Categories: Technical Deep Dive

How SRE teams are eliminating scaling headaches with Hawkeye

It’s peak holiday shopping season, and your e-commerce platform is experiencing record traffic. Your team initiates a scaling operation to handle the load, increasing the UI deployment’s replica count. But instead of scaling smoothly, pods remain stuck in pending state. The PagerDuty alert sounds: “Maximum pod_status_pending GreaterThanThreshold 0.0”. What should be a routine scaling operation has become a critical incident requiring deep investigation across multiple layers of your Kubernetes infrastructure.

The Modern Scaling Investigation Reality

In today’s Kubernetes environments, scaling issues occur within sophisticated observability stacks. CloudWatch captures detailed node and pod metrics while recording scheduler decisions. Prometheus tracks resource utilization, and your APM solution monitors service performance. Yet when scaling problems arise, this wealth of information often complicates rather than simplifies the investigation.

A typical troubleshooting session spans multiple systems and contexts:

You start in Prometheus, examining node capacity metrics. Resources seem available at the cluster level, but are they accessible to your workload? Switching to CloudWatch Container Insights, you dive into pod-level metrics, trying to understand resource utilization patterns. Your logging platform shows scheduler events, but the messages about resource pressure don’t align with your metrics.

The investigation expands as you correlate data across systems:

• Node metrics show available capacity
• Pod events indicate resource constraints
• Scheduler logs mention taint conflicts
• Prometheus alerts show resource quotas approaching limits
• Service mesh metrics indicate traffic distribution issues

Each tool provides critical information, but understanding how these pieces fit together requires constantly switching contexts and mentally correlating events across different abstraction layers in your Kubernetes stack.

Why Scaling Challenges Defy Quick Analysis

What makes scaling investigation particularly demanding isn’t just checking resource availability – it’s understanding the complex interaction between different layers of Kubernetes resource management and constraints:

Available CPU and memory might look sufficient at the cluster level, but pod anti-affinity rules could prevent optimal placement. Node selectors and taints might restrict where pods can run. Resource quotas at the namespace level might block scaling even when node capacity is available. Quality of Service classes affect pod scheduling priority, and Pod Disruption Budgets influence how workloads can be redistributed.

Your observability tools faithfully record all these metrics and events, but understanding the sequence and cause-effect relationships requires simultaneously analyzing multiple data streams while understanding Kubernetes scheduling logic, resource management, and workload distribution patterns.

Hawkeye: Your Scaling Expert

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to check resource metrics – it’s how it analyzes capacity constraints across multiple layers of your Kubernetes infrastructure simultaneously. While an SRE would need to manually correlate data between node metrics, scheduler logs, pod events, and cluster configurations, Hawkeye processes all these data streams in parallel to quickly identify bottlenecks and constraints.

This parallel analysis capability allows Hawkeye to discover cause-and-effect relationships that might take hours for humans to uncover. By simultaneously examining node capacity, scheduling rules, workload distribution patterns, and historical scaling behavior, Hawkeye can identify subtle constraints that wouldn’t be apparent from any single metric or log stream.

Real World Impact

For teams using Hawkeye, the transformation goes beyond faster scaling incident resolution. Engineers report a fundamental shift in how they approach capacity management:

Instead of spending hours correlating data across different monitoring tools during scaling incidents, they can focus on implementing systematic improvements based on Hawkeye’s comprehensive analysis. The mean time to resolution for scaling-related incidents has decreased dramatically, but more importantly, teams can prevent many scaling bottlenecks entirely by acting on Hawkeye’s early warnings and recommendations.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of pod_status_pending analysis while your team focuses on innovation.

Follow Hawkeye

How Desktop Infrastructure Teams are Conquering the Morning Login Storm

It’s 8:45 AM, and your phone lights up with a flood of tickets. “VDI is crawling,” reads the first message. “Can’t access my desktop,” says another. Within minutes, your ServiceNow queue is filled with frustrated users reporting login times stretching past three minutes. You’re facing the dreaded “morning login storm,” and somewhere in the maze of profiles, network traffic, and host metrics lies the root cause – if only you could find it fast enough.

For desktop infrastructure teams, this scenario is all too familiar. In a recent case study, a Fortune 500 company reported that their average login times had ballooned from 45 seconds to over 180 seconds, affecting 65% of their workforce. The business impact? Thousands of lost productivity hours and mounting frustration from both users and IT staff.

The Complex Reality of Modern VDI Environments

Today’s VDI deployments are far more complex than their predecessors. Consider the interconnected components that must work in perfect harmony for a single login:

• Profile services managing user data
• Network infrastructure handling massive morning traffic
• Host resources balancing compute demands
• Storage systems managing IO queues
• Authentication services processing credentials

Traditional monitoring approaches often fall short because they focus on individual metrics rather than the holistic user experience. Your dashboard might show green status lights while users face unacceptable delays. More concerning, by the time you’ve collected and correlated data from multiple tools, precious troubleshooting time has been lost.

The Hidden Costs of Slow Logins

The impact of VDI performance issues extends far beyond the obvious frustration of waiting for a desktop to load. Organizations face:

• Lost productivity during peak business hours
• Increased support ticket volume overwhelming IT teams
• Shadow IT as users seek alternatives
• Employee satisfaction and retention challenges
• Reduced confidence in IT infrastructure

One desktop administrator we spoke with put it perfectly: “Every minute of login delay is multiplied by hundreds of users. It’s not just time we’re losing – it’s trust.”

Enter Hawkeye: Your AI-Powered VDI Performance Partner

This is where a fundamentally different approach comes into play. Instead of relying on static thresholds and manual correlation, Hawkeye acts as an intelligent teammate that understands the complex interplay of VDI components.

In a recent deployment, Hawkeye identified a perfect storm of conditions causing login delays:

• Profile load times exceeding 90 seconds for 75% of sessions
• 40% packet retransmission rates during peak periods
• Profile server CPU utilization spiking to 92%
• Storage latency averaging 45ms for read operations
• Cache hit ratios dropping to 35% during login storms

More importantly, Hawkeye didn’t just collect these metrics – it understood their relationships and impact on the user experience. Within minutes, it provided a comprehensive analysis and actionable remediation steps.

The Transformed Workflow

With Hawkeye as part of the team, the VDI support workflow changes dramatically:

Before Hawkeye:

• Manual correlation of multiple monitoring tools
• Hours spent gathering data from various sources
• Reactive response to user complaints
• Trial-and-error troubleshooting

With Hawkeye:

• Instant correlation of performance metrics
• Proactive identification of emerging issues
• Clear visualization of impact and root cause
• Specific, prioritized remediation steps

Real Results in Real Environments

Organizations leveraging Hawkeye for VDI performance management are seeing transformative results:

• Login times reduced by up to 75%
• Support ticket volume decreased by 60%
• Mean time to resolution cut from hours to minutes
• Proactive resolution of 40% of potential issues before user impact

Looking Forward: From Reactive to Proactive

The future of VDI management isn’t about adding more monitoring tools or building more complex dashboards. It’s about having an intelligent teammate that understands the intricacies of your environment and can take action before users are impacted.

Hawkeye is leading this transformation by:

• Learning normal login patterns for your environment
• Predicting potential bottlenecks before they impact users
• Automatically correlating events across your VDI stack
• Providing clear, actionable recommendations for optimization

Ready to Transform Your VDI Operations?

If you’re ready to move beyond the limitations of traditional monitoring and embrace the future of intelligent VDI management, we’re here to help. Contact us to learn how Hawkeye can become your team’s AI-powered desktop infrastructure expert and help deliver the consistent, high-performance VDI experience your users deserve.

Follow
Hawkeye

How SRE teams are revolutionizing virtualization operations with GenAI

It’s 2 AM, and your phone lights up with another alert: “Critical: Database VM Performance Degradation.” As you dive into your KubeVirt dashboard, you’re faced with a wall of metrics – CPU throttling, IO wait times, memory pressure, and storage latency all competing for your attention. Which metric matters most? What’s the root cause? And most importantly, how quickly can you restore service before it impacts your business?

For SRE teams managing virtualized workloads on Kubernetes, this scenario is all too familiar. KubeVirt has revolutionized how we run virtual machines on Kubernetes, but it’s also introduced new layers of complexity in performance monitoring and troubleshooting. When a VM starts degrading, engineers must correlate data across multiple layers: the VM itself, the KubeVirt control plane, the underlying Kubernetes infrastructure, and the physical hardware – all while under pressure to resolve the issue quickly.

The Reality of KubeVirt Performance Investigation

Traditional approaches to VM performance troubleshooting often fall short in Kubernetes environments. Consider a recent incident at a major financial services company: Their production database VM suddenly showed signs of performance degradation. The traditional investigation process looked something like this:

1. Check VM metrics in KubeVirt dashboard
2. Review node resource utilization
3. Analyze storage metrics
4. Investigate guest OS metrics
5. Check impact on dependent services
6. Correlate timestamps across different metric sources
7. Draw conclusions from fragmented data

This manual process typically takes hours, requires multiple context switches between tools, and often misses crucial correlations that could lead to faster resolution. Meanwhile, dependent services degrade, and business impact compounds by the minute.

The Hidden Costs of Manual Investigation

The true cost of traditional VM performance troubleshooting extends far beyond just the immediate incident:

• Engineering Time: Senior engineers spend hours manually correlating data across different layers of the stack
• Business Impact: Extended resolution times mean longer service degradation
• Team Burnout: Complex investigations at odd hours contribute to SRE team fatigue
• Missed Patterns: Without systematic analysis, recurring patterns often go unnoticed
• Knowledge Gap: Detailed investigation steps often remain undocumented, making knowledge transfer difficult

Enter Hawkeye: Your AI-Powered VM Performance Expert

Hawkeye transforms this investigation process through its unique ability to simultaneously analyze and correlate data across your entire stack. Let’s look at how Hawkeye handled the same database VM performance incident:

Within minutes of the initial alert, Hawkeye had:

• Identified CPU throttling at 98% of allocated limits
• Correlated high IO wait times (45ms) with storage IOPS throttling
• Detected memory pressure despite adequate allocation
• Quantified the impact on dependent services (35% increased latency)
• Generated a comprehensive analysis with actionable recommendations

But Hawkeye’s value goes beyond just speed. Its ability to understand the complex relationships between different layers of your infrastructure means it can identify root causes that might be missed in manual investigation. In this case, Hawkeye correlated the VM’s performance degradation with recent storage class QoS limits and memory balloon device behavior – connections that might take hours to discover manually.

The Transformed Workflow

With Hawkeye as part of your team, the investigation workflow changes dramatically:

1. Instant Context: Instead of jumping between dashboards, engineers start with a complete picture of the incident
2. Automated Correlation: Hawkeye automatically connects metrics across VM, host, storage, and service mesh layers
3. Clear Action Items: Each analysis includes specific, prioritized recommendations for resolution
4. Continuous Learning: Hawkeye builds a knowledge base of your environment, improving its analysis over time

Moving from Reactive to Proactive

The real power of Hawkeye lies in its ability to help teams shift from reactive troubleshooting to proactive optimization. By continuously analyzing your environment, Hawkeye can:

• Identify potential resource constraints before they cause incidents
• Recommend optimal VM resource allocations based on actual usage patterns
• Alert on subtle performance degradation patterns before they become critical
• Provide trend analysis to support capacity planning decisions

Getting Started with Hawkeye

Transforming your KubeVirt operations with Hawkeye is straightforward:

1. Connect your telemetry sources:
   • KubeVirt metrics
   • Kubernetes cluster metrics
   • Storage performance data
   • Service mesh telemetry
2. Configure your preferred incident management integration
3. Start receiving AI-powered insights immediately

The Future of VM Operations

As virtualization continues to evolve with technologies like KubeVirt, the old ways of monitoring and troubleshooting no longer suffice. Hawkeye represents a fundamental shift from manual correlation to AI-driven analysis, transforming how SRE teams manage virtual infrastructure and enabling them to focus on strategic improvements rather than reactive firefighting.

Ready to transform your KubeVirt operations? Contact us to see how Hawkeye can become your team’s AI-powered SRE teammate and help your organization tackle the complexity of modern virtualization environments.

Follow
Hawkeye

How SRE teams are revolutionizing application debugging with Hawkeye

The PagerDuty alert comes in at the worst possible time: “Maximum pod_container_status_waiting_reason_crash_loop_back_off GreaterThanThreshold 0.0”. Your application is caught in the dreaded CrashLoopBackOff state. While your CloudWatch logs capture every crash and restart, the sheer volume of error data makes finding the root cause feel like solving a puzzle in the dark.

The Traditional Debug Dance

In a modern Kubernetes environment, SREs have powerful tools at their disposal. CloudWatch diligently captures every log line, metrics flow into Prometheus, and your APM solution tracks every transaction. Yet, when faced with a CrashLoopBackOff, these tools often present more questions than answers.

A typical investigation starts with CloudWatch Logs, where you’re immediately confronted with thousands of entries across multiple restart cycles. You begin the methodical process of piecing together the story: the first crash occurrence, any changes in error messages between restarts, and potential patterns in the pod’s behavior before each failure.

Next comes the metrics investigation in Prometheus. You pull up graphs of memory usage, CPU utilization, and network activity, looking for correlations with the crash timing. Everything looks normal, which is both reassuring and frustrating – no obvious resource constraints to blame.

Then it’s time to dig deeper. You pull up the Kubernetes events, checking for any cluster-level issues that might be affecting the pod. You review recent deployments in your CI/CD pipeline, wondering if a configuration change slipped through code review. Each step adds more data but doesn’t necessarily bring you closer to a resolution.

Why CrashLoopBackOff Defies Traditional Analysis

What makes CrashLoopBackOff particularly challenging isn’t a lack of data – it’s the complexity of piecing together the right narrative from overwhelming amounts of information. Modern observability tools give us unprecedented visibility into our systems, but they don’t inherently understand the relationships between different signals.

A single CrashLoopBackOff incident typically spans multiple dimensions:

The application layer might show clean logs right up until the crash, missing the crucial moments that would explain the failure. System metrics might appear normal because the pod isn’t running long enough to establish baseline behavior. Kubernetes events capture the restarts but not the underlying cause.

Even more challenging is the ripple effect through your microservices architecture. A crashing service can trigger retry storms from dependent services, creating noise that obscures the original problem. Your observability tools faithfully record every detail, but understanding the cascade of events requires deep system knowledge and careful analysis.

Hawkeye: Bringing Context to Chaos

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to process logs faster than humans – it’s how it understands the complex relationships between different parts of your system. When Hawkeye analyzes a CrashLoopBackOff, it doesn’t just look at the logs in isolation. It builds a comprehensive narrative by:

Simultaneously analyzing data across multiple observability systems and environments. While humans must context-switch between different tools and mentally piece together timelines, Hawkeye can instantly correlate events across your entire observability stack. What might take an SRE hours of checking CloudWatch logs, then Prometheus metrics, then deployment histories, and then trying to build a coherent timeline, Hawkeye can process in seconds by analyzing all these data sources in parallel.

Analyzing the impact on your entire service mesh. Instead of just focusing on the crashing pod, Hawkeye maps out how the failure ripples through your system, helping identify whether the crash is a cause or symptom of a broader issue.

Correlating deployment changes with system behavior. Hawkeye doesn’t just know what changed – it understands how those changes interact with your existing infrastructure and configuration.

Real World Impact

For teams that have integrated Hawkeye into their operations, the transformation goes beyond faster resolution times. Engineers report a fundamental shift in how they approach application reliability:

Instead of spending hours reconstructing what happened during an incident, they can focus on implementing Hawkeye’s targeted recommendations for system improvement. The mean time to resolution for CrashLoopBackOff incidents has dropped from hours to minutes, but more importantly, repeat incidents have become increasingly rare as Hawkeye helps teams address root causes rather than symptoms.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of crash analysis while your team focuses on innovation.

Follow
Hawkeye

The Morning Routine You Know Too Well

It’s 8:45 AM, and you’re settling in with your first coffee of the day. Your inbox is already filled with Commvault alerts from overnight backups. Three failed jobs need investigation, two clients are showing performance degradation, and someone from the development team is urgently requesting a restore from last week’s backup. Just another morning in the life of an SRE managing backups.

If this scenario feels painfully familiar, you’re not alone. Managing enterprise backup operations with Commvault is a critical but increasingly complex responsibility. As organizations generate more data across diverse environments, the challenge of ensuring reliable backups while maintaining efficiency has never been greater.

The Hidden Costs of Traditional Backup Management

The current approach to backup management often follows a predictable but inefficient pattern. SREs spend hours each day:

• Manually reviewing job failure notifications
• Investigating performance bottlenecks
• Correlating issues across multiple clients
• Managing storage capacity across various targets
• Responding to urgent restore requests
• Generating and analyzing compliance reports

While Commvault’s Command Center provides robust capabilities, the reality is that most teams still rely heavily on manual investigation and tribal knowledge to maintain their backup operations. This approach isn’t just time-consuming—it’s increasingly unsustainable as data volumes grow and recovery time objectives become more stringent

Rethinking Backup Management for the AI Era

What if your morning routine looked different? Imagine walking in to find:

• Common backup failures already reviewed and diagnosed with action plans defined
• Performance issues proactively analyzed and solution identified
• Capacity problems predicted and prevention ready to be performed
• Restore requests automatically triaged and prioritized

This isn’t a distant future—it’s the reality for teams that have embraced AI-powered operations using Hawkeye. By combining Commvault’s robust backup capabilities with an intelligent, vigilant, and tireless AI operations teammate, organizations are transforming how they protect their data.

The Power of an GenAI powered SRE for Backup Operations

Modern backup management requires more than just automation—it needs intelligence. Here’s how an AI teammate changes the game:

Proactive Issue Resolution

• Automatically detects and diagnoses common backup failures before they impact operations, providing clear guidance to your team and saving them hours of work every day.
• Identifies underlying causes of recurring issues, allowing operations to improve over time by freeing your team to become more proactive instead of being swamped with their daily alert backlog.

Intelligent Investigation

• Correlates issues across multiple backup jobs and clients
• Analyzes performance trends to prevent degradation
• Provides detailed root cause analysis with specific remediation steps
• Learns from each investigation to improve future responses

A Day in the Life of working with GenAI powered SRE 

Let’s revisit that morning scenario, but this time with   GenAI powered SRE: 

8:45 AM: You arrive to find that overnight backup issues have been automatically categorized and prioritized. Of the three failed jobs:

• Two were automatically investigated and a recommendation for resolution was provided.
• One requires additional collaboration on the investigation with a human
• The performance degradation issues were reviewed and Dawkeye identified a pattern based on prior performance issues. The resolution that was extracted from prior incidents has been provided.
• The restore request has been validated and queued based on priority

Instead of spending hours on routine investigation, you can focus on strategic improvements to your backup infrastructure while your AI teammate handles the routine operations.

The Future of Backup Management

As data continues to grow exponentially, the traditional approach to backup management becomes increasingly unsustainable. By embracing AI as a teammate in your backup operations, you’re not just solving today’s challenges—you’re preparing for tomorrow’s demands.

The future of backup management lies in the perfect balance of human expertise and AI capabilities. While your AI teammate handles the routine operations, you can focus on strategic initiatives that drive value for your organization.

Ready to transform your Commvault backup operations? Let’s discuss how an AI teammate can help you achieve more reliable, efficient, and proactive backup management.

Follow
Hawkeye

How SRE teams are transforming CPU utilization management with AI

A PagerDuty alert breaks the silence: “GreaterThanUpperThreshold” on node CPU utilization. Your Kubernetes cluster is experiencing severe CPU spikes, and although your observability stack is capturing every metric, the root cause remains elusive. With applications spread across dozens of namespaces and hundreds of pods, finding the culprit means correlating data across multiple monitoring systems and timeframes.

The Resource Investigation Reality

In a modern Kubernetes environment, CPU spike investigation isn’t hampered by a lack of data – quite the opposite. Your observability stack provides multiple lenses into the problem:

CloudWatch Container Insights shows node-level CPU metrics spiking to concerning levels. Prometheus captures detailed pod-level resource utilization across your cluster. Your APM solution tracks application performance metrics. Your logging platform collects application logs that might indicate why certain components are consuming more resources than usual.

Yet this wealth of data often makes the investigation more complex rather than simpler. A typical troubleshooting session involves constantly switching between these different tools and mentally correlating their data:

You start in CloudWatch, identifying the affected nodes and the timing of the spikes. Switching to Prometheus, you examine pod-level metrics, trying to match spike patterns with specific workloads. Your APM tool shows increased latency in several services, but is it cause or effect? The logging platform shows increased error rates in some components, but do they align with the CPU spikes?

Each tool tells part of the story, but piecing together the complete narrative requires extensive context switching and complex mental correlation of events across different timelines and granularities.

Why CPU Spikes Challenge Traditional Analysis

What makes CPU spike investigation particularly demanding isn’t just finding the high-CPU workload – it’s understanding the broader context and impact across your entire system. A spike in one component can trigger a cascade of effects:

Increased CPU usage in one pod might cause the Kubernetes scheduler to rebalance workloads across nodes. This rebalancing can trigger further spikes as pods migrate and initialize. Meanwhile, resource contention might cause other services to slow down, leading to retry storms that amplify the problem.

Your observability tools capture all of this activity faithfully, but understanding the sequence of events and cause-effect relationships requires simultaneously analyzing multiple data streams and understanding complex system interactions.

Hawkeye: Bringing Clarity to Resource Analysis

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to collect metrics faster than humans – it’s how it analyzes data streams in parallel to build a comprehensive understanding of system behavior. While an SRE would need to manually switch between CloudWatch, Prometheus, logging tools, and application metrics to piece together the story, Hawkeye simultaneously processes all these data sources to identify patterns and correlations.

This parallel processing capability allows Hawkeye to quickly identify cause-and-effect relationships that might take hours for humans to discover. By analyzing metrics, logs, events, and application data simultaneously, Hawkeye can trace how a CPU spike in one component ripples through your entire system.

Real World Impact

For teams using Hawkeye, the transformation goes beyond faster incident resolution. Engineers report a fundamental shift in how they approach resource management:

Instead of spending hours correlating data across different observability tools, they can focus on implementing systematic improvements based on Hawkeye’s comprehensive analysis. The mean time to resolution for CPU-related incidents has dramatically decreased, but more importantly, teams can now prevent many issues before they impact production by acting on Hawkeye’s early warnings and recommendations.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of CPU Spike analysis while your team focuses on innovation.

Follow
Hawkeye

How SRE teams are transforming error analysis with Hawkeye

Your monitoring dashboard explodes with alerts as your web application in the ‘checkout’ namespace starts generating a torrent of errors. CloudWatch is capturing every error, your APM solution is tracking every failed request, and Prometheus metrics show increasing error rates – but with different error types, status codes, and messages flooding in, finding the signal in this noise feels like trying to drink from a fire hose.

The Modern Error Analysis Challenge

In today’s microservices environments, error investigation occurs within sophisticated observability stacks. Your APM solution traces every request, CloudWatch captures detailed error logs, Prometheus tracks error metrics, and your logging platform aggregates errors across services. Yet when error floods occur, this wealth of information often obscures rather than illuminates the root cause.

A typical investigation unfolds across multiple systems:

You start in your APM tool, watching transaction traces light up with errors. The service dependency map shows cascading failures, but which service triggered the cascade? Switching to CloudWatch, you wade through error logs trying to identify patterns. Each log entry adds more context but also more complexity – different error types, varying stack traces, and multiple affected components.

The investigation branches out as you attempt to correlate data:

• APM traces show increased latency preceding the errors
• Prometheus metrics indicate growing error rates across multiple services
• Kubernetes events reveal pods restarting due to failed health checks
• Load balancer metrics show increased 5xx responses
• Individual service logs contain different error messages and stack traces

Each tool captures a piece of the puzzle, but understanding how these pieces fit together requires constantly switching contexts and mentally correlating events across different services, timelines, and abstraction layers.

Why Error Floods Challenge Traditional Analysis

What makes error flood analysis particularly demanding isn’t just the volume of errors – it’s understanding the relationships and root causes across a distributed system. Error patterns often manifest in complex ways:

An error in one microservice might trigger retry storms from dependent services, amplifying the error rate. Rate limiting kicks in, causing a new wave of errors with different signatures. Circuit breakers trip, changing the error patterns yet again. Each layer of your reliability mechanisms, while protecting the system, also transforms the error signatures and complicates the analysis.

Your observability tools dutifully record every error, metric, and trace, but understanding the sequence of events and cause-effect relationships requires simultaneously analyzing multiple data streams while understanding service dependencies, reliability patterns, and failure modes.

Hawkeye: Your Error Analysis Expert

Here’s how Hawkeye transforms this investigation:

The Hawkeye Difference

What sets Hawkeye apart isn’t just its ability to aggregate errors – it’s how it analyzes error patterns across multiple observability systems simultaneously. While an SRE would need to manually correlate data between APM traces, error logs, metrics, and service dependencies, Hawkeye processes all these data streams in parallel to quickly identify patterns and causality chains.

This parallel analysis capability allows Hawkeye to discover cause-and-effect relationships that might take hours for humans to uncover. By simultaneously examining service behavior, error patterns, and system metrics, Hawkeye can trace how an error in one component cascades through your entire system.

Real World Impact

For teams using Hawkeye, the transformation goes beyond faster error resolution. Engineers report a fundamental shift in how they approach system reliability:

Instead of spending hours correlating data across different monitoring tools during incidents, they can focus on implementing systematic improvements based on Hawkeye’s comprehensive analysis. The mean time to resolution for error floods has decreased dramatically, but more importantly, teams can prevent many cascading failures entirely by acting on Hawkeye’s early warnings and recommendations.

Implementation Journey

Integrating Hawkeye into your Kubernetes environment is straightforward:

1. Connect your existing observability tools – Hawkeye enhances rather than replaces your current monitoring stack
2. Configure your preferred incident response workflows
3. Review Hawkeye’s incident analysis, drill down with questions, and implement recommendations.

Scale your team and improve morale by transforming your approach to application debugging from reactive investigation to proactive improvement. Let Hawkeye handle the complexity of taming the error flood while your team focuses on innovation.

Follow
Hawkeye

How SRE teams are evolving their Kubernetes observability with AI

Picture this: Your team has invested countless hours building the perfect Grafana dashboards for your Kubernetes clusters. You’ve got detailed panels tracking CPU, memory, network metrics, and carefully configured alerts through Prometheus. Yet when a critical service degradation hits, your engineers still spend precious hours digging through multiple dashboards, correlating metrics, and scanning through logs trying to piece together what’s happening.

If this sounds familiar, you’re not alone. While Grafana provides powerful visualization capabilities and Prometheus offers robust metrics collection, the exponential growth in Kubernetes complexity has created a fundamental challenge: The human capacity to process and correlate this vast amount of telemetry data simply can’t keep pace with modern cloud-native operations.

The Hidden Costs of Dashboard-Driven Operations

Today’s Kubernetes environments generate an overwhelming amount of telemetry data. A typical production cluster might track:

• Thousands of metrics across hundreds of pods
• Multiple node pools with varying resource configurations
• Complex autoscaling behaviors
• Intricate service dependencies
• Network policies and security configurations

Traditional approaches rely on pre-built dashboards and static alert thresholds. But this creates several challenges:

1. Context Blindness: While your Grafana dashboard might show high CPU utilization, understanding whether this is caused by a misconfigured horizontal pod autoscaler, resource limits, or a noisy neighbor requires correlating data across multiple sources.
2. Alert Fatigue: Static thresholds lead to both false positives and missed issues. A spike in pod restarts might be normal during a deployment but critical during steady state.
3. Investigation Overhead: Engineers spend valuable time switching between different dashboards, metrics, and log sources to understand the full picture.

Enter Hawkeye: Your AI-Powered Kubernetes Expert

Instead of replacing your existing Grafana and Prometheus setup, Hawkeye acts as an intelligent layer that understands the complex relationships in your Kubernetes environment. Here’s how it transforms operations:

Intelligent Investigation

When a potential issue arises, Hawkeye automatically:

• Correlates metrics across your entire observability stack
• Analyzes historical patterns to identify anomalies
• Examines pod events, scheduler decisions, and resource utilization
• Reviews recent configuration changes and deployments
• Provides a comprehensive root cause analysis with clear remediation steps

Real-World Example: Pod Scheduling Issues

Consider a common scenario: Services are experiencing increased latency, and your Grafana dashboards show elevated pod pending metrics. Traditional investigation would require:

1. Checking node resource utilization across the cluster
2. Examining scheduler logs for failed binding attempts
3. Reviewing pod events and specifications
4. Analyzing historical trends to understand capacity patterns
5. Investigating potential configuration changes

Hawkeye transforms this process by:

• Instantly correlating pod scheduling failures with resource constraints
• Identifying patterns in node utilization and pod placement
• Analyzing the impact of recent deployment changes
• Suggesting specific remediation steps, such as adjusting resource quotas or scaling node pools
• Learning from each investigation to provide increasingly precise insights

Beyond Reactive Monitoring

Where Hawkeye truly shines is in its ability to move beyond reactive monitoring to proactive optimization:

1. Predictive Capacity Planning: By analyzing historical trends and seasonal patterns, Hawkeye can recommend node pool adjustments before resource constraints impact services.
2. Configuration Optimization: Instead of waiting for issues to occur, Hawkeye continuously analyzes pod specifications and resource utilization to suggest improvements to requests, limits, and HPA configurations.
3. Pattern Recognition: As Hawkeye learns your environment’s normal behavior, it can identify potential issues before they trigger traditional alerting thresholds.

The Transformed Workflow

With Hawkeye, your team’s daily operations shift dramatically:

• Engineers start with a comprehensive analysis rather than raw metrics
• Routine investigations are automated, freeing up time for strategic work
• Knowledge is captured and shared consistently across the team

Getting Started

Implementing Hawkeye alongside your existing Grafana and Prometheus stack is straightforward:

1. Connect your telemetry sources:

• Prometheus metrics
• Container logs
• Kubernetes events
• Configuration management tools

1. Define your operational preferences and SLOs
2. Start benefiting from Hawkeye’s intelligent analysis and recommendations

The Future of Kubernetes Operations

As Kubernetes environments continue to grow in complexity, the traditional dashboard-centric approach to operations becomes increasingly unsustainable. By combining Hawkeye’s AI-powered analysis with your existing Grafana and Prometheus infrastructure, teams can transform from reactive firefighting to proactive optimization.

Ready to see how Hawkeye can transform your Kubernetes operations? Contact us to learn how we can help your team break free from dashboard limitations and achieve new levels of operational excellence.

Follow Hawkeye

SRE teams are constantly battling against time when it comes to incident resolution. Every minute of downtime can translate to significant financial losses and reputational damage.

The numbers tell a striking story: SRE teams today spend up to 70% of their time investigating and responding to incidents, leaving precious little bandwidth for innovation and systemic improvements. In a world where system complexity grows exponentially, this reactive approach isn’t just unsustainable—it’s holding organizations back from their true potential.

This reality exists despite having powerful tools like Datadog and ServiceNow at our disposal. These platforms represent the pinnacle of modern observability and incident management, yet teams still struggle to keep pace with increasing demand. The challenge isn’t with the tools themselves—it’s with how we use them. Adding to this challenge, most organizations have more than one observability tool which means SRE teams rarely get the benefit of having all the information they need in one place.

The Challenge of Fragmented Observability

While Datadog and ServiceNow are powerful tools individually, many organizations face challenges in integrating them effectively. Often, different teams prefer different tools, leading to a fragmented observability landscape. Application metrics might reside in Datadog, while infrastructure logs are sent to CloudWatch, and security events are tracked in another platform. This fragmentation forces engineers to navigate multiple interfaces, manually correlate data, and waste valuable time piecing together a complete picture of an incident.

What is Datadog?

Datadog is a cloud-based monitoring and analytics platform that provides organizations with real-time visibility into their IT infrastructure. It enables the monitoring of servers, databases, applications, and cloud services, offering insights into performance and potential issues. Datadog excels at collecting, searching, and analyzing traces across distributed architectures, which is crucial for maintaining system health and efficiency. It offers a wide range of capabilities, including application performance monitoring (APM), cloud and on-premise monitoring, and over 200 vendor-supported integrations. Learn more.

What is ServiceNow?

ServiceNow is a cloud-based platform that streamlines workflows across various departments within an organization. It specializes in IT service management (ITSM), IT operations management (ITOM), and IT business management (ITBM). ServiceNow excels at automating tasks, managing incidents, and tracking progress against service level agreements (SLAs). It provides a centralized system for managing IT operations, enabling efficient incident response and resolution. Learn more.

Hawkeye: Bridging the Gap with Generative AI for Datadog ServiceNow Integration

Hawkeye acts as an intelligent bridge between Datadog and ServiceNow, leveraging the power of Generative AI to automate tasks, enhance insights, and streamline workflows. Here’s how Hawkeye transforms the way SREs work with Datadog ServiceNow integration:

Automated Data Correlation

Hawkeye automatically correlates data from Datadog and ServiceNow, eliminating the need for manual cross-referencing. For example, when an alert is triggered in Datadog, Hawkeye can automatically create an incident in ServiceNow, populate it with relevant context from Datadog, and assign it to the appropriate team.

This multi-tool correlation happens in seconds, not the minutes or hours it would take a human engineer to manually gather and analyze data from each platform. More importantly, Hawkeye learns the relationships between different data sources, understanding which tools typically provide the most relevant information for specific types of incidents.

Intelligent Alerting

Hawkeye analyzes historical incident data and learns to identify patterns and anomalies. This allows it to filter out noise and prioritize alerts based on severity and context, reducing alert fatigue and ensuring that critical issues are addressed promptly. This is particularly valuable in a Datadog ServiceNow integration, where a high volume of alerts can easily overwhelm SRE teams.

Root Cause Analysis

Hawkeye goes beyond simply correlating data by performing automated root cause analysis. By analyzing metrics, logs, and traces from Datadog, combined with incident data from ServiceNow, Hawkeye can pinpoint the root cause of an issue, accelerating resolution times. This capability is crucial for efficient Datadog ServiceNow event management.

Automated Remediation

For common incidents, Hawkeye can automatically trigger remediation actions, such as restarting services or scaling resources. This minimizes downtime and frees up SREs to focus on more complex issues. This automation capability further enhances the value of Datadog ServiceNow integration.

The Transformed Workflow: Streamlining Datadog and ServiceNow Incident Response

Let’s consider a scenario where a critical application experiences a sudden spike in latency. In a traditional workflow, an SRE would need to:

1. Receive an alert from Datadog.
2. Log in to Datadog to investigate the issue.
3. Manually correlate metrics, logs, and traces to identify the root cause.
4. Create an incident in ServiceNow.
5. Update the incident with findings from Datadog.
6. Assign the incident to the appropriate team.

With Hawkeye, this process is streamlined and automated:

1. Hawkeye receives the alert from Datadog.
2. Hawkeye automatically correlates the alert with relevant data in Datadog and ServiceNow.
3. Hawkeye performs root cause analysis and identifies the source of the latency.
4. Hawkeye creates an incident in ServiceNow, populates it with relevant context, and assigns it to the appropriate team.
5. If the issue is common, Hawkeye may even trigger automated remediation actions.

Benefits of Datadog ServiceNow Integration with Hawkeye

The benefits of using Hawkeye extend beyond simply improving incident response times. By automating tasks and providing intelligent insights, Hawkeye empowers SREs to:

Reduce alert fatigue. By filtering out noise and prioritizing alerts, Hawkeye helps SREs focus on the most critical issues.
Accelerate incident resolution. Automated data correlation and root cause analysis help SREs resolve incidents faster.
Improve system stability. Predictive insights and automated remediation help prevent incidents and maintain system uptime.
Increase efficiency. Automation frees up SREs from tedious manual tasks, allowing them to focus on more strategic work.
Enhance collaboration. By providing a centralized platform for incident management and data analysis, Hawkeye improves collaboration between teams.

Getting Started

Hawkeye represents a significant step forward in the evolution of IT operations management. By harnessing the power of Generative AI, Hawkeye transforms how SREs interact with Datadog and ServiceNow, enabling them to work more efficiently, resolve incidents faster, and proactively maintain system stability.

Hawkeye’s flexible integration capabilities mean you can connect it to your entire observability stack, creating a unified intelligence layer across all your tools.

Take the Next Step

Ready to experience the power of GenAI for your incident management workflows? See the live demo and contact us to learn more about how Hawkeye can help you transform your Datadog and ServiceNow integration and take your SRE team to the next level.

 

How forward-thinking SRE teams are revolutionizing their toolchain with Hawkeye

A Fortune 500 financial services company faced a common challenge: despite significant investments in Splunk for log analytics and ServiceNow for incident management, their SRE team was drowning in alerts. With over 100,000 daily log entries and dozens of critical services to monitor, their engineers spent countless hours switching between Splunk’s powerful search interface and ServiceNow’s incident management platform. The traditional solution would have been to hire more engineers—but in today’s competitive market, that wasn’t just expensive; it was nearly impossible.

Their transformation began with a simple question: What if GenAI could bridge the gap between these powerful platforms? Within three months of implementing Hawkeye, their mean time to resolution (MTTR) plummeted by 45%, freeing their SRE team to finally focus on proactive improvements and innovation.

The Current Landscape: Powerful Tools, Complex Workflows

This story isn’t unique. Across industries, organizations are are realizing that the problem isn’t the tools themselves, but the lack of a unified, intelligent way to leverage them. Different teams often prefer different tools, leading to scenarios where application logs might live in Splunk, while cloud metrics flow to CloudWatch, and APM data resides in Datadog. This fragmentation means engineers must master multiple query languages and mentally correlate data across platforms to get a complete picture of system health.

Splunk and ServiceNow represent the gold standard in their respective domains. Splunk’s powerful search processing language (SPL) can slice through terabytes of log data to find the needle in the haystack, while ServiceNow brings structure and automation to incident management workflows.

What is Splunk used for?

Splunk excels at capturing, indexing, and correlating machine-generated data – logs, metrics, traces – turning raw information into valuable insights. It’s a powerhouse for:

• Security Information and Event Management (SIEM): Detecting and responding to security threats.
• IT Operations Management: Monitoring infrastructure and application performance.
• Business Analytics: Uncovering trends and patterns to drive better decision-making.

Learn more.

What is ServiceNow?

ServiceNow is the backbone of IT service management (ITSM), streamlining workflows and automating tasks across the enterprise. It’s a central hub for:

• Incident Management: Tracking, prioritizing, and resolving IT incidents.
• Problem Management: Investigating and addressing the root causes of incidents.
• Change Management: Controlling and managing changes to IT systems.

Learn more.

Enter Hawkeye: Your Integration-Savvy GenAI Teammate for Splunk and ServiceNow

Consider a different approach. Instead of humans serving as the integration layer between tools, Hawkeye acts as an intelligent orchestrator that not only bridges Splunk and ServiceNow but can pull relevant information from your entire observability ecosystem. This isn’t about replacing any of your existing tools—it’s about having a GenAI powered SRE that maximizes their collective value and helps your team deliver results and scale.

Beyond Simple Integration: How Hawkeye Enhances Splunk and ServiceNow

Hawkeye’s approach to tool integration goes far beyond simple API connections. When investigating an incident, it can simultaneously analyze Splunk logs using complex SPL queries, correlate findings with historical ServiceNow tickets, and gather context from other observability tools—all in seconds. More importantly, it learns from each interaction, building a knowledge base that makes future investigations even more efficient.

What makes Hawkeye particularly powerful with Splunk is its ability to:

• Automatically generate and refine SPL queries based on incident context
• Correlate log patterns across different time periods and services
• Identify relevant log entries without requiring exact search terms
• Transform raw log data into actionable insights. Hawkeye doesn’t just show you logs; it provides clear, concise summaries, highlights critical events, and suggests potential solutions.

The Transformed Workflow: Streamlining Splunk and ServiceNow Incident Response

Hawkeye revolutionizes incident response by streamlining workflows and empowering engineers with AI-driven insights.

Traditional workflows require engineers to:

1. Receive a ServiceNow ticket
2. Construct multiple Splunk queries
3. Analyze log patterns
4. Correlate findings across tools
5. Document everything back in ServiceNow

With Hawkeye, engineers instead start with a unified view of the issue and all the information needed to resolve it in one coherent root cause analysis. Routine issues are easily resolved by implementing the recommended actions, while complex problems come with detailed investigation summaries that already include relevant data from across your observability stack.

Hawkeye Workflow:

1. An incident is reported in ServiceNow.
2. Hawkeye automatically analyzes the incident, generates SPL queries, and retrieves relevant data from Splunk and other integrated tools.
3. Hawkeye correlates findings, identifies root causes, and provides actionable recommendations.
4. Engineers review Hawkeye’s analysis, implement solutions, and focus on preventing future occurrences.

This shifts the engineer’s role from data gatherer to strategic problem solver.

The Future of SRE Work: From Survival to Strategic Impact

The transformation Hawkeye brings to SRE teams extends far beyond technical efficiency. In today’s competitive landscape, where experienced SRE talent is both scarce and expensive, organizations face mounting pressure to maintain reliability while controlling costs. The traditional response—hiring more engineers—isn’t just expensive; it’s often not even possible given the limited talent pool.

Hawkeye fundamentally changes this equation. By automating routine investigations and providing intelligent analysis across your observability stack, it effectively multiplies the capacity of your existing team. This means you can handle growing system complexity without proportionally growing headcount. More importantly, it transforms the SRE role itself, addressing many of the factors that drive burnout and turnover:

• Engineers spend more time on intellectually engaging work like architectural improvements and capacity planning, rather than repetitive investigations. 
• The dreaded 3 AM wake-up calls become increasingly rare as Hawkeye handles routine issues autonomously (*roadmap, today it recommends an action plan). 
• New team members come up to speed faster, learning from Hawkeye’s accumulated knowledge base, and cross-training becomes easier as Hawkeye provides consistent, comprehensive investigation summaries.

For organizations, this translates directly to the bottom line through reduced recruitment costs, higher retention rates, and the ability to scale operations without scaling headcount. More subtly, it creates a virtuous cycle where happier, more engaged engineers deliver better systems, leading to fewer incidents and more time for innovation.

Getting Started

Implementing Hawkeye alongside your existing tools is a straightforward process that begins paying dividends immediately. While this blog focuses on Splunk and ServiceNow, Hawkeye’s flexible integration capabilities mean you can connect it to your entire observability stack, creating a unified intelligence layer across all your tools.

Take the Next Step

Ready to transform your fragmented toolchain into a unified, intelligent operations platform? Check our demo or contact us to see how Hawkeye can become your team’s AI-powered SRE teammate and help your organization move from reactive to proactive operations.