Monitoring and observability are often used interchangeably, but there are key differences between the two approaches. This is part 1 of a three-part blog series. Part 1 outlines the specifics of monitoring versus observability and explains how both approaches complement each other. Part 2 highlights how to implement an observability framework at scale with Datadog. Finally, part 3 shares salient business use cases for sustainable observability.
What is Monitoring?
Monitoring is the measuring and comparing of the data gathered against a goal or target. From an IT perspective, monitoring is collecting data (e.g., application or system data), analyzing it against key performance indicators, and alerting IT teams to proactively fix the underlying issue before any outage happens, ensuring business continuity.
Monitoring focuses on availability, performance, and capacity by:
- Collecting data and telling you that something is wrong (e.g., what is my system status?)
- Collecting metrics and discovering faults from those predetermined metrics
- Focusing on specific key performance indicators (KPIs)
- Capturing and reporting on small, known problems examining systems’ internals (e.g., memory or CPU utilization)
- Collecting predetermined metrics data to notify faults (e.g., network latency, decreased I/O performance)
- Helping confirm planned changes
Monitoring is usually applied to monolithic, traditional IT environments where you have a defined set of applications, infrastructure, integrations, and input/output workflows with clear expectations on what to measure and when.
Today, organizations leverage hybrid and multi-cloud environments by using distributed architectures with microservices orchestration platforms using continuous integration and continuous delivery (CI/CD) DevSecOps processes to anticipate and adapt to ever-changing customer expectations. These dynamic IT operation environments expose organizations to unpredictable system failures or security breaches (e.g., one incorrect open source version in a microservice would create a cascading effect; also, in a recent 2022 survey, 59% said security is their biggest concern with regard to continued use of Kubernetes and containers), which cannot be handled by traditional monitoring strategies and tools, leading to visibility gaps and performance challenges.
What is Observability?
Observability is aggregating, correlating, and analyzing a steady stream of data (e.g., application, third party-software, infrastructure data) to effectively monitor and troubleshoot applications to meet business requirements – be it business continuity, customer experience, service level indicators (SLIs) or service level objectives (SLOs).
Observability focuses on metrics (data measured over intervals of time), logs (an event record of past discrete events), and traces (a representation of a series of distributed events) Refer to our blog Leveraging Datadog to Increase Observability for detailed explanation of these three pillars (metrics, events, and logs). Observability also:
- Examines collected data and tells you what is wrong and why it happened (e.g., why server crashed?)
- Generates metrics by knowing what to monitor and identifying future metrics/events/systems to be monitored
- Focuses on entire, holistic IT landscape environment
- Deduces systems’ internal state from its external outputs and discovers conditions you might never think to look for and tracks their relationship to specific issues
- Contextualizes collected data to identify root cause (e.g., data correlation, distributed tracing, anomaly detection)
- Helps manage both planned and unplanned changes by delivering a comprehensive view of the IT landscape
Observability does not replace monitoring but enables improved monitoring and application performance management (APM). The more observable your IT environment, the more quickly and accurately you can identify the root cause effectively.
Some of the primary observability benefits include:
- Business agility: quick decision making, quick root cause detection, responsive and faster time to resolution, speed in identifying issues proactively, etc.
- Revenue growth: drive revenue and innovation, improve customer relations, enhance decision making, better future proofing, etc.
- Employee/team engagement: confidence in underlying systems and infrastructure, reduced frustration, etc.
- Cost Savings/Reputation: internal and operational efficiencies, cost optimization, increase customer retention, maintain SLA/SLO/SLIs, Enhance reputation, etc.
Organizations need observability platforms, in addition to monitoring tools, to proactively identify and resolve issues before they become too expensive or impossible to contain.
When you boil it down, monitoring focuses on “what” by providing visibility that makes it clear when performance issues or bottlenecks occur; business benefits include cost effectiveness, fewer IT concerns, better security, and increased operations’ productivity. However, observability focuses on “why” by offering deeper views into the technical details; business benefits include insights into digital business operations, speeds innovation, and enhances customer experience. Downtime is costly affecting reputation, revenue, and morale; since legacy monitoring tools were no longer sufficient, we need new observability tools complementing monitoring tools to stay ahead
Our next blog will focus on using Datadog, a monitoring and analytics tool for implementing observability platforms.
Infinitive has implemented observability and monitoring platforms for several clients across multiple industries. Our proven observability framework methodology can help your organization mitigate issues while increasing business agility, revenue, and cost savings in as little as 30 days. For more information on how to implement a sustainable observability solution, contact us today.