Observability has been gaining importance in big data IT because it not only tells you when downtime or an error happened but why it happened.

With the rise of new technologies like cloud, DevOps, microservices, containers, and virtualization, IT & DevOps teams are working on simultaneously boosting speed and reducing friction throughout the code to generation lifecycle.

IT & DevOps teams are firefighting complex new challenges every day. Facing such challenges, observability is crucial to completely grasp what is actually occurring in your business’s applications and systems. With observability, enterprises can fully optimize the performance of their big data stacks and IT environments and effectively maximize value in a way that isn’t possible with monitoring alone.

What is Observability?

Observability enables DevOps and IT teams to gather real-time information from various sources, including performance monitoring solutions and logs, to create a complete picture of their big data stacks and IT environments.

An accurate observability definition might say: Observability is the ability of IT and DevOps teams to fully understand what is going on within multi-layered architectures and take complete advantage of real-time performance data to optimize performance.

Observability makes it viable for developers and IT administrators to organize data from fragmented and disparate sources to derive reliable and actionable knowledge of the environment. DevOps and IT admins are empowered with an understanding of how deep and impactful performance issues are, enabling them to troubleshoot at a root level.

When big data stacks and IT infrastructure are observable, navigation from effect to cause becomes faster and simpler. Root cause analysis can be complex and requires several steps, including any number of innocent intermediaries. Observability allows admins and developers to complete those steps much more quickly.

Observability vs. Monitoring

The reality is there is no observability vs. monitoring battle going on. Both are related terms within the big data and cloud perspectives. In the stages of observability, monitoring comes first. Later, true observability arrives.

Developers and IT admins require performance data and other essential information to evaluate and further improve tasks, track performance, identify anomalies, and optimize processes. Monitoring refers to the collection and visualization of this data. It lets users know whether the system is working or not, and alerts them when anomalies or issues are detected. This allows for a rapid and viable response.

Observability builds on this. It combines massive amounts of data from monitoring tools and logs and infers using artificial intelligence and machine learning to generate actionable insights. Users are not only alerted when an error or issue arises but—more importantly— why it occurs. With observability, DevOps and IT administrators deploy rapid responses to incidents, significantly reducing downtime and minimizing its impact on end-users.

Insights derived from observability create a holistic picture of the environment, helping IT teams and DevOps determine exactly what to monitor more closely, and how to optimize performance. Only with observability can enterprises transition from reactive to proactive to predictive IT. But observability vs. monitoring? That’s a bit of a mirage. The former follows the latter.

The Benefits of Observability

Actionable Insights via End-to-End Visibility. True observability gives users the “why” of big data incidents, problems, and anomalies. Armed with a comprehensive picture of the environment, plus access to insights, DevOps and IT teams can understand why these events happen and what can be done immediately to address them and further optimize performance.

Classification of Performance Problems. Not infrequently, applications get bogged down and suffer multiple performance problems. IT admins and developers can accelerate resolution if they can quickly classify or categorize these issues. Through observability and continuous tuning, IT and DevOps can quickly recognize which applications are causing the issues and conquer challenges more efficiently.

Definitive Analysis. Observability, along with continuous tuning, eliminates guesswork from performance analysis and adds more precision. This means DevOps and IT teams no longer have to rely heavily on experience and gut instinct when solving issues within their big data environments. Proactive and actionable insights fuel users to move efficiently and accurately.

Solving Intermittent Problems. Intermittent performance problems are quite difficult to diagnose and resolve due to several factors. For one, developers find it difficult to determine the conditions of the failure. Two, pattern matching or pattern recognition are less effective because of the problems’ unpredictability. Observability, when fine-grained and properly scoped, provides reliable data to support the trend analysis and anomaly detection needed to single out the source, or sources, of intermittent problems.

Gaining Insight into Highly Dynamic Environments. Cloud-based application environments are inherently dynamic and constantly changing. Any missed change in the infrastructure can cause performance levels to go down. Observability ensures that all transitions are captured and gives users an accurate picture of how a system behaves.

The Impact of Observability on IT Operations and DevOps

Many IT teams and DevOps tune their big data and IT environments manually. However, multi-tenant environments are now more complex and dynamic and require constant fast-paced optimization. Manual optimization can’t keep up.

Given the wide variety and volume of data, as well as the furious velocity of data collection, manual tuning is no longer a feasible option. It now requires automation. Observability, through instrumenting systems and applications and gathering metrics and logs, enables the automated understanding of system behavior.

For DevOps, observability empowers teams to:

  • Spend less time testing and debug applications during development.
  • Gain higher confidence of achieving SLAs in production.
  • Enhance usability due to ease of application iteration.
  • Reduce the need to become performance experts.

Observability lets IT Operations teams:

  • Benefit from correlated end-to-end system visibility.
  • Examine the sequence of a problem through correlated system and application performance data.
  • Acquire accurate, automated ML-based operational insights.
  • Understand and explain system behavior over time.

Observability enables IT Operations and DevOps to transition from manual tuning to robust, proactive, and dynamic optimization. They unlock access to optimization recommendations, spawned by integrated AI and ML, based on reliable, real-time, and rich contextual operational data that traverses infrastructure and application performance.

Unleash Observability with the Right Tools

Pepperdata provides observability and continuous tuning for the big data analytics stack. For optimal performance on premises or in the cloud, Pepperdata provides real-time visibility for troubleshooting, debugging, planning, and automated tuning.

Increasingly, many organizations find that their current legacy monitoring solutions are no longer adequate in today’s modern IT world. Big data application performance requires observability and continuous tuning to treat infrastructure and application performance as an integrated function, capturing and correlating the performance data from each domain to fully inform both development and operations teams.

Read our whitepaper for more on how observability and continuous tuning make it possible for enterprises to derive maximum value from today’s big data ecosystem.

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