Modern Enterprise Observability and Monitoring Defined
- Sid Roy
- Jul 15, 2024
- 6 min read
This article was written by Frankenstein Tools Technology Writing Staff
Observability (often referred to as IT Monitoring), has evolved from a niche technical practice into a critical business function essential for the success of digitally powered enterprises.
What was once considered an esoteric and “techy” type exercise is rightfully now considered a critical practice for digitally powered business. Nor are IT observability investments now solely targeted at external customers or systems of revenue engagement, but even internal employee experience as well are considered table stakes.
A plethora of observability vendors - both technology and services - exist to help organizations of all types address the common and unique requirements in support of their IT service and operations management strategy. These include the following key areas:
Systems Availability
Systems Performance
Systems Error and Fault Detection
Systems Trend Analysis
Systems Reporting and Analytics
End User Experience and Analytics
Systems Log Analysis
Systems Data Analysis
Systems Capacity Analysis
Systems Events Consolidation and Analytics
Events Management & AI Operations
IT Task and Workflow Automation
These are just but a few key use cases that major organizations look to fill or partially fill with observability-focused solutions. Please also note that the word “Systems” above can be replaced with specific technical areas including applications, network, cloud, end user and database, to name but a few. There are over fifteen major categories of observability solutions.
Observability Disciplines
Recently, the market has been grouping Observability capabilities into one or more of the following four categories which does a reasonable job from a ten-thousand-foot level:
Application Performance Management
One of the more complex disciplines of Observability, application performance management encompasses several sub-disciplines that include runtime code-level analysis, transaction-level analysis; HTTP and web analysis; Infrastructure and application hosting environment analysis; integration / API analysis, database, and query performance in addition to the real / synthetic user experience monitoring- which arguably is its own discipline (digital experience monitoring).
Application Performance Monitoring (APM) involves tracking and managing the performance, runtime health and availability of enterprise applications to ensure they meet business and customer expectations. Typical metrics include response times, transaction throughput, error rates, and resource utilization. If configured and planned effectively, APM provides unrivaled visibility into application performance which identify and then diagnose issues rapidly. In the world of hybrid architectures, APM tools often are your only option - these are powerful tools that can easily pinpoint the root causes of performance bottlenecks and support reliability and efficiency of critical business applications
Log Analytics
Log Analytics has become increasingly important with the rise of highly-distributed enterprise computing platforms- most notably cloud. Effective log management involves collecting, centralizing, and analyzing logs from diverse sources, extraction and reporting of deep insights into system behavior. This helps in identifying root causes of issues, enhancing event correlation, and understanding complex interdependencies within IT infrastructures
Independent from enterprise considerations, even user side logging data is growing with the explosion of mobile apps and IoT. One estimate suggests that each person on earth a day generates on average 1GB of log data per day generated by the various IT systems they interact. Observability logging provides deep insights into system behavior and performance through detailed log data with context-rich information that helps in understanding the complete state of a system. This includes capturing structured, unstructured, and semi-structured logs that detail not just what happened, but also why it happened.
If done correctly, log management is the most effective approach for system’s monitoring- but it can be complex. But it is well worth it, as a comprehensive logging approach allows for better correlation of events across distributed systems, making it easier to trace the root cause of issues and understand complex interdependencies within the IT infrastructure.
IT Infrastructure Observability
A catch-all bucket of monitoring which can vary far and wide depending on the organization. One thing for sure, Infrastructure Monitoring is much more than the CPU, Memory, and Disk of yesteryear.Major organizations need to contend with four different architectures: on premise, hybrid, cloud, and multi-cloud. The range of metrics needed to monitor all of these us vast with company-specific requirements around custom metrics.
Fundamentally, Infrastructure monitoring involves the continuous tracking and management of various components that make up an IT environments “hardware” such as servers, database, network, storage, desktops, and message busses. Infrastructure monitoring also includes virtual, containerized, middleware, various cloud metrics and Kubernetes, representing performance indicators for deployed hyperscaler technologies.
Tools used in infrastructure monitoring often provide real-time alerts and notifications, helping IT teams promptly address potential issues before they escalate into significant problems. Additionally, these tools can offer detailed insights and analytics, enabling the optimization of resource utilization and the detection of performance bottlenecks. In addition to the technical aspects,
AI Operations
AI Ops- or what old timers used to call events management- Artificial Intelligence for IT Operations, refers to the enhanced ability to integrate, ingest, monitor, apply data lifecycle principles, analyze, and optimize complex IT environments using AI and ML techniques.
Modern approaches integrate machine learning with advanced analytics and traditional IT operations to improve visibility and understanding of the targeted systems performance and runtime behavioral execution.
By leveraging monitoring telemetry data from logs, metrics, traces, and events, AI Ops platforms can isolate on detected anomalies, predict potential issues, and provide actionable insights to prevent downtime and improve efficiency. AI Ops platforms typically include features such as intelligent alerting, root cause analysis, and predictive maintenance. Intelligent alerting helps in reducing noise by correlating and prioritizing alerts, ensuring that only the most critical issues are addressed promptly.
Root cause analysis leverages AI to quickly identify the underlying causes of problems, speeding up the resolution process. Predictive maintenance uses machine learning models to forecast potential failures and recommend preventive actions, minimizing the risk of unexpected outages. AI Ops has emerged from a consolidation point for events data as a key enabler of operating efficiency which ties into Observability, IT Service Management, CMDB and automation.
Additional Observability Disciplines
Network Monitoring: another catch-all term to describe various network-focused Observability capabilities including device-level, fault, and performance monitoring. Observability vendors are also offering WAN monitoring to provide service levels of major carriers.
Storage Monitoring: disk management is the bane of monitoring teams with all those C drives continually maxing out. Outside of that, storage monitoring is often overlooked- incorrectly. Be it on premise storage arrays or AWS s3- monitoring storage KPIs within the context of the environment is invaluable
Legacy Systems: if you are still running a legacy mainframe, it’s likely because of its reliability. Extensive Monitoring of the system with the highest performance and lowest error rate is usually low on the priority list. However, with application modernization, microservices and API integration, mainframes are often in the critical path for other applications. Being to understand run state KPI’s mapped to specific legacy workloads is vital
Integration & API Monitoring: integration and API frameworks often are not well suited for traditional APM. However, visibility into these core shared services is a separate practice unto itself. Be it Kong, ApiGee, AWS or legacy Tibco- these centralized aggregation hubs require a combination of infrastructure and process level monitoring to ensure healthy systems and actionable telemetry
ETL, Pipelines and Batch Job Monitoring: In line with integration monitoring, data movement capabilities and processes require monitoring for both the platform health as well as specific process / job health that the platforms are enabling. This is an oft overlooked area across enterprises
Cloud Monitoring: A standalone practice, most organizations rely on hyperscalers integrated monitoring like AWS Cloud Watch or Azure Monitor. Cloud Monitoring encompasses many different elements from cloud specific cloud services as well as serverless environments which can be tricky if you are used to monitoring things a certain way
Employee End User Monitoring: Internally focused systems rarely see the seven-figure spend of a Splunk or Dynatrace unless it is part of a revenue story. This changed after COVID and the employee experience which couldn’t be augmented by on-campus tech support. These tools are combinations of infrastructure, process, and vendor application monitoring. Tools like ControlUp, Lakeside and Aternity are doing well helping customers support Microsoft, Citrix, Desktop applications and other employee focused technologies
Parting Thoughts
There are a lot of tools out there, and most organizations will need to deploy several to cover all their needs. It’s easy to get lost in the technology and mind-numbed by theoretical PowerPoint ROI studies which should astronomical returns that somehow never get realized.
In the end, it’s critical that enterprises focus on their technical requirements and ensure the right tool is selected at the right cost. User and usability considerations should be priority number 2 behind technical fitness to ensure that your users will login to the platform and use it.
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