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www.scling.com
The right side of speed -
learning to shift left
Lars Albertsson, Scling
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www.scling.com
Left is up
2
Winston W Royce:
"Managing the development
of large software systems"

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www.scling.com
Speed vs X tradeoff?
security
compliance
Speed or quality
operations
tech debt
3

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www.scling.com
Enterprise culture - friction as a service
analyst analyst
architect architect
developer developer
risk manager vs risk manager
security officer security officer
tester tester
ops engineer ops engineer
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www.scling.com
Is there a tradeoff?
Separation pitches teams and interests against each other
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www.scling.com
Ghost of shifts past
70s - 90s: reign of waterfall
90s: flood of IT failures
dot com crash
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www.scling.com
Extreme programming
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www.scling.com
A ladder to climb
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Test certified program
Google ~2006

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www.scling.com
Agile
9

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www.scling.com
Revenue models or role models
10Credits: @johncutlefish, @henrikkniberg

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www.scling.com
DevOps
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www.scling.com
No speed tradeoff!
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www.scling.com
Collaboration is
13
people
workflows
coupling / decoupling
not
tools

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www.scling.com
The most disruptive tech changes collaboration
RDBMS
version control
GNU + Linux
git + Github
containers
cloud
Hadoop
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www.scling.com
Big data
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www.scling.com
Hadoop
file storage
batch processing
single dimension database
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www.scling.com
Big data workflows
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
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www.scling.com
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
democratised data
minimal operations
minimal risk deployments
reproducibility
Big data workflows
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www.scling.com
Big data workflows
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
democratised data
minimal operations
minimal risk deployments
reproducibility
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Real value of big data.
Not achieved by late adopters.

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www.scling.com
Ghost of shifts present
*Ops all the things
20

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www.scling.com
DataOps
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www.scling.com
Enterprise culture big data
Hadoop / Spark / Flink
+
traditional workflows
mutable data
microservices
functional teams
heterogeneous data platform
=
worst of two worlds
22

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www.scling.com
Enterprise culture DevOps
Kubernetes / cloud
+
test in staging
handover deployment
no prod access
(Dev)Ops team
...
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www.scling.com
How to shift left
common goals
same definition of success
incentives, not gateways
common tools & environments
common processes
common teams
24

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www.scling.com
Craft - improving artifacts
25AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG

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www.scling.com
Factory - improving processes
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Lotus final assembly, Brian Snelson, https://www.flickr.com/photos/32659528@N00/2868525496
AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG

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www.scling.com
1994: OS/2 Warp CID installation
27
Grmbl, who
reinstalled my
machine?

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www.scling.com
IT craft to factory
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Lotus final assembly, Brian Snelson, https://www.flickr.com/photos/32659528@N00/2868525496
AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG
Security Waterfall
Application
delivery
Traditional
operations
Traditional
QA
Infrastructure
DevSecOps Agile
Containers
DevOps CI/CD
Infrastructure
as code

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www.scling.com
Security Waterfall
IT craft to factory
29
Application
delivery
Traditional
operations
Traditional
QA
Infrastructure
DB-oriented
architecture
DevSecOps Agile
Containers
DevOps CI/CD
Infrastructure
as code
Data factories,
data pipelines,
DataOps
Lotus final assembly, Brian Snelson, https://www.flickr.com/photos/32659528@N00/2868525496
AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG

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www.scling.com
DevSecOps
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SECURITY

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www.scling.com
DevSecOps by induction
validate security of system
make secure easy
validate security of changes
ignore perfection - focus on delta
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www.scling.com
Ghost of shifts yet to come
machine learning
security
compliance
are still at ends with speed
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www.scling.com
MLOps
33
DATA
SCIENCE

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www.scling.com
Naive ML
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www.scling.com
Towards sustainable production ML
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Multiple models,
parameters, features
Assess ingress data quality
Repair broken data from
complementary source
Choose model and parameters based
on performance and input data
Benchmark models
Try multiple models,
measure, A/B test

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www.scling.com
ComplianceOps
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COMPLIANCE

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www.scling.com
ComplianceOps by induction
validate compliance of system
make compliant easy
validate compliance of change
ignore perfection - focus on delta
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www.scling.com
long game
very long
ladders
recipes
people & teams
craft to process
focus on delta
Doing the shift
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www.scling.com
Doing the shift
long game
very long
ladders
recipes
people & teams
craft to process
focus on delta
learn faster by collaboration
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www.scling.com
Data value = data + domain expertise + data practices
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Disrupt?
https://xkcd.com/1831/
Adapt?
+ 1000s of failures...

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www.scling.com
Data value = data + domain expertise + data practices
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Disrupt?
https://xkcd.com/1831/
Adapt?
+ 1000s of failures...

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www.scling.com
Data value = data + domain expertise + data practices
42
Data lake
Stream storage
Client data +
domain expertise
Practices from
data leaders
Disrupt?
https://xkcd.com/1831/
Collaborate?
Data-value-as-a-service
Adapt?
+ 1000s of failures...

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www.scling.com
The ghost of Winston W. Royce (1929-1995)
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www.scling.com
Tech has massive impact on society
44
Product?
Supplier?
Employer?
Make an active
choice whether to
have an impact!
Cloud?