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The right side of speed - learning to shift left

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Lars Albertsson
Lars Albertsson

Many disciplines are on the wrong side of speed - there is a tradeoff with development speed and security, data science, compliance, etc. Let us look at disciplines that have succeeded in shifting left by integrating development, and learn successful patterns: testing, DevOps, agile, DataOps.

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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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