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How to defeat downtime with Observability?


In today’s world, the essential ingredient for the success of an organization is the ability to reduce downtime. If not handled properly, it interrupts the company’s growth, impacts customer satisfaction, and could result in significant monetary losses. Resolutions can also be difficult when the correct data is unavailable, thus prolonging the downtime. This affects the SLA and decreases the product’s reliability in the market.

The best way to deal with downtime is to avoid its occurrence. Data teams should have access to tools and measures to prevent such an incident by detecting it even before it happens. This kind of transparency can be achieved using Observability. By implementing Observability, teams can manage the health of their data pipeline and dramatically reduce downtime and resolution time.

What is Observability? 

Introduction to Observability

Observability is the ability to measure the internal status of a system by examining its outputs. A system is highly observable if it does not require additional coding and services to assess and analyze what’s going on. During downtime, it is of utmost importance to determine which part of the system is faulty at the earliest possible time. 

Three Pillars of Observability

Three Pillars of Observability

The three pillars that must be considered simultaneously to obtain Observability are logs, metrics, and traces. When you combine these three “pillars,” a remarkable ability to understand the whole state of your system emerges. Let us learn more about these pillars:

Logs are the archival records of your system functions and errors. They are always time-stamped and come in either binary or plain text and a structured format that combines text and metadata. Logs allow you to look through and see what went wrong and where within a system.

Metrics can be a wide range of values monitored over some time. Metrics are often vital performance indicators such as CPU capacity, memory usage, latency, or anything else that provides insights into the health and performance of your system. The changes in these metrics allow teams to understand the system’s end performance better. Metrics offer modern businesses a measurable means to improve the user experience.

Traces are a method to follow a user’s journey through your application. Trace documents the user’s interaction and requests within the system, starting from the user interface to the backend systems and then back to the user once their request is processed. 

A system’s overall performance can be maintained and enhanced by implementing the three pillars of Observability, i.e., logs, metrics, and traces. As distributed systems become more complex, these three pillars give IT, DevSecOps, and SRE teams the ability to access real-time insight into the system’s health. Areas of degrading health can be prioritized for troubleshooting before impacting the system’s performance. 

What are the benefits of Observability?

Observability tools are not only a requirement but a necessity in this fast-paced data-driven world. Key benefits of Observability are:

  1. Detecting an anomaly before it impacts the business, thus preventing monetary losses.
  2. Speed up resolution time and meet customer SLAs
  3. Reduce repeat incidents
  4. Reduce escalations
  5. Improve collaboration between data teams (engineers, analysts, etc.)
  6. Increase trust or reliability in data
  7. Quicker decision making

Observability Use-cases

Observability is essential because it gives you greater control over complex systems. Simple systems have fewer moving parts, making them easier to manage. Monitoring CPU, memory, databases, and networking conditions are usually enough to understand these systems and apply the appropriate fix.

Distributed systems have a far higher number of interconnected parts, so the number and types of failure are also higher. Additionally, distributed systems are constantly updated, and every change can create a new kind of failure. Understanding a current problem is an enormous challenge in a distributed environment, mainly because it produces more “unknown unknowns” than simpler systems. Because monitoring requires “known unknowns,” it often fails to address problems in these complex environments adequately.

Observability is better suited for the unpredictability of distributed systems, mainly because it allows you to ask questions about your system’s behavior as issues arise. “Why is X broken?” or “What is causing latency right now?” are a few questions that Observability can answer.

SREs often waste valuable time combing through heaps of data and identifying what matters and requires action. Rather than slowing down all operations with tedious, manual processes, Observability provides automation to identify which data is critical so SREs can quickly take action, dramatically improving productivity and efficiency, rather than slowing down all operations with tedious, manual processes.

Best practices to implement Observability

  • Monitor what matters most to your business to not overload your teams with alerts.
  • Collect and explore all of your telemetry data in a unified platform.
  • Determine the root cause of your application’s immediate, long-term, or gradual degradations.
  • Validate service delivery expectations and find hot spots that need focus.
  • Optimize the feedback loop between issue detection and resolution.

Observability tools

Features to consider while choosing the right tool

Observability tools have become critical to meeting operational challenges at scale. To get the best out of Observability implementation, you will need a reliable tool that enables your teams to minimize toil and maximize automation. Some of the key features to consider while choosing an application are:

  • Core features offered
  • Initial set-up experience
  • Ease of use 
  • Pricing
  • Third-party integrations
  • After-sales support and maintenance

List of tools

Considering the above factors, we have compiled a list of effective observability tools that can offer you the best results:

  • ContainIQ
  • SigNoz
  • Grafana Labs
  • DataDog
  • Dynatrace
  • Splunk
  • Honeycomb
  • LightStep
  • LogicMonitor
  • New Relic

Reporting for Observability

Skedler Reports helps Observability and SOC teams automate stakeholder reports in a snap without breaking the budget.


Reporting for Observability

With effective observability tools, you also need a reliable reporting tool that can deliver professional reports from these tools to your stakeholders regularly on time. If you use Grafana for Observability or Elastic Stack for SIEM, check out Skedler Reports. 

Skedler Reports helps Observability and SOC teams automate stakeholder reports in a snap without breaking the budget. You can test-drive Skedler for free and experience its value for your team. Click here to download Skedler Reports.

Is observability the future of systems monitoring?

As the pressure increases to resolve issues faster and understand the underlying cause of the problem, IT and DevOps teams need to go beyond reactive application and system monitoring.

They will need to dig deeper into the tiniest technical details of every application, system, and endpoint to witness the real-time performance and previous anomalies to correct repeat incidents.

A mature observability strategy can give you an insight into previous unknowns and help you more quickly understand why incidents occur. And as you continue on your observability journey and understand what and why things break, you’ll be able to implement increasingly automated and effective performance improvements that impact your company’s bottom line.

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