November 2024 Summaries

Digital experience monitoring (DEM) is a strategic business practice designed to enhance understanding of user interactions with technology platforms, encompassing customers, employees, and automated bots. By integrating tools like end-user experience monitoring, network monitoring, and application performance monitoring, DEM provides comprehensive insights from a user’s entry to exit on a platform, helping to proactively resolve issues and inform strategic decisions. DEM tools, such as synthetic transaction monitoring, real user monitoring, application performance monitoring, and network performance monitoring, gather and analyze data to optimize user experience, streamline operations, and drive innovation. However, challenges such as digital ecosystem complexity and data accuracy must be navigated when adopting DEM solutions. New Relic offers a robust suite of DEM tools that seamlessly integrate into digital ecosystems, providing end-to-end visibility and actionable insights to preemptively address user experience issues.

Digital experience monitoring (DEM) is a crucial business practice that helps organizations understand and enhance user interactions on their technology platforms by integrating tools like end-user experience monitoring, application performance monitoring, and network monitoring. DEM tools gather and process data from various sources, including real user monitoring and synthetic transaction monitoring, to analyze performance and inform decisions for improving user experience and operational efficiency. Despite the benefits of providing detailed insights for better decision-making and proactive issue resolution, implementing DEM solutions can pose challenges, such as integrating into complex digital ecosystems and ensuring data accuracy. A successful DEM strategy involves assessing the existing digital environment, defining objectives, and selecting appropriate tools and vendors through a collaborative approach involving key stakeholders. New Relic offers a comprehensive suite of DEM tools designed to seamlessly integrate into digital ecosystems, providing end-to-end visibility and actionable insights with the help of AI to enhance user experiences.

Application Performance Monitoring (APM) is a crucial practice for businesses seeking to optimize digital experiences by tracking and analyzing application performance in real-time. It helps identify and resolve issues before they impact users, ensuring applications meet expected service levels. APM offers benefits such as exposing root causes of performance problems, reducing revenue loss from downtime, improving resource utilization, and enhancing user experience. It provides deep visibility across an application’s components, including infrastructure and third-party services, and is integral for DevOps and site reliability engineers to maintain application reliability. APM tools utilize several metrics like response time, error rate, and CPU usage to assess performance and offer features like operational dashboards, real user monitoring, and synthetic monitoring. As applications become more complex, APM is often complemented by observability to provide comprehensive insights into system health. With the rise of distributed IT landscapes, APM's role in modern IT strategies continues to grow, making it indispensable for ensuring seamless and efficient digital operations.

The State of Observability for Retail report highlights how leading retail organizations leverage observability to enhance digital customer experiences and operational efficiency, with a reported fourfold return on investment. The report, based on insights from industry professionals, emphasizes the strategic importance of observability in minimizing downtime and optimizing customer journeys, particularly during peak shopping periods like Black Friday and Cyber Monday. Retailers are increasingly adopting AI, IoT, and security measures to build resilience and streamline operations, with the industry outpacing others in addressing outages and achieving a median mean-time-to-detection of just 32 minutes. The trend towards full-stack observability is marked by a move to consolidate tools, with a preference for a single platform that connects IT performance data to business outcomes, promising transformative impacts on business operations and customer experience. The New Relic Retail Solution exemplifies this by providing a comprehensive, AI-enhanced platform that bridges observability best practices with business outcomes, offering real-time monitoring and analysis across all retail systems and applications.

The State of Observability for Retail report highlights the strategic role of observability in enhancing digital customer experiences and operational efficiency within the retail sector, drawing insights from 148 industry technologists and decision-makers. Retailers are leveraging observability solutions, such as the New Relic Intelligent Observability Platform, to minimize downtime, optimize customer journeys, and address challenges during peak shopping periods like Black Friday and Cyber Monday. The report indicates that retailers are adopting technologies like AI and IoT to build resilience and streamline operations, with observability driving business value and offering a competitive edge. Retail organizations are increasingly favoring a single, consolidated observability platform to link IT performance data to business outcomes, with a notable reduction in the number of tools used, enhancing their operational efficiency. The report underscores the importance of digital experience monitoring in growth, with plans for deploying browser, mobile, and synthetic monitoring to improve online customer journeys. Security, governance, risk, and compliance are top strategies motivating observability adoption, reflecting a sector-wide commitment to leveraging advanced technologies for operational excellence.

In the context of digital transactions and the sales season, enhancing the online shopping experience through observability is crucial for maintaining system performance and driving revenue growth. Observability, a strategic approach for understanding system performance, can significantly reduce downtime and improve customer satisfaction, as demonstrated by companies like Kmart, Skyscanner, and Shutterstock using New Relic's tools. These tools allow businesses to monitor performance across various tech stacks, prepare for high traffic periods, and gain insights into customer journeys, thus reducing incidents and enhancing the shopping experience. Core Web Vitals and full stack observability are key strategies utilized by companies like Kurt Geiger and M&S to optimize performance, with the former improving site speed and the latter reducing downtime by visualizing their tech ecosystem. Distributed tracing and synthetic monitoring further enable precise traffic management and proactive issue resolution, as illustrated by Thortful's approach. Observability capabilities are increasingly adopted, with 75% of surveyed organizations deploying multiple tools, signaling a move towards more comprehensive system monitoring to safeguard against potential sales losses and improve customer satisfaction.

In the realm of digital transactions, ensuring a seamless online shopping experience is vital, especially during the sales season, and leveraging observability tools can significantly enhance this experience while driving revenue growth and minimizing performance risks. Observability, a strategic approach to understanding system performance, has shown that outages can cost organizations up to $1 million per hour, but with enhanced observability, businesses could boost revenue and operations by up to 29%. Companies like Kmart, Skyscanner, and Shutterstock utilize New Relic's observability tools to reduce downtime and improve customer satisfaction by gaining comprehensive insights into their tech stacks, including AWS and Kubernetes. Observability also allows businesses to understand the customer journey better, with platforms like Thortful using distributed tracing to manage traffic surges and ensure seamless user interactions. By proactively preparing for high traffic periods through various observability capabilities, retailers like Kmart and Marks and Spencer have optimized their systems for peak performance, reducing downtime significantly and saving millions annually. The upcoming holiday season presents an opportunity to implement robust observability practices, safeguarding against potential sales losses and laying the groundwork for long-term improvements in customer satisfaction and revenue growth.

At KubeCon North America, New Relic introduced its one-step observability solution for Kubernetes, designed to streamline the monitoring of dynamic Kubernetes environments by automatically integrating application performance monitoring (APM) with Kubernetes deployments. This innovation addresses the challenges developers face in managing containerized applications by providing AI-enhanced insights and pre-configured dashboards that facilitate faster incident resolution and boost developer productivity. The platform offers native support for OpenTelemetry and Prometheus, allowing comprehensive visibility and correlation across applications and Kubernetes clusters, while simplifying the installation and setup process through a Helm chart and ensuring up-to-date APM agents. By democratizing observability with AI-powered insights, New Relic aims to enhance performance management, maximize uptime, and drive business revenue, making observability accessible to users of all skill levels.

At KubeCon North America, New Relic announced a new one-step observability solution for Kubernetes, designed to streamline monitoring by automatically integrating application performance monitoring (APM) with Kubernetes deployments without requiring additional configurations. This solution leverages AI-strengthened insights and pre-configured dashboards to enhance the management of Kubernetes workloads, reducing incident resolution times and boosting developer productivity. Kubernetes, an open-source system for automating the management of containerized applications, is popular for its role in driving innovation and efficiency, but presents challenges in monitoring performance. New Relic's platform offers unified visibility across applications and Kubernetes, facilitates faster issue resolution, and integrates with OpenTelemetry and Prometheus to prevent data fragmentation. The solution democratizes observability by allowing users of varying skill levels to access insights through natural language prompts, aiming to maximize uptime and performance while enhancing customer satisfaction and driving business revenue.

OpenTelemetry Operator is a powerful tool for managing the deployment and configuration of OpenTelemetry Collectors and implementing auto-instrumentation in Kubernetes environments. It automates the Collector's deployment and configuration, ensuring smooth operation across clusters using the Open Agent Management Protocol for vendor-agnostic settings. The Operator also simplifies the process of auto-instrumentation, allowing applications to gather telemetry data without needing source code modifications. However, successful implementation requires proper installation of cert-manager and the Operator, careful configuration of the OpenTelemetryCollector resource, and accurate endpoint configuration for telemetry data exportation. The blog provides detailed installation instructions, troubleshooting tips for common issues, and strategies for optimizing the use of both code-based and zero-code instrumentation options. Overall, it equips users with the knowledge to leverage the Operator for efficient telemetry data collection and monitoring in Kubernetes applications.

New Relic AI offers a proactive approach to monitoring by utilizing AI to predict and address potential system issues before they cause downtime. By integrating with large language models and the New Relic data platform, it provides insights into system performance through natural language queries. Users can analyze performance trends, identify anomalies, detect monitoring gaps, and set up synthetic monitors to simulate user interactions. The AI assistant translates natural language into New Relic Query Language (NRQL) queries for detailed system health checks and anomaly detection. This proactive monitoring aids in maintaining continuous uptime and reducing customer frustration by allowing engineering teams to address issues swiftly. New Relic AI, currently in preview, can be activated by accepting specific terms and offers a chat-based interface for interactive troubleshooting and monitoring setup, ensuring comprehensive coverage of technology stacks.

New Relic AI offers a proactive approach to performance monitoring by leveraging AI to predict and resolve issues before they occur, thereby minimizing downtime and enhancing user satisfaction. This tool, which combines large language models with New Relic's data platform, enables engineering teams to analyze performance trends, identify anomalies, and detect monitoring gaps through natural language interactions. Users can set up synthetic monitors to simulate user interactions and ensure application availability, while also identifying unmonitored services to prevent blind spots in system coverage. Although New Relic AI is currently in preview, it is easily accessible and integrates seamlessly into existing workflows, allowing teams to transition from reactive to proactive monitoring and ensure stable, efficient operations.

The OpenTelemetry Operator is designed to streamline the management of OpenTelemetry in Kubernetes environments by automating the deployment and configuration of Collectors and facilitating zero-code instrumentation for applications. It aids in automatically injecting and configuring instrumentation into Kubernetes pods, enabling telemetry data collection without altering source code. The Operator supports various deployment patterns and leverages the Open Agent Management Protocol for consistent observability settings across different vendors. Before installation, a Kubernetes cluster with a cert-manager is required, and the Operator can be installed using kubectl or Helm. The blog provides detailed guidance on installation, deployment, and troubleshooting common issues related to Collector deployment and auto-instrumentation. It emphasizes the importance of correctly configuring endpoints and checking for proper deployment order and annotations to ensure successful telemetry data processing.

Observability practitioners leverage telemetry data to answer both high-level and detailed questions about system performance, using tools like the New Relic Query Language (NRQL) on data collected from various sources, including OpenTelemetry and Prometheus. By employing a process referred to as "zooming in," users can start with broad overviews of system performance and then drill down into specific issues, such as identifying latency in e-commerce applications' frontend services. Through examples, the text demonstrates how NRQL queries can provide insights into the duration and distribution of transaction times, identify key services contributing to latency, and pinpoint specific endpoints, such as the "PlaceOrder" endpoint in a checkout service, that may experience performance spikes. This approach allows for targeted investigation of potential bottlenecks and more informed decision-making to address system inefficiencies.

Observability practitioners can harness telemetry data to analyze system performance at both high-level and granular detail, using New Relic Query Language (NRQL) on metrics, events, logs, and traces collected from various sources like New Relic agents and OpenTelemetry. By employing a technique referred to as "zooming in," users can start with a broad overview and then delve into specific data aspects, such as identifying customers experiencing slow checkout responses in an ecommerce application. The process involves using NRQL to query tracing telemetry, like distributed trace summaries and span events, to pinpoint latency issues and service dependencies, ultimately using these insights to improve system performance. The example presented focuses on an ecommerce application with a frontend service and backend microservices, demonstrating how to trace latency issues in the checkout service and its endpoints over time, highlighting the importance of understanding transaction durations and service dependencies in observability practices.