By Baruch Sadogursky
Devops is usually viewed from a traditional perspective of a collaboration of Dev, Ops and QA, driven by the change in Culture, People and Process. But how do you know where you stand and were to move? As in almost any field, data and metrics give you the gauges and instruments. In this talk we’ll talk about the key measurements for the DevOps transformation process and provide you with 3 metrics you can start measuring tomorrow.
Data driven devops as presented at QCon London 2018Baruch Sadogursky
Devops is usually viewed from a traditional perspective of a collaboration of Dev, Ops, and QA, driven by the change in Culture, People, and Process. But how do you know where you stand and where to move? As in almost any field, data and metrics give you the gauges and instruments. In this talk, we’ll talk about the key measurements for the DevOps transformation process and provide you with 3 metrics you can start measuring tomorrow.
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I will talk about my experiences from the last 15 years, including small software delivery teams until big financial institutions.
Why would a company like to be "agile"?
How can a company achieve that?
How can you achieve Technical Excellence in your software teams?
What developer skills are more important than languages, methods or frameworks?
This will be an interactive session with a Q&A at the end.
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DevОps is usually viewed from a traditional perspective of a collaboration of Dev, Ops, and QA, driven by the change in Culture, People, and Process. But how do you know where you stand and where to move? As in almost any field, data and metrics give you the gauges and instruments. In this talk, we’ll talk about the key measurements for the DevOps transformation process and provide you with 3 metrics you can start measuring tomorrow.
Data driven devops as presented at QCon London 2018Baruch Sadogursky
Devops is usually viewed from a traditional perspective of a collaboration of Dev, Ops, and QA, driven by the change in Culture, People, and Process. But how do you know where you stand and where to move? As in almost any field, data and metrics give you the gauges and instruments. In this talk, we’ll talk about the key measurements for the DevOps transformation process and provide you with 3 metrics you can start measuring tomorrow.
Achieving Technical Excellence in Your Software Teams - from Devternity Peter Gfader
Our industry has a problem: We are not lacking software methodologies, programming languages, tools or frameworks but we need great software engineers.
Great software engineer teams build quality-in and deliver great software on a regular basis. The technical excellence of those engineers will help you escape the "Waterfall sandwich" and make your organization a little more agile, from the inception of an idea till they go live.
I will talk about my experiences from the last 15 years, including small software delivery teams until big financial institutions.
Why would a company like to be "agile"?
How can a company achieve that?
How can you achieve Technical Excellence in your software teams?
What developer skills are more important than languages, methods or frameworks?
This will be an interactive session with a Q&A at the end.
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DevOps is the Answer... What was the question again? DevOps and Digital Trans...DevOpsGroup
DevOps is the Answer... What was the question again? DevOps and Digital Transformation was the Keynote speech at DevOps Pro Vilnius in May 2016 and it discussed the reasons why organisations are facing digital disruption and why devOps is a key part of your Digital Transformation strategy
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Does the size of the pull request matter? If yes, what should be its ideal size? In this talk, I will explain why it is important to be concerned with the size of the pull requests and what should be taken into account to understand what is the ideal size. We will look at the impact on the quality and speed of development that the size of the pull requests can cause, understand the costs associated with working with large or small PRs, and show the relationship with the queueing theory.
DevОps is usually viewed from a traditional perspective of a collaboration of Dev, Ops, and QA, driven by the change in Culture, People, and Process. But how do you know where you stand and where to move? As in almost any field, data and metrics give you the gauges and instruments. In this talk, we’ll talk about the key measurements for the DevOps transformation process and provide you with 3 metrics you can start measuring tomorrow.
A Research Study into DevOps Bottlenecks as presented at Codemash 2018Baruch Sadogursky
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We asked the Fortune 500 software delivery leaders what holds them back. This talk is the analysis of their insights on what bottlenecks they encountered in their DevOps journey.
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TechSEO Boost 2017: Fun with Machine Learning: How Machine Learning is Shapin...Catalyst
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Patterns and antipatterns in Docker image lifecycle @ DevOpsDays Charlotte 2017Baruch Sadogursky
While Docker has enabled an unprecedented velocity of software production, it is all too easy to spin out of control. A promotion-based model is required to control and track the flow of Docker images as much as it is required for a traditional software development lifecycle. New tools often introduce new paradigms. We will examine the patterns and the antipatterns for Docker image management, and what impact the new tools have on the battle-proven paradigms of the software development lifecycle.
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So, you want to update the software for your user, be it the nodes in your K8s cluster, a browser on user’s desktop, an app in user’s smartphone or even a user’s car. What can possibly go wrong?
In this talk, we’ll analyze real-world software update fails and how multiple DevOps patterns, that fit a variety of scenarios, could have saved the developers. Manually making sure that everything works before sending an update and expecting the user to do acceptance tests before they update is most definitely not on the list of such patterns.
Join us for some awesome and scary continuous update horror stories and some obvious (and some not so obvious) proven ideas for improvement and best practices you can start following tomorrow.
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5. Poll time!
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
How many of you are software engineers?
How many of you are optimists?
How many of you are self-confident in their work?
6. Dunning-Kruger Effect a.k.a. ”optimism”
People suffer from illusory superiority,
mistakenly assessing their cognitive ability as
greater than it is.
Wikipedia
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
“
7. Second-system effect a.k.a. “self-confidence”
The tendency of small, elegant, and
successful systems, to be succeeded by over-
engineered, bloated systems, due to inflated
expectations and overconfidence.
The Mythical Man-Month
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
“
21. Velocity to agile is like ? To devops…
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
?
22. Velocity to agile is like NPS To devops…
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
NPS
23. Also, it’s a lot like profits
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Metric Easily
understandable
Unity actionable
Profit
✓ ✓ ✗
Velocity
✓ ✓ ✗
NPS
✓ ✓ ✗
24. Also, it’s a lot like profits
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Metric Easily
understandable
Unity actionable
Profit
✓ ✓ ✗
Velocity
✓ ✓ ✗
NPS
✓ ✓ ✗
25. Also, it’s a lot like profits
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Metric Easily
understandable
Unity actionable
Profit
✓ ✓ ✗
Velocity
✓ ✓ ✗
NPS
✓ ✓ ✗
26. Poll time!
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Who knows what velocity is?
Who knows what burndown chart is?
Who has a burndown chart?
Who looks at the burndown chart?
Who trusts the burndown chart?
Who knows what to do if it doesn't look right?
31. This is devops*
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Ops
QADev
Common goals,
tools, culture
Deep
specialization
*Unless you’re netfliX
**You’re not
35. How do metrics collaborate?
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
dev ops qa
dev • Time to build
• Release pipelines
stability
• Artifact replication
topology
• False reopen count
• QA coverage
ops • Avg cost of customer • Smoke tests quality
qa • Test suite stability
• Incoming defect
velocity
• Test execution time
• Time to create new
QA lab
influenced
affected
36. How do metrics collaborate?
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
dev ops qa
dev • Time to build
• Release pipelines
stability
• Artifact replication
topology
• False reopen count
• QA coverage
ops • Avg cost of customer • Smoke tests quality
qa • Test suite stability
• Incoming defect
velocity
• Test execution time
• Time to create new
QA lab
influenced
affected
37. How do metrics collaborate?
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
dev ops qa
dev • Time to build
• Release pipelines
stability
• Artifact replication
topology
• False reopen count
• QA coverage
ops • Avg cost of customer • Smoke tests quality
qa • Test suite stability
• Incoming defect
velocity
• Test execution time
• Time to create new
QA lab
influenced
affected
38. How do metrics collaborate?
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
dev ops qa
dev • Time to build
• Release pipelines
stability
• Artifact replication
topology
• False reopen count
• QA coverage
ops • Avg cost of customer • Smoke tests quality
qa • Test suite stability
• Incoming defect
velocity
• Test execution time
• Time to create new
QA lab
influenced
affected
39. How do metrics collaborate?
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
dev ops qa
dev • Time to build
• Release pipelines
stability
• Artifact replication
topology
• False reopen count
• QA coverage
ops • Avg cost of customer • Smoke tests quality
qa • Test suite stability
• Incoming defect
velocity
• Test execution time
• Time to create new
QA lab
influenced
affected
40. How do metrics collaborate?
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
dev ops qa
dev • Time to build
• Release pipelines
stability
• Artifact replication
topology
• False reopen count
• QA coverage
ops • Avg cost of customer • Smoke tests quality
qa • Test suite stability
• Incoming defect
velocity
• Test execution time
• Time to create new
QA lab
influenced
affected
41. How do metrics collaborate?
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
dev ops qa
dev • Time to build
• Release pipelines
stability
• Artifact replication
topology
• False reopen count
• QA coverage
ops • Avg cost of customer • Smoke tests quality
qa • Test suite stability
• Incoming defect
velocity
• Test execution time
• Time to create new
QA lab
influenced
affected
52. How effective are we?
Samples are good enough for that
As long as they are representative
Need to be collected over time
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
55. Development affects operational costs
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Central container images repository
Developers keep pushing images
Storage prices skyrocket
Not all PoPs need all images
57. Development affects QA
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Anecdotes about test suite stability
shared at daily startup
Special issue type “test suite
stability”
59. Development influences Operations trust
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Operations want to predict costs and
be trustworthy
Will application updates generate
more load?
72. Step into data-driven life
@jbaruch www.jfrog.com/shownotes #codemash #datadrivendevops
Removes blame game
Builds accountability and trust
Creates common base for discussion