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Navigating the SRE Landscape for 2024: A Comprehensive Exploration of Decentralized Practices

Leo Vasiliou
Catchpoint

As decentralized and complex systems shape the landscape, site reliability engineering (SRE) practices are evolving to meet the challenges posed by this paradigm shift. The recent SRE Report 2024, a comprehensive survey-based exploration conducted by Catchpoint, provides insights into the dynamic nature of SRE practices and the key considerations influencing the reliability landscape.

Catchpoint's sixth SRE Report uncovers findings from a survey of more than 400 IT professionals globally. Specifically, the report delves into the adjustments and adaptations SRE practices are undergoing in response to organizations recognizing the need to extend their monitoring and learning purview beyond directly controlled elements and embrace third-party services and endpoints. This shift reflects a departure from limiting scope to first-party services toward a more federated approach, compelling organizations to reimagine reliability within the context of distributed architectures.

The report highlights several key insights that illuminate the current landscape of SRE practices:

Decentralized Monitoring

64% of organizations believe that SRE practitioners should monitor endpoints outside their direct control, such as third-party services, indicating a growing emphasis on critical visibility beyond organizational boundaries.

Tool Diversity

66% of organizations utilize two to five monitoring tools, aligning with their staff size and unique capabilities. With 81% of organizations having multiple telemetry types feeding their observability frameworks, this underscores the recognition that a varied toolkit enhances the ability to address the complexity of modern architectures.

Structural Evolution

51% and 44% of companies structure their teams by product or service, or by platform or capability, respectively. The use of these structures trends upward with larger company sizes, reflecting the evolving nature of roles and team structures within the SRE domain.

Learning from Incidents (LFI)

LFI emerges as a universal business opportunity, with 52% acknowledging the need for improvement in reviewing major incidents, irrespective of company size.

Artificial intelligence (AI) in SRE

While 53% anticipate AI making work easier in the next two years, mixed views on AI's usefulness are evident across different organizational ranks. Management leans towards AI for potential cost savings, whereas individual contributors express reservations, citing a preference for pride in their work over efficiency.

As the SRE landscape continues to evolve, practitioners anticipate significant challenges in the coming years. Balancing costs, time constraints, aligning ranks, and navigating the complexities of decentralized systems are identified as prominent challenges. Resource constraints, particularly concerns related to cost or budget (44%), underscore the need for organizations to explore monitoring elements outside their direct management, including content delivery networks (CDN) and domain name systems (DNS).

Learning from incidents has also emerged as a focal point for improvement across organizations. The report underscores the need for organizations to dedicate protected time to learn from both major and non-major incidents, emphasizing that each presents a valuable learning opportunity. With 71% of respondents dealing with dozens or even hundreds of non-ticketed incidents monthly, the need for refining blameless feedback loops is critical to fortify the resilience of companies over time.

But one of the biggest takeaways from this year’s report is the nuanced perspectives regarding the role of AI in SRE. Views on AI's usefulness are notably influenced by organizational rank. Management and leadership view AI as a potential avenue for cost savings, considering its application in reducing headcount or accelerating time to market. In contrast, individual contributors exhibit a less positive sentiment, emphasizing the importance of being proud of their work over operational efficiency. This divergence in mindset is expected to drive mixed views on the integration of AI in SRE. However, survey respondents identified GenAI as a promising application, although this perception may be influenced by the overarching hype surrounding AIOps.

It is evident that SRE practices are at a crossroads, adapting to the demands of decentralized systems and the evolving expectations of practitioners across various organizational ranks. The report serves not only as a reflection of current practices but also as a guide for the future, offering insights into potential challenges and opportunities on the horizon. As organizations grapple with the intricacies of decentralized architectures, the SRE landscape continues to evolve, driven by a collective commitment to reliability, learning and the pursuit of excellence.

Leo Vasiliou is Director of Product Marketing at Catchpoint

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Navigating the SRE Landscape for 2024: A Comprehensive Exploration of Decentralized Practices

Leo Vasiliou
Catchpoint

As decentralized and complex systems shape the landscape, site reliability engineering (SRE) practices are evolving to meet the challenges posed by this paradigm shift. The recent SRE Report 2024, a comprehensive survey-based exploration conducted by Catchpoint, provides insights into the dynamic nature of SRE practices and the key considerations influencing the reliability landscape.

Catchpoint's sixth SRE Report uncovers findings from a survey of more than 400 IT professionals globally. Specifically, the report delves into the adjustments and adaptations SRE practices are undergoing in response to organizations recognizing the need to extend their monitoring and learning purview beyond directly controlled elements and embrace third-party services and endpoints. This shift reflects a departure from limiting scope to first-party services toward a more federated approach, compelling organizations to reimagine reliability within the context of distributed architectures.

The report highlights several key insights that illuminate the current landscape of SRE practices:

Decentralized Monitoring

64% of organizations believe that SRE practitioners should monitor endpoints outside their direct control, such as third-party services, indicating a growing emphasis on critical visibility beyond organizational boundaries.

Tool Diversity

66% of organizations utilize two to five monitoring tools, aligning with their staff size and unique capabilities. With 81% of organizations having multiple telemetry types feeding their observability frameworks, this underscores the recognition that a varied toolkit enhances the ability to address the complexity of modern architectures.

Structural Evolution

51% and 44% of companies structure their teams by product or service, or by platform or capability, respectively. The use of these structures trends upward with larger company sizes, reflecting the evolving nature of roles and team structures within the SRE domain.

Learning from Incidents (LFI)

LFI emerges as a universal business opportunity, with 52% acknowledging the need for improvement in reviewing major incidents, irrespective of company size.

Artificial intelligence (AI) in SRE

While 53% anticipate AI making work easier in the next two years, mixed views on AI's usefulness are evident across different organizational ranks. Management leans towards AI for potential cost savings, whereas individual contributors express reservations, citing a preference for pride in their work over efficiency.

As the SRE landscape continues to evolve, practitioners anticipate significant challenges in the coming years. Balancing costs, time constraints, aligning ranks, and navigating the complexities of decentralized systems are identified as prominent challenges. Resource constraints, particularly concerns related to cost or budget (44%), underscore the need for organizations to explore monitoring elements outside their direct management, including content delivery networks (CDN) and domain name systems (DNS).

Learning from incidents has also emerged as a focal point for improvement across organizations. The report underscores the need for organizations to dedicate protected time to learn from both major and non-major incidents, emphasizing that each presents a valuable learning opportunity. With 71% of respondents dealing with dozens or even hundreds of non-ticketed incidents monthly, the need for refining blameless feedback loops is critical to fortify the resilience of companies over time.

But one of the biggest takeaways from this year’s report is the nuanced perspectives regarding the role of AI in SRE. Views on AI's usefulness are notably influenced by organizational rank. Management and leadership view AI as a potential avenue for cost savings, considering its application in reducing headcount or accelerating time to market. In contrast, individual contributors exhibit a less positive sentiment, emphasizing the importance of being proud of their work over operational efficiency. This divergence in mindset is expected to drive mixed views on the integration of AI in SRE. However, survey respondents identified GenAI as a promising application, although this perception may be influenced by the overarching hype surrounding AIOps.

It is evident that SRE practices are at a crossroads, adapting to the demands of decentralized systems and the evolving expectations of practitioners across various organizational ranks. The report serves not only as a reflection of current practices but also as a guide for the future, offering insights into potential challenges and opportunities on the horizon. As organizations grapple with the intricacies of decentralized architectures, the SRE landscape continues to evolve, driven by a collective commitment to reliability, learning and the pursuit of excellence.

Leo Vasiliou is Director of Product Marketing at Catchpoint

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...