Site Reliability Engineering: An Imperative in Enterprise IT - Part 1

Heidi Carson
Pepperdata

Site reliability engineering (SRE) is fast becoming an essential aspect of modern IT operations, particularly in highly scaled, big data environments. As businesses and industries shift to the digital and embrace new IT infrastructures and technologies to remain operational and competitive, the need for a new approach for IT teams to find and manage the balance between launching new systems and features and ensuring these are intuitive, reliable, and friendly for end users has intensified as well.


Interest in and around site reliability engineering has surged over the last few years. According to a recent finding by LinkedIn, site reliability engineer is listed as among the 25 fastest growing professions within the last five years.

But what exactly is site reliability engineering?

And how does it impact a digital enterprise's ability to satisfy completely or even exceed their Service Level Objectives (SLOs) and reach their business goals, even in large-scale environments?

Though there is no such thing as perfect technology, having the right processes in place may make a world of difference. Continue reading to learn more about site reliability engineering and how to implement best practices to ensure all your systems run at maximum efficiency and reliability.

What is Site Reliability Engineering?

Site reliability engineering looks and treats IT operations from a software engineering perspective. The mission is to constantly monitor IT systems, tools, and features, primarily their availability, latency, performance, and capacity.

Site reliability engineers rely on software to manage systems, pinpoint problems, and automate various operation tasks. SRE obtains the tasks that have been historically assigned to and performed manually by the operations teams and hands them over to site reliability engineers. The SREs then take the tasks and leverage automation and standardization to address problems and further improve the reliability of the entire production system.

SREs are now seen as a critical part in the creation and management of scalable and highly reliable software systems. With SREs, IT teams and system admins can govern and operate much larger systems via code. This practice allows them to scale and sustain thousands or hundreds of thousands of machines.

What Does a Site Reliability Engineer Do?

An SRE is responsible for maximizing a computer system's reliability and efficiency. SREs understand what all people who interface with a computer system expect from that system and work to meet those expectations at scale. As such, SREs serve as the glue between software engineering and IT operations. SREs often describe their job in terms of creatively filling in the gaps to make people happy, from developers to end-users to members of the management team. You know that your SREs are doing a good job when you can take it for granted that all your systems are running at maximum efficiency and reliability.

Site reliability engineers usually work in tandem with both IT operations and software development teams. SRE teams help IT operations drive deeper reliability into their production systems. On top of that, SR teams likely help IT, support and development teams reduce time spent on support tickets and escalations, thus allowing them to focus, develop, and roll out new and improved features and services.

Enterprises task site reliability engineers to proactively create and implement software and services designed to boost IT operations and support. This can range from monitoring capabilities to sending notifications when there are changes in the code during production. SRE teams usually work on homegrown tools from scratch as this allows them to efficiently deal with issues in software delivery or incident management.

SRE teams can also be deployed to work on support escalation. However, as systems mature, they become reliable. This then results in fewer critical events in production, which translates to fewer support escalations. Site reliability engineers gather up so much knowledge in both software engineering and IT operations that they become great support teams themselves, helping organizations route issues to the right people.

Because they touch on many aspects of software development and IT, site reliability engineers also take part in the documentation of tribal knowledge. SRE teams also perform post-documentation work such as constant upkeep and runbooks to keep the quality and integrity of knowledge updated and intact.

Site reliability engineers often take on-call responsibilities. Given their exposure to various areas of engineering and IT, SRE teams constantly collaborate to enhance system reliability and optimize on-call processes.

SRE Best Practices in Big Data Environments

There is no perfect SRE strategy. Any site reliability framework requires constant refinement to make sure operational needs are met. The following SRE principles and best practices will help big data organizations execute and tailor their SRE strategy based on their requirements.

■ Construct SLOs: SLOs form the bedrock of your SRE strategy. It is the foundation on which other essential aspects such as budgets, schedules, and priorities are based on and built upon. To help create SLOs, experts recommend defining them like an end user. To do just that, you need to genuinely ask how good your services should be? This helps you set a threshold for acceptable performance and reliability, which any point beyond will prompt users to open support tickets. In big data environments, defining your SLOs translates directly into the amount of investment you need to make in SRE.

■ Monitoring and Measuring: SRE teams constantly monitor their applications/systems for performance issues and service availability, especially in large-scale environments. All is good when everything is behaving as expected. But when an issue is important enough to affect a user, that issue must be dealt with immediately. For such reliability concerns, the best way to deal with it is to treat these issues as bugs. That means entering these issues into the bug tracking system and applying immediate action when it surfaces before it impacts the user experience.

■ Efficient Capacity Planning: Enterprises need to look ahead when it comes to planning their capacity, especially in today's complex on-premises and cloud environments. They need to take into account the capacity requirements to address increased organic growth (more product adoptions) and inorganic growth (surges in demand driven by feature launches, marketing campaigns, etc.). Failure to forecast and plan for adequate capacity can result in outages. For example, massive user events such as  Black Friday or Cyber Monday require more capacity than usual. Sites and apps that don't have the capacity to handle volumes of visitors during these events will likely crash.

■ Look Out for System Changes: In many instances, the majority of outages are due to changes made to a live system. This can be a reconfiguration for a new binary push. It's important to realize that even the slightest change can lead to a big impact. Thus, it's prudent that SRE teams analyze any change and the potential risk it entails. Any change to the management should be supervised. Prior to making the change, SRE teams need to take into account the long-term effects of the change, not just how it can affect the system now. If the change results in unexpected behavior, site reliability engineers must immediately roll back the system to its previous configurations and diagnose after to cut down Mean Time to Recovery (MTTR). Conducting loading testing and accurate provisioning is key to efficient capacity planning. Overprovisioning can result in underutilized resources going to waste, thus increasing your expenses.

■ Automation, Automation, Automation: Toil is the type of production service task that's usually repetitive, and scales linearly as the service evolves. Toil is manual, yet automatable. Especially in today's complex, big data environments, SRE teams must automate their toil responses, such as testing every backup and other manual and repetitive processes. By developing an automated solution to manage toil, engineers can reduce their manual workload and focus on innovating.

■ Blameless, Constructive Postmortems: Postmortems are crucial to SREs as it provides engineers with written documentation of an incident and other important details such as impact, actions performed for mitigation and resolutions, root causes, and recommended follow-up actions. For postmortems to be completely blameless, it must include matters pertaining to the incident, its processes, actions, and recommendations. It should not mention or indict specific individuals or teams as well as inappropriate behavior. This approach prevents a culture of finger-pointing and laying the blame on people. Instead, it encourages engineers to identify flaws and focus on improving their systems and processes.

Go to: Site Reliability Engineering: An Imperative in Enterprise IT - Part 2

Heidi Carson is Product Manager at Pepperdata

Related Links

Hot Topics

Related Vendors

The Latest

Payment Outages Threaten $44.4 Billion in US Retail and Hospitality Sales Annually

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

From Automation to Intelligence: How AI Will Redefine DevOps in 2026

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

AI First? Why CIOs Are Putting the Cart Before the Horse

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

Mitigating Complexity for Today's Database Administrator

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

How Enterprises Are Re-Architecting Around AI

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

MEAN TIME TO INSIGHT Podcast - Episode 20: 2026 NetOps Predictions

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

EMA at 30: The Value of Consistency

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

Making Sense of Logical Data Management

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

2026 Data Center Predictions

APMdigest's Predictions Series continues with 2026 Data Center Predictions — industry experts offer predictions on how data centers will evolve and impact business in 2026 ...

2026 DataOps Predictions - Part 2

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms ...

Site Reliability Engineering: An Imperative in Enterprise IT - Part 1

Heidi Carson
Pepperdata

Site reliability engineering (SRE) is fast becoming an essential aspect of modern IT operations, particularly in highly scaled, big data environments. As businesses and industries shift to the digital and embrace new IT infrastructures and technologies to remain operational and competitive, the need for a new approach for IT teams to find and manage the balance between launching new systems and features and ensuring these are intuitive, reliable, and friendly for end users has intensified as well.


Interest in and around site reliability engineering has surged over the last few years. According to a recent finding by LinkedIn, site reliability engineer is listed as among the 25 fastest growing professions within the last five years.

But what exactly is site reliability engineering?

And how does it impact a digital enterprise's ability to satisfy completely or even exceed their Service Level Objectives (SLOs) and reach their business goals, even in large-scale environments?

Though there is no such thing as perfect technology, having the right processes in place may make a world of difference. Continue reading to learn more about site reliability engineering and how to implement best practices to ensure all your systems run at maximum efficiency and reliability.

What is Site Reliability Engineering?

Site reliability engineering looks and treats IT operations from a software engineering perspective. The mission is to constantly monitor IT systems, tools, and features, primarily their availability, latency, performance, and capacity.

Site reliability engineers rely on software to manage systems, pinpoint problems, and automate various operation tasks. SRE obtains the tasks that have been historically assigned to and performed manually by the operations teams and hands them over to site reliability engineers. The SREs then take the tasks and leverage automation and standardization to address problems and further improve the reliability of the entire production system.

SREs are now seen as a critical part in the creation and management of scalable and highly reliable software systems. With SREs, IT teams and system admins can govern and operate much larger systems via code. This practice allows them to scale and sustain thousands or hundreds of thousands of machines.

What Does a Site Reliability Engineer Do?

An SRE is responsible for maximizing a computer system's reliability and efficiency. SREs understand what all people who interface with a computer system expect from that system and work to meet those expectations at scale. As such, SREs serve as the glue between software engineering and IT operations. SREs often describe their job in terms of creatively filling in the gaps to make people happy, from developers to end-users to members of the management team. You know that your SREs are doing a good job when you can take it for granted that all your systems are running at maximum efficiency and reliability.

Site reliability engineers usually work in tandem with both IT operations and software development teams. SRE teams help IT operations drive deeper reliability into their production systems. On top of that, SR teams likely help IT, support and development teams reduce time spent on support tickets and escalations, thus allowing them to focus, develop, and roll out new and improved features and services.

Enterprises task site reliability engineers to proactively create and implement software and services designed to boost IT operations and support. This can range from monitoring capabilities to sending notifications when there are changes in the code during production. SRE teams usually work on homegrown tools from scratch as this allows them to efficiently deal with issues in software delivery or incident management.

SRE teams can also be deployed to work on support escalation. However, as systems mature, they become reliable. This then results in fewer critical events in production, which translates to fewer support escalations. Site reliability engineers gather up so much knowledge in both software engineering and IT operations that they become great support teams themselves, helping organizations route issues to the right people.

Because they touch on many aspects of software development and IT, site reliability engineers also take part in the documentation of tribal knowledge. SRE teams also perform post-documentation work such as constant upkeep and runbooks to keep the quality and integrity of knowledge updated and intact.

Site reliability engineers often take on-call responsibilities. Given their exposure to various areas of engineering and IT, SRE teams constantly collaborate to enhance system reliability and optimize on-call processes.

SRE Best Practices in Big Data Environments

There is no perfect SRE strategy. Any site reliability framework requires constant refinement to make sure operational needs are met. The following SRE principles and best practices will help big data organizations execute and tailor their SRE strategy based on their requirements.

■ Construct SLOs: SLOs form the bedrock of your SRE strategy. It is the foundation on which other essential aspects such as budgets, schedules, and priorities are based on and built upon. To help create SLOs, experts recommend defining them like an end user. To do just that, you need to genuinely ask how good your services should be? This helps you set a threshold for acceptable performance and reliability, which any point beyond will prompt users to open support tickets. In big data environments, defining your SLOs translates directly into the amount of investment you need to make in SRE.

■ Monitoring and Measuring: SRE teams constantly monitor their applications/systems for performance issues and service availability, especially in large-scale environments. All is good when everything is behaving as expected. But when an issue is important enough to affect a user, that issue must be dealt with immediately. For such reliability concerns, the best way to deal with it is to treat these issues as bugs. That means entering these issues into the bug tracking system and applying immediate action when it surfaces before it impacts the user experience.

■ Efficient Capacity Planning: Enterprises need to look ahead when it comes to planning their capacity, especially in today's complex on-premises and cloud environments. They need to take into account the capacity requirements to address increased organic growth (more product adoptions) and inorganic growth (surges in demand driven by feature launches, marketing campaigns, etc.). Failure to forecast and plan for adequate capacity can result in outages. For example, massive user events such as  Black Friday or Cyber Monday require more capacity than usual. Sites and apps that don't have the capacity to handle volumes of visitors during these events will likely crash.

■ Look Out for System Changes: In many instances, the majority of outages are due to changes made to a live system. This can be a reconfiguration for a new binary push. It's important to realize that even the slightest change can lead to a big impact. Thus, it's prudent that SRE teams analyze any change and the potential risk it entails. Any change to the management should be supervised. Prior to making the change, SRE teams need to take into account the long-term effects of the change, not just how it can affect the system now. If the change results in unexpected behavior, site reliability engineers must immediately roll back the system to its previous configurations and diagnose after to cut down Mean Time to Recovery (MTTR). Conducting loading testing and accurate provisioning is key to efficient capacity planning. Overprovisioning can result in underutilized resources going to waste, thus increasing your expenses.

■ Automation, Automation, Automation: Toil is the type of production service task that's usually repetitive, and scales linearly as the service evolves. Toil is manual, yet automatable. Especially in today's complex, big data environments, SRE teams must automate their toil responses, such as testing every backup and other manual and repetitive processes. By developing an automated solution to manage toil, engineers can reduce their manual workload and focus on innovating.

■ Blameless, Constructive Postmortems: Postmortems are crucial to SREs as it provides engineers with written documentation of an incident and other important details such as impact, actions performed for mitigation and resolutions, root causes, and recommended follow-up actions. For postmortems to be completely blameless, it must include matters pertaining to the incident, its processes, actions, and recommendations. It should not mention or indict specific individuals or teams as well as inappropriate behavior. This approach prevents a culture of finger-pointing and laying the blame on people. Instead, it encourages engineers to identify flaws and focus on improving their systems and processes.

Go to: Site Reliability Engineering: An Imperative in Enterprise IT - Part 2

Heidi Carson is Product Manager at Pepperdata

Related Links

Hot Topics

Related Vendors

The Latest

Payment Outages Threaten $44.4 Billion in US Retail and Hospitality Sales Annually

Payment system failures are putting $44.4 billion in US retail and hospitality sales at risk each year, underscoring how quickly disruption can derail day-to-day trading, according to research conducted by Dynatrace ... The findings show that payment failures are no longer isolated incidents, but part of a recurring operational challenge that disrupts service, damages customer trust, and negatively impacts revenue ...

From Automation to Intelligence: How AI Will Redefine DevOps in 2026

For years, the success of DevOps has been measured by how much manual work teams can automate ... I believe that in 2026, the definition of DevOps success is going to expand significantly. The era of automation is giving way to the era of intelligent delivery, in which AI doesn't just accelerate pipelines, it understands them. With open observability connecting signals end-to-end across those tools, teams can build closed-loop systems that don't just move faster, but learn, adapt, and take action autonomously with confidence ...

AI First? Why CIOs Are Putting the Cart Before the Horse

The conversation around AI in the enterprise has officially shifted from "if" to "how fast." But according to the State of Network Operations 2026 report from Broadcom, most organizations are unknowingly building their AI strategies on sand. The data is clear: CIOs and network teams are putting the cart before the horse. AI cannot improve what the network cannot see, predict issues without historical context, automate processes that aren't standardized, or recommend fixes when the underlying telemetry is incomplete. If AI is the brain, then network observability is the nervous system that makes intelligent action possible ...

Mitigating Complexity for Today's Database Administrator

SolarWinds data shows that one in three DBAs are contemplating leaving their positions — a striking indicator of workforce pressure in this role. This is likely due to the technical and interpersonal frustrations plaguing today's DBAs. Hybrid IT environments provide widespread organizational benefits but also present growing complexity. Simultaneously, AI presents a paradox of benefits and pain points ...

How Enterprises Are Re-Architecting Around AI

Over the last year, we've seen enterprises stop treating AI as “special projects.” It is no longer confined to pilots or side experiments. AI is now embedded in production, shaping decisions, powering new business models, and changing how employees and customers experience work every day. So, the debate of "should we adopt AI" is settled. The real question is how quickly and how deeply it can be applied ...

MEAN TIME TO INSIGHT Podcast - Episode 20: 2026 NetOps Predictions

In MEAN TIME TO INSIGHT Episode 20, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA presents his 2026 NetOps predictions ... 

EMA at 30: The Value of Consistency

Today, technology buyers don't suffer from a lack of information but an abundance of it. They need a trusted partner to help them navigate this information environment ...

Making Sense of Logical Data Management

My latest title for O'Reilly, The Rise of Logical Data Management, was an eye-opener for me. I'd never heard of "logical data management," even though it's been around for several years, but it makes some extraordinary promises, like the ability to manage data without having to first move it into a consolidated repository, which changes everything. Now, with the demands of AI and other modern use cases, logical data management is on the rise, so it's "new" to many. Here, I'd like to introduce you to it and explain how it works ...

2026 Data Center Predictions

APMdigest's Predictions Series continues with 2026 Data Center Predictions — industry experts offer predictions on how data centers will evolve and impact business in 2026 ...

2026 DataOps Predictions - Part 2

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026. Part 2 covers data and data platforms ...