Big data stacks and IT platforms run thousands of workloads and applications. To succeed, developers must pursue a shared understanding of how their apps function. To gain this understanding, observability is crucial. If you can’t truly observe your tech stacks, then there’s no way you can understand them. Observability is now a prerequisite for successful big data analytics operations.

IT service providers and business organizations need to keep their entire IT infrastructure performing at an optimal level to meet business requirements and consumer demands. This includes platform services, containers, and microservices. To ensure dependability and predictability, enterprises have to observe and monitor hardware, applications, and other metrics associated with infrastructure performance.

Many materials and articles discuss a supposed conflict: observability vs. monitoring. But the reality is that, when it comes to optimizing big data stacks, monitoring and observability are two different but closely related things. Monitoring pertains to creating and deploying systems to gather data, with the goal of planning and executing a data-driven response when something goes wrong. Observability is the practice of equipping those monitoring systems with the features and tools to collect actionable data that alerts users when an error has occurred and guides the user toward a speedy resolution.

Observability is now a crucial component in big data analytics optimization and performance tuning. However, there are degrees to it.

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Stage 1: Manual Monitoring

Manual monitoring requires constant human involvement. While it is feasible for small businesses and startups due to lower system costs, it is not a sustainable practice as your business grows, due to three main factors: human error, speed factor, and labor costs.

  • Human Error. Data gathering is an extensive process for us humans. Given that big data stacks produce mountains of data of different variety and at a furious pace, the human brain cannot record, organize, and prioritize large datasets from multiple sources accurately. Any data process performed manually is prone to human errors. Typos, erroneous data entries, and missed fields are virtually inevitable. Any data analysis based on manually gathered and processed data is bound to be unreliable.
  • Speed Factor. In a world where time is equivalent to money, speed matters. You need to derive insights from your datasets in real time if you want your IT infrastructure to quickly recover. Entering, processing, and analyzing data manually requires tons of time, which is far from ideal in a world that demands speed on top of accuracy.
  • Labor Costs. To fully leverage a manual monitoring approach, you’ll need more people to perform intensive data processes. Hiring more people means you have to invest more, preventing you from concentrating your funds and efforts on other priorities that could contribute toward your company’s goals and mission.

Stage 2: Monitoring Tools

The introduction of monitoring tools helps reduce the burden of manual monitoring. It’s a big step up from Stage 1. You have all the information as to what the problem is. These solutions allow you to look back at what happened. However, it won’t show you the why.

We are living in a world where enterprises, DevOps teams, and IT professionals place a premium on extremely automated and dynamic environments. Traditional monitoring solutions that are anchored to hosts, applications, and networks may deliver data faster than manual monitoring approaches, but if you can’t access this data in real time and understand it in its context, the information becomes dated and unreliable.

The lack of automation, alerting capabilities, and insights also prevent you from fully unlocking and maximizing the value of your data.

Stage 3: Smart Monitoring

At this stage, monitoring tools function continuously. Not only do these smarter tools gather data; they give feedback from the environment, including the application’s performance, resource consumption, and usage patterns.

Automated processes driven by data always beat manual processes. With smart monitoring tools, users can achieve high application availability because these tools drastically reduce the time to detect and mitigate issues as they arise. Automation eliminates manual, repetitive, and error-prone results while treating users to enhanced productivity, speed, and scalability.

Smart monitoring offers automated alerts. The system can tell you if you’re not meeting Service Level Indicators (SLI) and Service Level Objectives (SLO) but it has little explanatory power to help you diagnose and fix issues.

Put simply, without the “why”, tools only enable reactive, not proactive, action.

Stage 4: Real-Time Observability

Comprehensive observability tools deliver not just the “what” but also the “why.”

A good visibility strategy lets you see everything that occurs across your entire big data and IT stack at all times. This lets you train your focus on fixing bugs instead of finding them. It enables you to discover issues across your platform from within a centralized location. With this approach, all issues become noticeable as they break the surface.

If you have visibility over all the separate parts of your software within a single platform, it becomes effortless to determine what is influencing the rest of your stack. Imagine your monitoring tool discovers an anomaly: It delivers a notification along with a stack trace. With the information available to you, you can delve deeper into the steps that led to that issue. The insights gathered from automated observability also guides you toward the step required to solve it.

With this level of visibility, you see the context. You can answer questions like:

  • Why did it happen?
  • How did that issue affect my users?
  • How did the rest of my stack behave?

This is crucial because it enables DevOps, IT teams, and software developers to more efficiently manage their environments. Systems that offer observability generate recommendations based on a thorough analysis of the performance data. Observability helps in discovering insights that will improve the performance of infrastructure and stacks, and it equips users with the knowledge to solve issues when they arise.

The Pepperdata Advantage

Pepperdata provides enterprises with real-time observability into their big data stacks. With Pepperdata, users can observe any and every part of the infrastructure, ask questions, and quickly find the appropriate answers. To learn more, read the white paper.