Today, most organizations are adopting modern distributed, cloud-native environments and refactoring their monoliths to microservices architecture. The transition is necessary for continuous innovation with higher agility and helps organizations benefit from the economies of scale. However, to achieve these benefits, organizations need to transform their IT Operations. Development and operations teams working in silos in traditional setups are often ill-equipped to support modern environments, which require closer cross-functional collaboration and quicker response. That’s why the traditional demarcation between development and operations teams has blurred, with organizations adopting DevOps and Site Reliability Engineering (SRE). Both these methodologies appear similar, sharing common goals of higher reliability, predictability, quality, and agility for frequent and stable releases; however, there’s a definite difference between DevOps and SRE. We will explore the difference between SRE and DevOps, and how SRE helps organizations transform their IT Operations. 

Andrew Shafer and Patrick Debois coined the term DevOps in 2008, and it is now commonly understood as a combination of development and operations practices, which shorten the development lifecycle with frequent releases. On the other hand, the concept of SRE has been in existence since 2004. Google’s Ben Treynor made it popular with his SRE team. According to Google, SRE is “what happens when you ask a software engineer to solve an operational problem.” Both SRE and DevOps emphasize bringing visibility into the complete application lifecycle and making people aware of processes outside of their immediate responsibility areas. Also, these methodologies place increased focus on monitoring and automation for higher speed, consistency, and quality across the application lifecycle.

Difference Between DevOps and Site Reliability Engineering (SRE)

It is clear that the two approaches aren’t that different; according to Google “SRE prescribes how to succeed in the various DevOps areas.” In other words, where DevOps tells us what needs to be done to make different teams work together, SRE provides us a more tangible approach, detailing how to make them work together. For instance, DevOps advocates reducing organizational silos, and to achieve this goal, SRE prescribes sharing of ownership and usage of common tools and processes across the stack. You can find more on DevOps vs SRE comparison in this blog by Google.

How SRE Transforms ITOps?

SRE teams are formed by people with cross-functional capabilities, i.e., they can troubleshoot routine and complex operational challenges and also have strong software development skills. These capabilities help site reliability engineers manage their work smartly as they can easily automate repetitive operational tasks with a few lines of code. Such automation not only brings consistency by preventing manual errors and oversights but also makes processes more scalable. However, automation cannot solve all problems and sometimes teams have to resolve issues manually. If the SRE team sees that its manual effort is consistently increasing, it can reassign tickets to the development team. Such an arrangement helps SRE teams maintain a proper balance between operational and development workloads.

Another major DevOps tenet is about embracing failure, which aligns well with the Agile philosophy of failing early, failing small. SRE adopts a more nuanced approach to reducing the cost of failure with Service Level Indicators (SLIs) and Service Level Objectives (SLOs). By monitoring SLIs such as request latency, request failure rates, etc. over a period, SREs can define the normal thresholds for these indicators, which helps in fixing the SLOs. By managing and tweaking SLIs and SLOs, SREs reduce error and failure rates, continuously adding reliability to the development lifecycle.

Is SRE the Best Model for ITOps? 

SRE might suit enterprises that are open to the cloud-native paradigm and have adopted cloud-based platforms and delivery models. It offers a definite set of practices, most of which are endorsed by Google. However, SRE is not the only way to improve ITOps; every year, vendors and analysts introduce a new approach to implement and make operations better, which has created an environment of confusion for IT practitioners.  

For instance, AIOps, an emerging ITOps model, involves the usage of data science to learn system behavior and help in smart provisioning, upscaling, and downscaling of infrastructure resources. In theory, it can allow organizations to use machine learning algorithms to iteratively improve their resource allocation and application performance with improved reliability and predictability. Then there is NoOps, which advocates the removal of manual processes entirely with increased adoption of AI, machine learning, and automation. It is important to note that to implement NoOps in the real sense, organizations might have to make significant investments in revamping their IT processes and workflows. 

As organizations evaluate all these new approaches for enhancing development and operations they must consider their tech-readiness and maturity to adopt new processes. Again, among all these approaches, they need to practice caution while automating their routines, as wrong automation can also lead to increased damage and sometimes, irreversible destruction. An automated system that moves fast needs constant monitoring to ensure that development and operational activities are progressing in the right direction.

How Klera Helps?

It is common for enterprise teams to have their own set of tools and practices for accomplishing their tasks. Measuring health and progress in the enterprise environment is not simple and one can struggle to implement the right set of metrics across the board. That’s why implementing SRE or any other process in such mature setups is often a complex challenge. Teams struggle to integrate their tools and processes and it’s not easy to get a unified view with KPIs and dashboards in dynamic and distributed environments. By the time organizations find a workaround to consolidate data and gain visibility into their environment, their development and operational setup move ahead and require rework. Therefore, organizations need higher agility to rapidly build custom apps to automate routine tasks, gather data, create interactive dashboards, and trigger intelligent, machine learning-based alerts and actions. Klera enables all this and much more with its no-code app development platform and out-of-the-box, bi-directional smart connectors for a wide range of ALM and DevOps tools.