SlideShare a Scribd company logo
www.scling.com
The right side of speed -
learning to shift left
Lars Albertsson, Scling
1
www.scling.com
Left is up
2
Winston W Royce:
"Managing the development
of large software systems"
www.scling.com
Speed vs X tradeoff?
security
compliance
Speed or quality
operations
tech debt
3
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
4
www.scling.com
Is there a tradeoff?
Separation pitches teams and interests against each other
5
www.scling.com
Ghost of shifts past
70s - 90s: reign of waterfall
90s: flood of IT failures
dot com crash
6
www.scling.com
Extreme programming
7
www.scling.com
A ladder to climb
8
Test certified program
Google ~2006
www.scling.com
Agile
9
www.scling.com
Revenue models or role models
10Credits: @johncutlefish, @henrikkniberg
www.scling.com
DevOps
11
www.scling.com
No speed tradeoff!
12
www.scling.com
Collaboration is
13
people
workflows
coupling / decoupling
not
tools
www.scling.com
The most disruptive tech changes collaboration
RDBMS
version control
GNU + Linux
git + Github
containers
cloud
Hadoop
14
www.scling.com
Big data
15
www.scling.com
Hadoop
file storage
batch processing
single dimension database
16
www.scling.com
Big data workflows
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
17
www.scling.com
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
democratised data
minimal operations
minimal risk deployments
reproducibility
Big data workflows
18
www.scling.com
Big data workflows
immutable datasets
functional transform processing
data pipelines
homogeneous, coordinated workflows
democratised data
minimal operations
minimal risk deployments
reproducibility
19
Real value of big data.
Not achieved by late adopters.
www.scling.com
Ghost of shifts present
*Ops all the things
20
www.scling.com
DataOps
21
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
www.scling.com
Enterprise culture DevOps
Kubernetes / cloud
+
test in staging
handover deployment
no prod access
(Dev)Ops team
...
23
www.scling.com
How to shift left
common goals
same definition of success
incentives, not gateways
common tools & environments
common processes
common teams
24
www.scling.com
Craft - improving artifacts
25AMC Javelin, CZmarlin, https://commons.wikimedia.org/wiki/File:AMC_Javelin_1971-74_purple_blown_custom.JPG
www.scling.com
Factory - improving processes
26
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
www.scling.com
1994: OS/2 Warp CID installation
27
Grmbl, who
reinstalled my
machine?
www.scling.com
IT craft to factory
28
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
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
www.scling.com
DevSecOps
30
SECURITY
www.scling.com
DevSecOps by induction
validate security of system
make secure easy
validate security of changes
ignore perfection - focus on delta
31
www.scling.com
Ghost of shifts yet to come
machine learning
security
compliance
are still at ends with speed
32
www.scling.com
MLOps
33
DATA
SCIENCE
www.scling.com
Naive ML
34
www.scling.com
Towards sustainable production ML
35
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
www.scling.com
ComplianceOps
36
COMPLIANCE
www.scling.com
ComplianceOps by induction
validate compliance of system
make compliant easy
validate compliance of change
ignore perfection - focus on delta
37
www.scling.com
long game
very long
ladders
recipes
people & teams
craft to process
focus on delta
Doing the shift
38
www.scling.com
Doing the shift
long game
very long
ladders
recipes
people & teams
craft to process
focus on delta
learn faster by collaboration
39
www.scling.com
Data value = data + domain expertise + data practices
40
Disrupt?
https://xkcd.com/1831/
Adapt?
+ 1000s of failures...
www.scling.com
Data value = data + domain expertise + data practices
41
Disrupt?
https://xkcd.com/1831/
Adapt?
+ 1000s of failures...
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...
www.scling.com
The ghost of Winston W. Royce (1929-1995)
43
www.scling.com
Tech has massive impact on society
44
Product?
Supplier?
Employer?
Make an active
choice whether to
have an impact!
Cloud?

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