DevOps.com

  • Latest
    • Articles
    • Features
    • Most Read
    • News
    • News Releases
  • Topics
    • AI
    • Continuous Delivery
    • Continuous Testing
    • Cloud
    • Culture
    • DevSecOps
    • Enterprise DevOps
    • Leadership Suite
    • DevOps Practice
    • ROELBOB
    • DevOps Toolbox
    • IT as Code
  • Videos/Podcasts
    • DevOps Chats
    • DevOps Unbound
  • Webinars
    • Upcoming
    • On-Demand Webinars
  • Library
  • Events
    • Upcoming Events
    • On-Demand Events
  • Sponsored Communities
    • AWS Community Hub
    • CloudBees
    • IT as Code
    • Rocket on DevOps.com
    • Traceable on DevOps.com
    • Quali on DevOps.com
  • Related Sites
    • Techstrong Group
    • Container Journal
    • Security Boulevard
    • Techstrong Research
    • DevOps Chat
    • DevOps Dozen
    • DevOps TV
    • Digital Anarchist
  • Media Kit
  • About
  • AI
  • Cloud
  • Continuous Delivery
  • Continuous Testing
  • DevSecOps
  • DevOps Onramp
  • Practices
  • ROELBOB
  • Low-Code/No-Code
  • IT as Code
  • More
    • Application Performance Management/Monitoring
    • Culture
    • Enterprise DevOps

Home » Editorial Calendar » Cognitive Analytics: ‘Operations Thinking’ for Development

Cognitive Analytics: ‘Operations Thinking’ for Development

By: contributor on September 21, 2016 Leave a Comment

In a recent blog post by my colleague Payal Chakravarty, “Synthetic Monitoring – the Start of the DevOps Monitoring Journey,” she discussed how developers, testers and operations staff all need to ensure their internet and intranet mobile applications and web applications are tested and operate successfully from different points of presence around the world.

Recent Posts By contributor
  • How to Ensure DevOps Success in a Distributed Network Environment
  • Dissecting the Role of QA Engineers and Developers in Functional Testing
  • DevOps Primer: Using Vagrant with AWS
More from contributor
Related Posts
  • Cognitive Analytics: ‘Operations Thinking’ for Development
  • Performance Monitoring, another view
  • MLOps Vs. DevOps: What’s the Difference?
    Related Categories
  • Application Performance Management/Monitoring
  • Editorial Calendar
    Related Topics
  • apm
  • application performance monitoring
  • devops
  • ibm
Show more
Show less

Continuing our five-part series on “Continuous Monitoring: The Role of DevOps and APM,” this blog post will talk about how analytics can help developers earlier in the DevOps life cycle think about the performance of their applications.

Continuous monitoring is not just about detecting operational problems in production; it is about getting feedback and reassurance that code changes you’ve made have resulted in the intended operational behavior before you deploy to production.

My career DNA is development. So I know, full well, how code changes can have unintended consequences.

Remember the bad old days when development methodologies relied on a series of quality checks (code reviews, various types of tests and release validation) to catch unintended consequences? If caught at all, most operational degradations surfaced long after the offending code was submitted, because the types of validation designed to catch these problems occurred at the very end of the development life cycle, when a “stability test” was possible. These “banana skin” issues were caught late or not caught at all. Either way, clients were impacted (through release delays, increased costs or service degradation) and you were not happy.

Continuous development was great for me, personally. It forced me to look at development in a whole new light and solve the “little and often” delivery problems. It was fun, but despite best efforts, one “banana skin” remained: the stability tests in the staging environment. Just like before, it could cause a late-breaking wobble in the delivery plan to production or, worse still, issues were missed altogether.

I didn’t solve this problem well because I was still thinking like a developer—mainly because I don’t have the skills, time or the interest to think like Operations.

If cognitive analytics are applied to operation problems, you don’t need to think like Operations to benefit from Operations thinking. Your solution automatically will learn what is normal for your application; it will set dynamic thresholds and it will proactively notify you when it becomes significantly anomalous. With cognitive solutions, you can enable operations (continuous monitoring) in staging and detect anomalies long before the stability test fails or before the application hits production! All you need to do is feed it the application metrics. The good news is, your buddies in operations can show you how to do it.

I am a positive person, so I am going to use a positive development scenario:

You have made a code change to improve the performance of your application. When the code is deployed into staging, the expected operational improvement should occur (for example, a response time metric should change for the better). If you are using cognitive solutions, this anomaly (new and unusual behavior) will be detected automatically and you will be informed.

  • You get the reassurance that your code changes are having the intended positive impact on the operation of the service in staging.
  • You can inform your Operations team that new, anomalous behavior will be seen and why this is actually a good thing.
  • Furthermore, since these changes are intentional and the solution is fully cognitive, it will learn the new “normal” over time and the anomaly will simply go away. You do not need to take any “Operations action” such as setting a new threshold level, etc.

The reverse is true, too. If you did not expect a change in a response time metric, or any of the other operational metrics, this would surface quickly, in staging, well before a test (if it exists) catches it. You rapidly can investigate this and take the necessary action before the “banana skin” moment.

Another type of “Operations thinking” that is easily enabled in staging is the ability to alert on patterns in log files.

This feature looks for patterns in logs, in real time, as files are ingested. You create the alerts. You can include alerts for the type of symptoms Operations looks for (again, talk to your buddies—they are the experts) or better still, you know the symptoms of your application starting to fail (you have investigated and fixed enough bugs!). What if you could use congitive solutions to look for these patterns continuously and proactively send you an e-mail if one emerges? Wouldn’t that be good?

Monitoring solutions have turned a corner. Before cognitive, monitoring solutions relied on Operations SMEs to carefully manage the environment. With cognitive solutions the SME is built-in, allowing developers to shift left “Operations thinking” into staging and focus on what they want to do and do best: code, code, code …

Watch a replay of the recently hosted webinar, “Learn Why We Must Shift APM Left in the DevOps Lifecycle.”

About the Author / Sinead Glynn

bp

Sinead Glynn is an offering manager for IBM’s IT Service Management portfolio within DevOps.
Within the IT Service Management portfolio, she specializes in Operations Analytics offerings and the value they bring to both IT Operations and DevOps teams. In her 10+ years at IBM, Sinead has worked in both Development Management and Offering Management, covering both Network Performance Management and Operations Analytics-type solutions. Connect with Sinead on Twitter / LinkedIn.

Filed Under: Application Performance Management/Monitoring, Editorial Calendar Tagged With: apm, application performance monitoring, devops, ibm

Sponsored Content
Featured eBook
The State of Open Source Vulnerabilities 2020

The State of Open Source Vulnerabilities 2020

Open source components have become an integral part of today’s software applications — it’s impossible to keep up with the hectic pace of release cycles without them. As open source usage continues to grow, so does the number of eyes focused on open source security research, resulting in a record-breaking ... Read More
« Identity Crisis
Digital Transformation Top of Mind at PagerDuty Summit 2016 »

TechStrong TV – Live

Click full-screen to enable volume control
Watch latest episodes and shows

Upcoming Webinars

Bring Your Mission-Critical Data to Your Cloud Apps and Analytics
Tuesday, August 16, 2022 - 11:00 am EDT
Mistakes You Are Probably Making in Kubernetes
Tuesday, August 16, 2022 - 1:00 pm EDT
Taking Your SRE Team to the Next Level
Tuesday, August 16, 2022 - 3:00 pm EDT

Latest from DevOps.com

Techstrong TV: Scratching the Surface of Testing Through AI
August 12, 2022 | Alan Shimel
Next-Level Tech: DevOps Meets CSOps
August 12, 2022 | Jonathan Rende
The Benefits of a Distributed Cloud
August 12, 2022 | Jonathan Seelig
Cycode Expands Scope of AppDev Security Platform
August 11, 2022 | Mike Vizard
Techstrong TV: The Use of AI in Low-Code
August 11, 2022 | Charlene O'Hanlon

Get The Top Stories of the Week

  • View DevOps.com Privacy Policy
  • This field is for validation purposes and should be left unchanged.

Download Free eBook

The Automated Enterprise
The Automated Enterprise

Most Read on DevOps.com

MLOps Vs. DevOps: What’s the Difference?
August 10, 2022 | Gilad David Maayan
CREST Defines Quality Verification Standard for AppSec Testi...
August 9, 2022 | Mike Vizard
We Must Kill ‘Dinosaur’ JavaScript | Microsoft Open Sources ...
August 11, 2022 | Richi Jennings
Leverage Empirical Data to Avoid DevOps Burnout
August 8, 2022 | Bill Doerrfeld
GitHub Brings 2FA to JavaScript Package Manager
August 9, 2022 | Mike Vizard

On-Demand Webinars

DevOps.com Webinar ReplaysDevOps.com Webinar Replays
  • Home
  • About DevOps.com
  • Meet our Authors
  • Write for DevOps.com
  • Media Kit
  • Sponsor Info
  • Copyright
  • TOS
  • Privacy Policy

Powered by Techstrong Group, Inc.

© 2022 ·Techstrong Group, Inc.All rights reserved.