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The value of data
Performance & scalability challenges
@bol.com
@arjenderuiter
Slides with input from Niels Basjes & Edith Kanters
Topics
• Value of data
• Scalability &
performance
• Tips
Value: customers find what they are
looking for with search rank
Value: inspire customers with product
recommendations
Value: a personal shop full of inspiration
for every customer
Value: provide us with explicit feedback on
your personal shop
6
Everything data
CUSTOMER
Right
frequency
Right
Event
Right
content
Right place
Right
promotion
Right
timing
02. Arjen de Ruiter, Bol.com
02. Arjen de Ruiter, Bol.com
Sprints
StoriesValue
ValueStory points/scope
As stories burn down, value
increases
15 million products:
not Big Data
15 million clicks a day:
not Big Data
>3.5TB + >20TB> 10.000.000.000 product
combinations: getting bigger!
It does not fit anymore
It takes too long
Scalable
processing: it
al starts small
15
16
17
18
19
Big enough and cost effective?
20
<schaalbaarheid>
02. Arjen de Ruiter, Bol.com
Economic size
Different
models
02. Arjen de Ruiter, Bol.com
02. Arjen de Ruiter, Bol.com
25
Linear horizontal scalability:
start small, end big
02. Arjen de Ruiter, Bol.com
MTBHIAGSD
Value
MTBHIAGSD
@andrianco, Netflix
So deliver fast!
Launch early & iterate
One of the company
rules of thumb at
Google is: if you are
not embarrassed by
your first launch, you
have not launched early
enough
Trim the tail, don’t spend time on low
value features
Value
Time
Epic 3
Epic 2
Epic 1
Tail … does not add too much additional value
Measure & act.
Fail fast, fail cheap.
A/B testing to find the best variant of a solution
Our main Big Data conclusion
02. Arjen de Ruiter, Bol.com

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02. Arjen de Ruiter, Bol.com

Editor's Notes

  1. Obv 700 dagen Als je klein begint en hiermee wilt kunnen eindigen, dan stelt dat eisen aan de schaalbaarheid van je oplossing
  2. Bottleneck!
  3. Fail fast, fail cheap Incementeel: story burn-down, value built-up
  4. Voor jou (publiek) kan dit wel voldoen Voorbeeld: niet gedistribueerde RDBMS op een appliance
  5. Data groeit harder dan snelheid van machines, daarom meerdere machines naast elkaar dan 1 grotere machine (verticaal vs horizontaal schalen)
  6. In het eerste voorbeeld van Edith begin je met 1 machine In het tweede voorbeeld 2 machines Etc. Niet alleen platform, maar ook design & codering Als er 1 uitvalt nemen de anderen het werk over In theory eindig, in praktijk oneindig Hadoop en Hbase als voorbeeld noemen
  7. Gevolgen voor retail: Focus op lage kosten Veel producten en varianten Veel product prijzen Denk “en” niet “of” Veel distributie manieren Vele kleintjes boven één grote leverancier Kies meten boven voorspellen Laat klanten het werk doen Maak de tail steeds beter vindbaar