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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

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2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...