Kubernetes Observability Ideal Dashboard Elements

Author

Govardhana M K
January 30, 2025

TechOps Examples

Hey — It's Govardhana MK 👋

Along with a use case deep dive, we identify the remote job opportunities, top news, tools, and articles in the TechOps industry.

👋 Before we begin... a big thank you to today's sponsor LOGZ

🔥 Logz.io Webinar - How To Optimize Your Observability Spend in 2025

Join Logz.io for an exclusive webinar on February 5th featuring CTO and Co-Founder, Asaf Yigal, and Customer Success Engineer, Seth King.

This live, 'Ask Me Anything'-style session will focus on implementing strategies to maintain high-quality observability at a lower cost.

🔍 Key Takeaways:

→ Optimize Observability Spending: Cut costs without sacrificing performance.

→ Data Retention and Cost Management: Balance compliance and costs effectively.

→ AI-Driven Efficiency: Automate insights to boost productivity.

IN TODAY'S EDITION

🧠 Use Case

  • Kubernetes Observability Ideal Dashboard Elements

🚀 Top News

👀 Remote Jobs

📚️ Resources

📢 Reddit Threads

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🧠 USE CASE

Kubernetes Observability Ideal Dashboard Elements

There are multiple tools and runbooks available for setting up observability in Kubernetes, yet defining what elements to track remains an overlooked challenge.

What to measure and highlight on an observability dashboard is where I see teams frequently missing the mark.

When you measure the wrong things, the insights you derive mislead you, and the outcome is far from the ideal place you originally planned for.

I've made this illustration to highlight how it should be for maximum effectiveness.

As we captured the crucial observability elements, let's focus on common Pitfalls in Kubernetes Observability

1. Observability Stops at Monitoring, No Actionable Insights

Seeing spikes in CPU or latency? Cool. But what do you do next?

Alerts should have built in playbooks. If you see X, try Y.

Include remediation steps in your dashboard.

2. Alert Fatigue & Noisy Dashboards

Alerting on every minor fluctuation. This leads to engineers ignoring alerts altogether.

Use rate of change alerts instead of absolute thresholds.

Example: 5x increase in error rate instead of errors > 100.

3. No Correlation Between Logs, Metrics, and Events

Debugging a failing pod?

You check Prometheus for resource spikes, ELK for logs, kubectl get events for Kubernetes events - all in different places.

Use tools like Loki, Jaeger, and OpenTelemetry to link logs, traces, and metrics in one view.

4. Ignoring the Cost of Observability

Pulling high resolution data every second for everything? That bloats your monitoring stack and racks up cloud bills.

Optimize scraping intervals. Do you need per second data for everything? Likely not.

Kubernetes observability should drive clarity and action.

Start by defining what you need to observe.

Get these elements right, and the rest falls into place.

To take this further, we are bringing an exclusive webinar on February 5th featuring Logz CTO and Co-Founder, Asaf Yigal, and Customer Success Engineer, Seth King.

This live, 'Ask Me Anything' style session will focus on implementing strategies to maintain high quality observability at a lower cost.

I run a DevOps and Cloud consulting agency and have helped 17+ businesses, including Stanford, Hearst Corporation, CloudTruth, and more.

What people say after working with me: Genuine testimonials

When your business needs my services, book a free 1:1 business consultation.

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