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SREs Need Faster, More Unified Data Investigation

Gagan Singh
Elastic

No one ever said Site Reliability Engineers (SREs) have it easy. SREs have to deal with ever-increasing amounts of data that is increasingly complex to discover and analyze. Heaps of metrics, logs, traces, and profiling data are also siloed, leading to a fragmented and opaque monitoring toolset to navigate operational efficiency and problem resolution.

Additionally, SREs have the unprecedented pressure to resolve site uptime/availability and performance issues and deliver data-driven insights that get to the root cause of those issues, which ensure mission-critical applications and workloads run smoothly and without interruption.

This increase in data scale and complexity drives the need for greater productivity and efficiency among SREs but also developers, security professionals, and observability practitioners so they can find the answers and insights faster while collaborating seamlessly.

In this environment, SREs need faster, more unified data investigation. An observability solution that provides not only unified data but also contextual-based analysis is a crucial tool for SREs to keep pace with the growing observability challenges, resolve site issues more quickly and easily, and deliver value to the organization by preventing disruptions to "business as usual" that can negatively impact daily operations and end-user experiences.

Decoding a Deluge of Data

To prevent and remediate system downtime and other related issues, SREs monitor thousands of systems that generate important trace, log, and metric data. This data is then used to identify problems and implement measures to prevent system or application interruptions in the future.

However, observability-ingested data can be complex and unpredictable as the number of nodes to monitor changes frequently. To date, it's been a challenge to perform data aggregation and analysis across various data sources from a single query. This is a problem because the ability to analyze system behavior with a combined understanding of multiple data sets is essential for an SRE. They need the ability to correlate and reshape data to unearth deeper insights into system and application behavior and perform post-hoc analysis after an issue is identified.

One way to meet the increasingly complex needs of SREs with speed and efficiency is via new AI-powered capabilities and natural language interfaces that enable concurrent processing irrespective of data source and structure.

Turning the Page on Old Ways of Data Investigation

What will this new world of faster, more unified data investigation look like?

For starters, we'll see reduced time to resolution as this will enhance detection accuracy in several important ways.

Secondly, it allows engineers to identify trends, isolate incidents, and reduce false positives. This richer context assists with troubleshooting and helps quickly pinpoint root causes and resolve issues.

Finally, we'll see leaps ahead for operational efficiency. From a single query, SREs will be able to create more actionable notifications, create visualizations or dashboards, or pinpoint performance bottlenecks and the root cause of system issues.

Concurrent processing will enable enhanced analysis with stronger insights. Operations engineers will be able to get their hands around a diverse array of observability data — not just application and infrastructure data, but also business data — regardless of what source it comes from or structure it takes.

In observability, context is everything. A world of faster, more unified data investigation would provide the ability to easily enrich data with additional context. With this context fed in, engineers can personalize and create an uninterrupted, intelligent, and efficient workflow for data inquiries.

With this type of functionality in place, SREs will redefine how they interact with data, which will democratize access to newfound data insights and transform the foundations of their decision-making.

It's time for SREs to turn the page on the data investigation approaches of the past. A world of faster, more unified data investigation awaits.

Gagan Singh is VP, Product Marketing, at Elastic

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SREs Need Faster, More Unified Data Investigation

Gagan Singh
Elastic

No one ever said Site Reliability Engineers (SREs) have it easy. SREs have to deal with ever-increasing amounts of data that is increasingly complex to discover and analyze. Heaps of metrics, logs, traces, and profiling data are also siloed, leading to a fragmented and opaque monitoring toolset to navigate operational efficiency and problem resolution.

Additionally, SREs have the unprecedented pressure to resolve site uptime/availability and performance issues and deliver data-driven insights that get to the root cause of those issues, which ensure mission-critical applications and workloads run smoothly and without interruption.

This increase in data scale and complexity drives the need for greater productivity and efficiency among SREs but also developers, security professionals, and observability practitioners so they can find the answers and insights faster while collaborating seamlessly.

In this environment, SREs need faster, more unified data investigation. An observability solution that provides not only unified data but also contextual-based analysis is a crucial tool for SREs to keep pace with the growing observability challenges, resolve site issues more quickly and easily, and deliver value to the organization by preventing disruptions to "business as usual" that can negatively impact daily operations and end-user experiences.

Decoding a Deluge of Data

To prevent and remediate system downtime and other related issues, SREs monitor thousands of systems that generate important trace, log, and metric data. This data is then used to identify problems and implement measures to prevent system or application interruptions in the future.

However, observability-ingested data can be complex and unpredictable as the number of nodes to monitor changes frequently. To date, it's been a challenge to perform data aggregation and analysis across various data sources from a single query. This is a problem because the ability to analyze system behavior with a combined understanding of multiple data sets is essential for an SRE. They need the ability to correlate and reshape data to unearth deeper insights into system and application behavior and perform post-hoc analysis after an issue is identified.

One way to meet the increasingly complex needs of SREs with speed and efficiency is via new AI-powered capabilities and natural language interfaces that enable concurrent processing irrespective of data source and structure.

Turning the Page on Old Ways of Data Investigation

What will this new world of faster, more unified data investigation look like?

For starters, we'll see reduced time to resolution as this will enhance detection accuracy in several important ways.

Secondly, it allows engineers to identify trends, isolate incidents, and reduce false positives. This richer context assists with troubleshooting and helps quickly pinpoint root causes and resolve issues.

Finally, we'll see leaps ahead for operational efficiency. From a single query, SREs will be able to create more actionable notifications, create visualizations or dashboards, or pinpoint performance bottlenecks and the root cause of system issues.

Concurrent processing will enable enhanced analysis with stronger insights. Operations engineers will be able to get their hands around a diverse array of observability data — not just application and infrastructure data, but also business data — regardless of what source it comes from or structure it takes.

In observability, context is everything. A world of faster, more unified data investigation would provide the ability to easily enrich data with additional context. With this context fed in, engineers can personalize and create an uninterrupted, intelligent, and efficient workflow for data inquiries.

With this type of functionality in place, SREs will redefine how they interact with data, which will democratize access to newfound data insights and transform the foundations of their decision-making.

It's time for SREs to turn the page on the data investigation approaches of the past. A world of faster, more unified data investigation awaits.

Gagan Singh is VP, Product Marketing, at Elastic

Hot Topics

The Latest

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Image
Broadcom

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...