Software vendors have been navigating the vast ocean of digital transformation opportunities across enterprises. As part of this challenge, they also manage a complex amalgamation of cloud-hosted software. From an operational point of view, this is a radical shift from traditional IT operations and software license management to cloud operations and deployment management, also known as CloudOps. Cloud observability is a key pillar of this CloudOps practice.
What is Cloud Observability?
Cloud observability is a robust methodology that provides a better view of the innards of a software application deployed within cloud environments. It equips developers and operations teams with invaluable insights, enabling them to monitor, troubleshoot, and optimize their applications with unprecedented precision. Cloud observability applies to every independent software vendor (ISV), software service provider, or technology-driven organization that wants to manage its software applications on the cloud to ensure continuous, real-time, and proactive operational efficiency.
The need for observability is also evident in the transition from installation-driven to deployment-driven software maintenance. Unlike traditional desktop software installed on a single computer for a limited set of users, cloud software is deployed and continuously updated through a tightly controlled delivery mechanism, which impacts all users. Under such circumstances, observability’s role in proactively ensuring peak performance and optimal user experience becomes paramount.
With $4.1 billion worth of opportunity projected by 2028, the cloud observability market is expected to play a significant role in software development. Let’s unveil the prominent use cases and learn about the observability tools and platforms that form the bedrock of any observability stack for modern software deployment.
Peering Through the Cloud: Applying Observability to Distributed Software Deployment
Distributed software deployment is the hallmark of today’s SaaS-based, cloud delivery model. It is characterized by scale and fault tolerance, achieved through variable capacity allocation and redundancy. Additionally, modern distributed cloud applications are designed with high modularity, and software development cycles are designed for continuous delivery. The culmination of all these requirements is a highly dynamic and complex software system.
Cloud observability tools help development teams effortlessly zoom out and within the application software stack. Observability acts like a lighthouse that can zoom out and clear the mist to provide precise insights into the stack's inter-component interaction dynamics. While zooming in, it acts as a high-powered microscope, providing a crystal-clear view into the intricate workings of individual components of the stack.
Compared with traditional software application maintenance methods, observability offers more valuable information than standard monitoring tools, which can only measure known metrics. Observability also goes beyond debugging to ensure quick detection, analysis, and resolution of problems in software deployment across the entire spectrum of functional to non-functional requirements, ranging from code-level bugs, scalability bottlenecks, resilience issues, and performance drifts. All of this has an overall positive impact on customer satisfaction and business success.
Spotlight on Cloud Observability Platforms: The 10 Observability Use Cases
Consider a general architecture of the tech stack of a distributed cloud software deployment. The cloud observability stack runs parallel to it and provides the necessary interventions to capture, query and analyze the MELT (Metrics, Events, Logs and Traces) data.
Here are the ten must-have observability use cases. These use cases are applicable to any cloud software deployment based on the level of deployment complexity and software development process maturity.
- 1MELT Data Capture: Capturing the Metrics, Events, Logs, and Traces from within the software runtime to establish the baseline evidence for comprehensive observability in a software system.
- 2Live Debugging: Examining a running software application in real-time to identify and fix issues in business logic through analysis of MELT data without halting or restarting.
- 3Application Troubleshooting: Diagnosing the application functionality for abnormalities arising from inter-service or inter-component dependencies.
- 4Application Performance Management: Discovering performance issues and bottlenecks within the application components and monitoring the resource utilization to ensure optimal usability of the overall software system.
- 5Observability Driven Capacity Planning: Forecasting the cloud resources required to maintain a right-sized deployment for optimal performance at the lowest cost, and best user experience.
- 6Observability Based Anomaly Detection: Identifying unusual patterns in the observability data collected from a software system that indicate potential issues such as performance degradation, unusual user actions, or security breaches, allowing for early intervention and resolution.
- 7UX Monitoring: Leveraging the observability findings to track a software application's user experience (UX) and identify bottlenecks related to user interactions and system responses.
- 8Observability Backed SLO: Measuring service level objectives to establish benchmarks for technical and business metrics for the software, as observed through MELT data.
- 9Observability Powered Security Analytics: Expanding the scope of cloud observability for cybersecurity protection and managing security incidents more effectively.
- 10Observability Visualization: Visual analysis of observability data to provide insights into the system's performance, health, and behavior.
Top Cloud Observability Tools Unleashed
The top observability tools and platforms addressing the major spectrum of use cases to build full stack cloud observability capabilities
1. MELT Data Capture
MELT represents Metrics, Events, Logs, and Traces, the four fundamental types of data used for cloud observability.
Metrics is a quantitative dataset that measures various aspects of system performance. This can include API response times, CPU usage, memory consumption, and function invocation counts.
Events indicate discrete incidents that highlight changes in the system's state. These include errors, user actions, and other significant incidents within the application.
Logs contain detailed records of messages generated by the software to provide qualitative context and a historical trail of the runtime execution.
Traces represent end-to-end records of a request through various system components to understand the interactions and dependencies across the components.
The most common means of capturing the MELT data is via the client libraries embedded in the source code. This approach follows static instrumentation, wherein software source code is augmented with additional code to capture MELT data.
OpenTelemetry is the de facto SDK used for this purpose. It supports many programming languages and can generate, collect, and export MELT data for further analysis.
The primary purpose of MELT Data Capture is to ingest data into a central repository to establish a single source of truth for the development teams to address any issues effectively. OpenTelemetry has a collector component for storing, processing, and exporting the MELT data. Assuming a self-hosted observability stack, this data can be ingested to a few well-known platforms for further analysis.
The Elastic platform is a pioneer in search and analytics. It is supported via OpenTelemetry collector integrations for storing observability data and can be deployed as a unified observability backend for performing AI-driven analytics.
Prometheus is an open-source platform for system monitoring and alerting. It offers a time-series database with powerful query engine for storing metrics and events. It is compatible with OpenTelemetry and has client libraries for static instrumentation.
Jaeger is a cloud observability platform designed for distributed tracing. Its agent and collector components capture traces via the OpenTelemetry SDKs, and Elastic, Cassandra, and others provide storage support.
SigNoz is an OpenTelemetry-compatible platform. It can act as a drop-in replacement for the OpenTelemetry collector to ingest MELT data directly from the instrumented application code to a self-hosted SigNoz cloud or a managed service.
Besides OpenTelemetry, a few proprietary options exist for capturing MELT data.
Fluent Bit offers an observability data pipeline for ingesting and enriching MELT data from various sources, including embedded devices, and forwarding that to multiple locations. It also supports exporting to the OpenTelemetry collector.
Lightrun offers a unique way of capturing MELT data without relying on OpenTelemetry SDKs. Instead, it offers a dynamic instrumentation approach, a more developer-friendly way of collecting MELT data within the software runtime.
2. Live Debugging
Live Debugging refers to diagnosing issues in a running software application without stopping, restarting, or modifying its source code.
The critical enabler for Live Debugging is the non-breaking breakpoint that captures MELT data without halting the runtime execution. This capability is crucial for minimizing downtime in production environments.
Lightrun is a developer-friendly observability platform that offers Live Debugging within the IDE. Developers can add dynamic logs, traces, and metrics anywhere in the codebase without modifying the code or redeploying the application.
Rookout (acquired by Dynatrace) offers Live Debugging capabilities for capturing logs and metrics. It also supports real-time profiling for pinpointing the exact line within the source code suspected of having performance issues in a running application.
3. Application Troubleshooting
While Live Debugging is limited to the application’s business logic and source code, Application Troubleshooting has a broader focus that spans the underlying service layers. It involves diagnosing hardware, OS, and networking-related issues that typically create bottlenecks in service-to-service communications.
To achieve Application Troubleshooting, observability platforms offer detailed analytical capabilities to aggregate MELT data from different services or subsystems of the deployed application and translate that into comprehensive insights about the overall system health.
Datadog is an industry leader in cloud observability with features, including Application Troubleshooting capabilities for infrastructure, containers, and network monitoring. Its intelligent features enable discovery, mapping, and monitoring of every service, faster issue detection, and centralization of all knowledge in a single place.
Aspecto.io ( a SmartBear Company) offers a distributed tracing platform based on OpenTelemetry data. This platform correlates, searches, explores, and visualizes traces across multiple services for complete visibility and troubleshooting.
Observe Inc. offers an observability data lake to accumulate siloed MELT data from a plethora of cloud services and OpenTelemetry clients. It offers a custom dashboard for exploratory MELT data analysis, visualization, and alerting mechanisms to assist in troubleshooting.
4. Application Performance Management
Application Performance Management (APM) is a crucial aspect of observability that focuses on monitoring and managing software applications' performance and availability. Due to the dynamic nature of today’s software applications, whose business success is impacted by user surges and geographic distribution, APM assumes great importance in observability-driven practices.
APM goes beyond Live Debugging and Application Troubleshooting to enable end-to-end monitoring and performance management of the service and infrastructure layers.
Datadog has an extensive set of APM features to detect and resolve root causes related to application performance and optimization through a collaborative approach for faster resolution.
Dynatrace is another full-stack cloud observability platform with extensive APM capabilities to identify and resolve real-time performance issues. It combines deep insights into the entire application stack with hybrid, multi-cloud deployment coverage.
New Relic is also a full-stack observability platform with an exhaustive range of monitoring and management capabilities covering services, platforms, infrastructure, and more. Their APM 360 application monitoring platform covers the entire gamut of performance metrics across the application and infrastructure.
Sumo Logic offers an observability analytics platform with APM capabilities that provides insights across all MELT data, covering performance metrics, logs and events, and distributed transaction tracing.
5. Observability Driven Capacity Planning
Observability Driven Capacity Planning complements APM to ensure that the cloud infrastructure is right-sized to optimally deploy the application for handling different levels of user and traffic surges, ranging from normal operations to peak load conditions. This use case relies on the insights provided by APM to predict future capacity needs and provide recommendations for effective capacity allocation. This synergy with APM helps balance high performance, reliability, with cost-efficiency in modern cloud workloads.
Another vital consideration under this use case is to contain the cost of massive observability data that accumulates over time, increasing the cost of MELT data storage.
Logz.io is a unified observability platform for cloud-native infrastructure. It is well-suited for performance analysis of Kubernetes infrastructure for holistic capacity management across clusters, nodes, and pods.
Chronosphere is a cloud-native observability platform that provides deep insights into every layer of a software stack, from the infrastructure to the applications to the business. The Chronosphere Control Plane helps manage the observability data volume and cost, to improve performance and deliver more business value.
6. Observability Based Anomaly Detection
Obervability Based Anomaly Detection continuously monitors and collects data on various aspects of application performance. This data is then analyzed to identify deviations from normal behavior that can be considered anomalies.
Given the influence of artificial intelligence, observability platforms have geared up to this challenge by leveraging various AI/ML algorithms to perform pattern recognition, predictive analytics, alert prioritization, and scoring around APM data to find anomalies. This use case augments APM to maintain high application performance and minimize downtime, ultimately improving overall operational efficiency.
Honeycomb.io is an observability platform with powerful anomaly detection features that automatically detect hidden patterns and narrow problems to their specific host, service, pod, database, or region.
New Relic offers AIOps powered anomaly detection for automatic issue detection, quick root cause identification, and the complete incident management workflow for rapid alerting, correlation, and resolution.
EdgeDelta is an AI-driven observability platform that generates automated insights from all the captured MELT data, curated in real-time for continuous inference. It can proactively monitor services with a guided AI copilot to summarize every anomaly and provide remediation recommendations.
7. UX Monitoring
UX Monitoring provides comprehensive insights into application performance's impact on user experience. It combines actual user activity data, synthetic tests, and user feedback to unearth performance issues within the application tech stack that hamper user interaction and facilitates root cause analysis to optimize and enhance the overall user experience.
Sentry is an application monitoring platform with extensive support for web, mobile, IoT, and enterprise application stacks. It supports UX monitoring features such as tracing, user feedback, and session replays, which capture the complete context of user interaction to pinpoint the source of UX bottlenecks.
LogicMonitor is a cloud infrastructure observability platform that supports SaaS and web monitoring. It ensures seamless digital experiences for end-users with on-the-spot service checks and synthetic transactions to optimize the performance of application front ends and websites.
8. Observability Backed SLO
Observability Backed SLO leverages the MELT data to define and monitor specific application metrics over a period that drives the key SLOs for a deployed software application.
SLOs comprise multiple service level indicators (SLI) and corresponding thresholds. Each SLI is a quantifiable measure of the application's performance derived from observability metrics and continuously monitored for breach of the permissible limits in threshold values. Standard SLOs tracked for cloud software deployment include service uptime, latency, and API error rates.
Honeycomb can define, explore, and act on SLO based on highly granular event data. It also has a debuggable interface for the engineering teams to investigate the source of SLO breaches quickly.
Sumo Logic’s reliability management feature allows SLOs to be defined based on log searches and queries to monitor golden signals like latency and errors. It has an SLO editor with custom views for SLO parameter filtering and SLO metadata.
9. Observability Powered Security Analytics
Observability Powered Security Analytics leverages advanced analytics on MELT data to identify unusual activities, such as unexpected spikes in traffic or abnormal login patterns, to anticipate potential threats and vulnerabilities in the application.
Apart from these activities, this use case affords better visibility on service and infrastructure-level vulnerabilities through continuous, real-time monitoring.
Datadog's SIEM offering is capable of advanced threat detection and investigation in dynamic cloud environments. It can visualize security insights from logs and supports many integrations and threat detection frameworks aligned to the MITRE ATT&CK® framework to build custom rules easily.
Dynatrace offers a continuous application security posture based on unified observability to secure and protect cloud-native applications. Its AI-powered, contextual security analytics helps accelerate incident investigation with automated attack path analysis.
Sumo Logic offers a cloud SIEM for cloud infrastructure security analytics. It continuously monitors the attack surface for increased threat visibility and deep security insights related to user and entity behavior, access control, risk profiles, and more.
Aside from this use case, there is also a parallel concept of security observability. It applies the fundamental principles of observability to monitor, detect, and respond to security threats and vulnerabilities based on the vast amount of security-specific data generated within a cloud deployment, such as firewall logs, OS system logs, network configuration logs, and other security-specific systems such as Endpoint Detection and Response (EDR), Extended Detection and Response (XDR) and Security Posture Management (SPM) systems.
Security observability enhances traditional software observability by augmenting MELT data to include network traffic, endpoint data, and threat intelligence. This provides a comprehensive view of any cloud environment's security posture, enabling more effective compliance monitoring and proactive threat hunting.
Deepfence offers a Cloud Native Application Protection Platform (CNAPP) supporting runtime security observability based on telemetry data correlating from applications and networks to provide insights into evolving attack behavior.
Snyk is a developer security observability platform that provides security observability at the software supply chain level to mitigate risks during development.
10. Observability Visualization
All observability platforms can transform raw MELT data into visual representations such as charts, graphs, and dashboards to reveal patterns, trends, and anomalies. More detailed visualizations include heatmaps to gauge resource utilization, topology maps to understand relationships and dependencies between different layers, and trace maps to understand the request flow across services.
Some full-stack observability platforms offer advanced visualization by rendering a systemic view of the entire application deployment architecture with drill-down options to generate on-the-fly visual metrics. This helps DevOps, platform, and IT teams quickly understand and analyze the performance, health, and behavior of their software deployment and the underlying infrastructure.
Cisco AppDynamics is an enterprise-grade IT and cloud observability platform covering application, cloud infrastructure, IT, and business-level observability. It has powerful visualization capabilities with full-stack visibility and the ability to design custom dashboards with Dash Studio.
Datadog offers realtime, interactive dashboards with customizable views and many visualization primitives for analyzing relevant observability insights and fostering collaboration across stakeholders.
In addition, some standalone data visualization platforms can integrate with observability data stores to offer Observability Visualization.
Grafana is a visualization platform designed explicitly for observability data. It offers beautiful, custom-designed dashboards for querying, visualizing, and understanding MELT data exported from external sources.
Kibana is part of the Elastic stack and offers a decent visualization platform for plotting and analyzing observability data, with search and pattern recognition features.
OpenSearch is an open-source project that offers a visualization and analytics platform for data-intensive applications, including observability. It supports observability data exploration and enrichment features.
FAQ
Cloud observability is a concept that is applicable to the cloud software deployment which helps developers and DevOps teams understand the internal working of the applications, microservices and sub-system components. Observability assists in querying the underlying runtime environment to get a holistic view of the overall system health, performance, and maintain adherence to technical and business metrics.
Observability is of paramount importance in the modern cloud based software deployment which relies on a highly distributed architecture with ephemeral services and cloud resources getting allocated and released on the fly, based on user activity, traffic or geographical scaling. Observability supersedes the traditional monitoring practice to ask unknown questions about the internal states, call traces, and critical metrics which goes beyond the usually known metrics gathered through standard monitoring practices.
Observability plays a vital role in understanding the functional as non-functional aspects of the software system and its underlying runtime environment and cloud infrastructure. An observability stack works alongside the software tech stack and can observe the deployed software across layers of the tech stack to provide application specific, service specific and infrastructure specific insights. Due to the dynamic nature cloud software deployment which scales up or down based on multiple factors, observability becomes a key enabler for faster debugging and troubleshooting of issues.
The choice of cloud observability tool mostly depends on the scale and maturity level of software deployment and the scope. For large scale, enterprise applications, Cisco AppDynamics, DataDog, Dynatrace and SumoLogic are the best observability platforms offering a wide gamut of features. For cloud infrastructure and service level observability, Honeycomb, New Relic and Chronosphere and good bets. For developer observability, Lightrun is the preferred platform. Apart from that, there are several open source alternatives for gathering, storing and visualizing observability data, such as, Elastic, Observe, Grafana and OpenSearch.
