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Developers need the right data and workflows to efficiently run cloud native environments. Learn more how observability in DevOps organizations help.
On: May 23, 2024
Jess is a Technical Content Writer on the content marketing team at Chronosphere. She has over a decade of experience writing, editing, and managing content for B2B technology brands. Prior to Chronosphere, she worked at TechTarget covering data center, virtualization, and IoT technology. She currently resides in Seattle and is a trivia enthusiast.
With the evolution and adoption of cloud native architecture, it is no longer beneficial to have large teams that either oversee just software development or operations. This has led to more organizations implementing a DevOps approach to promote faster development, rapid software updates, and constant application optimization.
Even though you can’t “buy” DevOps, developers can’t achieve all of its objectives without the right tools in place. Developers need observability platforms to do their job efficiently, quickly troubleshoot incidents, understand how code deploys effect production, and keep applications online. This is where organizations leverage observability — so developers get visibility into cloud native environments, boost their efficiency, and create the most reliable applications possible.
So how exactly do these two concepts fit together? Let’s dive into what DevOps is, how it supports cloud native architecture, and how observability and a DevOps culture increases overall developer efficiency.
The DevOps methodology combines and automates work from both software development and operations teams, instead of segmenting the two teams. Organizations then create specialized, service-specific teams and roll up code into composite applications with a continuous integration/continuous delivery pipeline. This lets developers publish code faster, oversee code from start to production, and constantly update applications.
Cloud native architectures rely on three main technologies to operate: microservices, containers, and Kubernetes. This lets organizations have scalability, flexibility, security, and resiliency within their IT environments.
But due to the distributed nature of cloud native — instead of traditional monolithic architectures — engineering departments need to have an organizational structure that supports operating many individual services instead of one, unified system. This makes DevOps, where collaboration and equitably sharing responsibilities between developers and operators the most ideal configuration.
As mentioned earlier, DevOps breaks down development and operations teams into smaller, specialized teams to simultaneously work on individual services and projects. This allows developers to focus on specific microservices and make regular updates and improvements to different parts of the overall cloud native environment on a daily basis.
When organizations implement both cloud native and DevOps methodology, they are able to accelerate time to market; integrate automation; easily increase efficiency; effectively scale; and support reliability and resiliency.
Now with the technology and organizational framework in place, developers need observability tools to ingest all the data from the cloud native architecture, monitor the customer experience, and provide context when something goes wrong.
Observability can do all of these tasks and also includes features for developers to control their data; quickly cut through high data volumes; and get necessary incident context to troubleshoot issues faster. With observability tools, developers can configure their dashboards and view the services they directly oversee and get more in-depth information instead of just raw telemetry, in addition to inspecting and understanding upstream and downstream dependencies.
Ultimately, observability is designed to help developers know, triage, and understand what’s happening in their cloud native environments and being able to address issues quickly when they occur regardless if it’s a developer or operator, site reliability engineer, or infrastructure engineer answering the page.
Not all observability platforms can handle cloud native architecture, which can pose challenges in a DevOps organizational structure. Traditional application performance monitoring software, or second generation observability, was designed to support monolithic architectures primarily running virtual machines, which meant developers knew more easily where the issue was and didn’t have to go through as much data to get what they needed.
This worked well as businesses used on-premises and cloud infrastructure along with separate development and operations teams. However, with the emergence of cloud native architecture and smaller, specialized development teams, organizations that still use application performance monitoring for cloud native environments often deal with:
Complexity: Application performance monitoring software was designed for a top-down organizational structure and waterfall development workflows. This translated into developers creating dashboards, reports, and alerts from the bottom up and disconnected from other teams. While teams could work quickly, this made it hard to scale as applications grew in complexity and difficult for developers to have visibility across a more distributed organizational and development structure.
Unprecedented observability data growth: Cloud native environments produce 10-100x more data than VM-based ones. Unfortunately, more data does not lead to better insights. In fact, developers spend more time than ever just sifting through all of this telemetry (container metrics, traces, and endless log lines) to validate whether an alert actually indicates an issue. Coupled with observability tool sprawl you have developers flipping through tabs trying to correlate whether the spike in errors is related to the last deployment, and it’s a recipe for frustration and slow troubleshooting times.
Observability costs: Most APM pricing models are still based upon data ingestion volumes, number of users, or number of hosts, which becomes costly very quickly with high cardinality data or large-scale cloud native environments. To reduce these costs, developers end up foregoing custom metrics and/or start dropping metrics to reduce data volumes and minimize cardinality tags. Though this can stabilize costs, it negatively impacts a developer’s ability to visualize the behaviors of their environment and produces incomplete or inaccurate data.
To better address these challenges organizations are using cloud native observability over traditional application performance monitoring (APM) and infrastructure monitoring tools.
Specifically, using cloud native observability in DevOps organizations helps developers:
In cloud native DevOps organizations, developers own their code from development to production, so they need data on code performance and the services they oversee. A cloud native observability platform provides features and dashboards so developers and teams can automatically just see the data most relevant to them, instead of having to sift through every possible alert or data view. These customized workflows let developers see individual service performance and relevant interdependencies at a glance.
Because cloud native environments produce so much data, developers need features that help them analyze, refine, and operate their data. Cloud native observability platforms empower developers to easily identify the most useful – and useless – data, so they have the most up-to-date observability data available. With observability, development teams can more confidently keep or drop metrics as well as take ownership of individual and team data usage because they can know specific metrics do, who uses them, and how often they are used; there’s also no longer a need to play traffic cop for metric usage as managers can place metric quotas on teams to avoid overages or crowding out other developers.
Cloud native environments already have a lot of working parts – so developers don’t necessarily have time to duplicate efforts or constantly spin up dashboards, service-level objectives (SLO), or alerts. Observability platforms can provide out-of-the box dashboards and metric types that can be configured to better align to internal SLO and service-level agreement definitions. This helps every developer across the organization have the right information and a common starting point, no matter what service or project they’re working on.
Teams in a DevOps culture work quickly and often push out multiple deployments over the course of a day. This requires a cloud native observability platform that rapidly loads real-time dashboards and swiftly returns query results so developers can quickly find any issues that arise before they affect the end user or overall platform performance. Observability can help developers:
All together, having these observability platform benefits help developers do their job more efficiently so they can effectively support cloud native application development, ship code in a timely manner, and get data when things go awry.
Cloud native adoption continues to grow with 91% percent of organizations increasing their spending on cloud native application development over the next 12-18 months, according to Enterprise Strategy Group’s 2023 report, The Mainstreaming of Cloud-native Apps and Methodologies.
Without the right organizational workflows and cloud native observability, developers end up spending more time troubleshooting than innovating.
Interested in learning more about Chronosphere and cloud native observability? Check out the resources below:
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