob_type properties Alice Nov 2nd viewed sales-123 listing { pos: 3 } fact table with specific, consistent structure (see WeWork talk!)

14. What We’ve Learned; Where We’re Going ● Pay close attention to what you store, and how you refine data ● Tools now are amazing ● Design with empathy and creativity 14 ● Grow it with the business! ● Build insights & products to help our users and customers!

15. Thanks! Data Scientist (Recommender Systems) Data Engineer (this stuff!) FS & BE Engineers (Python) harlan@wayup.com, @harlanh We’re Hiring!

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