What’s the most important factor for enterprise DevOps enablement? Is it culture, is it automation or is it feedback?
Pretty much every large enterprise, at least those that realize the world is kind of in a state of change, is thinking about how to make their organization more agile. They’re also quickly reading Marc Andreessen’s famous Wall Street Journal piece from a few years ago, “Why Software Is Eating The World.”
Hopefully, they’re then putting these two themes, agility and innovation, through software together and deciding that key to remaining competitive is arming their technology teams with the tools, processes, freedoms and cultures to do good stuff.
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All of that can nicely be wrapped up by the DevOps approach—a seemingly simple approach that posits that the creation of software and the delivery of that software shouldn’t be two discrete tasks, but rather a continuous process with software developers having deeper understanding of operating paradigms, while IT operations folks have more of an appreciation of the nuances of software development.
For such a simple theme, DevOps sure creates a lot of debate with people arguing tirelessly about whether the most important thing in DevOps is organizational culture change or its really about the technicalities around IT operations. I’ve always sided with the proponents of the former theory, and I have been adamant that without a culture shift within an organization, no end of new tooling will make an ounce of difference to actual outcomes.
CloudMunch introduces its DevOps intelligence platform
So, I was interested to here from young DevOps vendor CloudMunch recently. The company is this week rolling out its Insights offering, a standalone DevOps intelligence platform.
According to Krish Subramanian, an executive with the company, while most of the talk about DevOps either focuses on culture or automation, CloudMunch’s take is that culture and automation are key elements for DevOps and helps deliver software faster. But you also need a continuous feedback loop layered on top of the delivery pipeline to deliver better software faster.
According to the company, the fact that much of the focus and effort in enterprise modernization is spent on automation is somewhat misguided. The company posits that organizations are getting suboptimal ROI in their DevOps adoption because siloed data across various tools leads to lack of visibility, metrics and performance.
CloudMunch wants to remediate that perceived issue with its platform, the new parts of which provide a single lens to view the different metrics across the software delivery lifecycle, a closed feedback loop of action/effect, and organizational wide visibility and governance over tools and processes.
Of course, almost every application or infrastructure monitoring company under the sun claims to provide this holistic, consistent and proactive approach toward monitoring. Anyone who is handy with Google search will realize there are tons of monitoring and visualization tools available in the market.
How does CloudMunch differ from other monitoring and visualization tools?
I asked Subramanian what made CloudMunch different from the others. In general, he says CloudMunch’s focus on end-to-end connection of data (all the way from code repositories such as GitHub through to the actual production environments) is key. This ability to tie in the feedback coming from insights to take further action is what makes a DevOps intelligence platform useful to enterprises.
I pushed back a little on this and asked for some specific differentiation. Datadog, for example, is a company I have written about extensively and one that also promises this end-to-end monitoring notion. The same goes for monitoring and analytics products from Chef and Silicon Valley darling New Relic—since it extended from just monitoring infrastructure and into applications. So, what is CloudMunch’s perspective on these companies and how does it differentiate itself from them?
In the case of New Relic, Subramanian says that product is more of a monitoring tool for the Ops part of DevOps and notes that they support New Relic as a data source, using that data to correlate with data from other tools to offer insights.
Subramanian sees DataDog as the closest competitor to his product but asserts that DataDog is more focused on performance monitoring, alerting, visibility, etc. He points out that performance monitoring is only one aspect of what CloudMunch is trying to do and they go far beyond that with more nuances questions: How is code quality impacting in this sprint? How is the code quality from last sprint impacting production after deployment? Whereas DataDog collects metrics and delivers direct insights based upon it, CloudMunch is more about insights on outcomes or those related to specific roles.
In the case of Chef Analytics, a new product recently announced by the automation vendor, Subramanian contends that it is more on the side of metrics and visualization than actionable insights at this point. He also points out that it is based on pipeline created using Chef Platform. CloudMunch is built to work across multiple platforms and tools, including Chef, Puppet and Ansible. This speaks to a core CloudMunch philosophy, which is to embrace whatever tools development and operations teams already use.
In terms of the key features CloudMunch offers, the product highlights are as follows:
- Unified management panel for metrics from various tools, stages and roles
- DevOps dashboard with actionable intelligence based on outcomes such as sprint, roles, release/deployment, builds, etc.
- Actions that drive automation tasks based on triggers/alerts
- Global Dashboard for managers to keep complete track of the entire lifecycle of the software delivered by various teams
- Executive Dashboards to enable leadership to get the big picture insights
- DevOps Advisor for decision makers to get just-in-time advisory/guidance based on correlated insights
- Support for DevOps tools such as Puppet, Chef, Ansible, Jenkins, Github, Subversion, Jira, Sonarqube, NewRelic, cAdvisor, Artifactory, VersionOne, Cruise Control, Heapster, Perforce, Amazon Web Services, Google Cloud, Docker, Kubernetes, Mesosphere and OpenShift.
My POV
I’m not 100 percent convinced that CloudMunch is completely unique in the space, but to be honest, that’s not the point. What is important is CloudMunch’s focus on offering teams choice of platforms, the company’s desire to embrace the totality of different development and operations tools in use, and its commitment to offering a single view across the organization. I’ve been watching CloudMunch for a couple of years now, and this release looks to be a strong offering from the company.
