The Transformative Power of AI and ML in Enhancing Observability   In the realm of IT operations, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is revolutionizing the way organizations maintain observability over their systems. Observability practices are enhanced by AI and ML to predict and prevent incidents before they occur, leading to significant improvements in reliability, availability, and customer experience. These technologies are applied across varying maturity stages, from gaining basic visibility to employing advanced predictive models that preempt potential issues. 1  The financial implications of downtime are profound, with each hour potentially costing organizations an average of $365,000, thus underscoring the critical need for efficient operational practices. This blog explores the transformative impact of AI and ML on observability within IT environments, demonstrating the benefits through various use cases and highlighting the substantial return on investment for organizations that adopt these practices. Harnessing AI and ML for Enhanced Observability   Organizations increasingly recognize the benefits of observability in enhancing reliability, availability and customer experience. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into observability practices offers predictive capabilities, allowing for the anticipation and prevention of incidents before they occur. AI and ML are applied in three maturity stages for observability: from basic visibility without AI to proactive AI that establishes baselines for normal operations, and finally, to advanced predictive models that can preempt potential issues based on historical data. One of the roles of AI in observability is to provide a deeper understanding of 'normal' operational patterns and to direct attention to anomalies that require immediate action. This approach is instrumental in resolving issues faster, reducing downtime impact, and instilling confidence in maintaining customer-facing and internal services.     The complexity of modern technology stacks makes it challenging to discern operational health, a dilemma that AI and ML help to address by centralizing visibility and offering actionable insights. Features that support this include dynamic baselining, predictive modeling, and improving detection accuracy. These capabilities are encapsulated in a variety of products designed to simplify the adoption of AI and ML for organizations, regardless of their current maturity stage in observability. Understanding AIOps and Its Practical Applications   AIOps is the application of AI and  machine learning to operations. This includes anomaly detection, alert noise reduction, probable root cause, automation and remediation, and proactive prevention.. The importance of AIOps stems from its ability to predict and prevent potential IT issues before they impact customers, which is crucial for maintaining high availability and reliability of IT services. AIOps has a range of use cases, demonstrating its benefits across various scenarios. For instance, it aids in swiftly resolving downtime, enhancing detection efficiency, and reducing manual processing efforts. A practical example includes an organization that shifted from static to dynamic thresholding to improve alert accuracy, as static thresholds were either overwhelming during peak hours or missing anomalies during off hours. This shift led to massive improvements in detection efficiency. The monetary benefits of AIOps adoption are significant. Moreover, downtime not only incurs financial losses but also damages reputation, sometimes causing customers to switch to competitors. AIOps steps in as a vital tool to navigate the complexities of modern IT environments, providing a predictive approach to maintaining service performance and preventing issues. It achieves this by collecting data, defining Key Performance Indicators (KPIs), establishing baselines for typical performance, and building predictive models to anticipate and mitigate potential issues.   While the implementation of AIOps may seem challenging, tools and products are available to facilitate the transition, allowing organizations to start with simple use cases and gradually progress to more complex applications. Overall, AIOps offers a path to proactive IT operations management, enabling organizations to stay ahead of potential service performance issues and drive better business outcomes. The Challenge of Observability in the Age of Big Data   In today's digital landscape, organizations are grappling with the immense volumes of data generated by their IT environments. This surge in data presents a significant challenge in maintaining observability over their systems. As data volumes expand, the task of monitoring and managing the performance of IT services becomes increasingly complex. One of the critical issues faced in this scenario is the phenomenon of 'alert storms,' where the sheer quantity of alerts overwhelms the IT teams, making it difficult to pinpoint and troubleshoot performance issues effectively. The recent Splunk AI for Observability webinar revealed that organizations are indeed in the midst of what could be described as the 'perfect storm' of challenges, with many participants acknowledging struggles with growing data volumes, alert storms, and troubleshooting. Among these, too many alerts stood out as a particularly prominent issue, as it can obscure the root causes of performance degradation, leading to extended downtime and a scramble to restore services. The economic impact of such downtime is staggering. A survey published in the 'Digital Resilience Pays Off' report highlighted that, on average, each hour of downtime can cost organizations up to $365,000, not to mention the potential reputational damage that can arise from poor customer experiences. To combat these issues, organizations are turning to artificial intelligence (AI) to enhance their observability practices. AI is leveraged to predict or prevent incidents before they occur, and customers are segmented into three maturity stages based on their use of AI. The most advanced customers employ predictive models that can foresee and mitigate negative outcomes, whereas the less mature ones are yet to harness AI's full potential. In conclusion, the path to overcoming the challenges posed by growing data volumes lies in the strategic application of AI. The Evolution and Impact of AI and ML    Artificial Intelligence (AI) and Machine Learning (ML) have become integral components of Splunk products, offering significant advantages in detecting service performance issues. The integration of these technologies within Splunk's suite has a rich history, with nearly a decade of implementation. The profound impact of AI and ML is evident in the ability to predict or prevent incidents before they happen, enhancing the observability of systems. Organizations leveraging AI and ML in Splunk's offerings report substantial benefits, such as accelerated problem resolution and enhanced reliability of customer-facing and internal services. The evidence supporting the return on investment for those adopting observability practices is compelling, with statistics showing that each hour of downtime can cost an average of $365,000, highlighting the critical nature of maintaining operational efficiency. Successful AI and ML implementations have led to increased detection efficiency, reduced manual processing, and the identification of previously unknown scenarios within service performance. These advancements have been showcased through customer stories, such as the IG Group's transition from static thresholds to dynamic baselining, which has drastically improved detection efficiency. Another example is AIB's use of Splunk products to facilitate triage processes, leading to the discovery of an issue caused by unusual snowfall in Ireland. Lastly, StubHub's use of baseline models helped control application errors and uncover hidden issues. To streamline the adoption of AI and ML, Splunk ensures its products are accessible and supportive of users at any stage of their AI and ML journey, from simple use cases to complex predictive model deployments. The Splunk App for Anomaly Detection exemplifies this commitment, automating the detection of anomalies in key metrics and KPIs, thereby demonstrating Splunk's dedication to enhancing service performance through advanced technology. Enhancing Operational Efficiency with Anomaly Detection    The introduction of the Splunk App for Anomaly Detection marks a significant advancement in operational efficiency for organizations leveraging Splunk’s AI capabilities. A demonstration of this app in action reveals its capacity to effortlessly operationalize anomaly detectors for actionable alerting, thus simplifying the traditionally complex and technical process of anomaly detection. By automating the configuration for specific metrics or KPIs, the app streamlines the machine learning process to detect anomalies at ingest time, effectively removing barriers such as the need for complex SPL, statistical knowledge, and parameter tuning. The benefits of implementing anomaly detection are substantial and multifaceted. The demonstration highlights how this technology enables organizations to detect both isolated point anomalies and sustained anomaly intervals, accompanied by confidence scores to gauge the significance of the detected anomalies. Moreover, the app facilitates efficient detection by alerting users when anomalies with high confidence scores are found, and provides the SPL query generated by the app for further use within Splunk. Operational efficiency is further enhanced when anomaly detection is paired with predictive AI capabilities. Organizations can predict potential incidents before they occur, mitigating downtime and improving service reliability. This predictive approach is demonstrated by the Anomaly app's ability to identify anomalous behavior and to recommend appropriate actions in real-time, thus preventing potential service disruptions.     In conclusion, the Splunk App for Anomaly Detection exemplifies the intersection of AI and operational efficiency, providing a powerful tool for organizations to proactively manage and maintain the performance of their systems and services. Understanding Splunk's AI Principles and the AI Assistant   Splunk integrates AI into its observability suite, offering domain-specific AI capabilities that enhance the efficiency of AI-driven processes. The involvement of humans remains crucial, ensuring that AI assists rather than replaces human decision-making. A notable innovation is the introduction of the Splunk AI Assistant for SPL, which simplifies the creation of SPL queries and their understanding. This assistant, currently in public preview, promises to unlock the full potential of SPL-powered Splunk products. Statistics underline the importance of AI in observability, demonstrating how AI applications in Splunk significantly reduce downtime costs, which can average $365,000 per hour. With complex technology stacks, AI helps organizations predict and prevent incidents, using models to forewarn about potential issues and enabling quick resolution. The AI-driven capabilities are designed to be open and extensible, allowing users to customize models or employ their own, maintaining Splunk's versatile problem-solving essence. The Splunk AI Assistant aims to be a scalable aid across the ecosystem, with future enhancements like an AI assistant for observability cloud and an improved adaptive thresholding experience. In essence, Splunk's AI principles and the AI Assistant offer a comprehensive, user-friendly AI integration that fosters proactive, informed, and efficient observability practices. Enhancing Security and Observability with AI and Unified Platforms   The integration of Artificial Intelligence (AI) into observability practices significantly accelerates the ability to detect, investigate, and respond to incidents. AI's role in enhancing security and observability is pivotal, particularly in predicting or preventing incidents before they occur. Organizations are adopting AI to gain insights on what normal performance looks like in their environments and to detect deviations that may indicate emerging issues. With the growth of data volumes and the complexity of technology stacks, AI becomes an indispensable tool in managing alert storms and troubleshooting performance issues. A survey by Splunk has found that each hour of downtime can cost organizations an average of $365,000, underscoring the financial impact of operational disruptions beyond the reputational damage it may cause to customer-facing services. The comprehensive nature of the Splunk platform caters to this need, with its decade-long incorporation of AI and machine learning to support a wide range of use cases. Furthermore, the importance of a unified platform cannot be overstated. Splunk's unified platform serves as a cohesive foundation for SecOps, ITOps, and DevOps teams, providing solutions that are tailored to their specific requirements while reaping the benefits of a holistic approach. This unified approach ensures that these teams can operate more efficiently, with better visibility and tools for rapid response, thereby reducing mean time to resolution (MTTR) and mitigating the risk of service performance issues. AIOps: A Mature Approach to Observability   In the increasingly complex IT landscape, AIOps emerges as a mature approach to observability, playing a pivotal role in enhancing incident response and operational efficiency. This approach is fostered by the need to predict or prevent incidents before they occur, leading to continuous improvement and predictive analytics capabilities within IT operations. The integration of AIOps has demonstrated a significant impact, particularly in the realm of incident management. Organizations that embrace AIOps have observed a faster resolution of problems, resulting in improved availability and reliability of services. Notably, customers leveraging AIOps have reported the ability to fix issues more swiftly and confidently maintain customer-facing and internal services. A mature AIOps strategy encompasses various stages of implementation, ranging from basic visibility into IT environments to advanced predictive models. These models can anticipate potential negative outcomes based on historical data, enabling preemptive remediation efforts. The statistical evidence supporting AIOps is compelling, with reports indicating that each hour of downtime can cost an average of $365,000, highlighting the financial incentives to adopt such strategies. Moreover, AIOps facilitates the identification of previously unknown scenarios, thereby driving innovation and informed decision-making. The journey toward AIOps maturity not only increases detection efficiency but also reduces manual processing and uncovers new insights, all of which contribute to the overarching goal of achieving digital resilience and operational excellence.     In conclusion, the integration of AI and ML into observability practices is recognized as a transformative force in IT operations. It is demonstrated that these technologies significantly enhance the ability to predict and prevent incidents, leading to improved service reliability and customer experience. The financial benefits are underscored by the high cost associated with downtime, encouraging organizations to adopt AI and ML to maintain operational efficiency. Through various use cases, the effectiveness of AI-driven solutions in addressing complex technology stack challenges is illustrated, showing a substantial return on investment. A mature approach to observability, encompassing AIOps, is presented as essential for ongoing improvement and innovation within organizations. It is concluded that AI and ML are indispensable in the contemporary digital landscape for driving business resilience and efficiency.   1  From Digital Resilience Pays Of (p.7) by  Splunk © 2023 Splunk Inc.  ... View more

Elevating Digital Service Excellence: The Synergy of Real User Monitoring and Application Performance Monitoring In today's digital landscape, the adoption of Splunk Real User Monitoring (RUM) and Splunk Application Performance Monitoring (APM) is pivotal for organizations aiming to enhance their web presence and user experience. RUM is diligently employed to monitor the genuine interactions of users with web and mobile applications, providing a granular view of user experience in real-time. Concurrently, APM is harnessed to investigate application performance, offering critical insights into transaction speeds and system health. Both are integral components of an observability suite that caters to the multifaceted nature of modern digital ecosystems, which encompass on-premise, hybrid, and multi-cloud environments. The integration of RUM and APM is showcased as a strategic move within a growth engineering team, underscoring its significant role in improving marketing operations and public-facing websites. By using observability tools, we can take a preemptive and informed approach to delivering digital services. This shifts incident management from reactive to showcasing digital resilience and excellence.   Understanding Splunk RUM and APM in Observability Practices     Driving Customer Experience With Splunk Observability: A Growth Engineering Team's Story Learn  how the growth engineering team at Splunk adopted Splunk Observability to improve incident detection, resolution, and customer experience. Watch Real User Monitoring (RUM) and Application Performance Monitoring (APM) are essential components of Splunk's observability suite. Their adoption by Splunk's growth engineering team signifies a strategic move towards enhancing the company's marketing operations and public-facing websites. RUM is integral for monitoring the actual experiences of users interacting with web and mobile applications, while APM focuses on the performance of applications, offering insights into transaction speeds, error rates, and system health. Splunk's observability products offer a unified solution for monitoring digital ecosystems that span across on-premise, hybrid, and multi-cloud environments, addressing the complexities that come with integrating multiple technologies and services. By adopting Splunk observability, organizations can achieve digital resilience, enabling them to recover swiftly from disruptions, adopt new operating models quickly, and ensure reliable and outstanding digital experiences for their customers. The growth engineering team within Splunk's marketing organization is a prime example of how internal teams are leveraging these tools to improve efficiency, detect incidents faster, and align business priorities. The team's journey to utilizing RUM and APM has led to impressive results, such as faster page load times, increased engineering efficiency, and significant improvements in core web vitals. These improvements are a testament to the power of a comprehensive observability strategy, which can transform reactive incident management into a foresighted approach to digital service delivery. The Growth Engineering Team's Path to Digital Resilience     Leveraging Splunk Observability for Complex Technology Stacks Explore how Splunk transitions its AEM stack to the cloud and leverages Splunk Observability to manage complex technology ecosystems and deliver exceptional customer experiences. Watch now   The Growth Engineering Team at Splunk has embarked on a digital resilience journey, with the mission to maintain public-facing websites and internal portals. Their objective is to provide a world-class customer experience by leveraging the power of Splunk products. To achieve this goal, the adoption of observability tools became imperative to address inefficiencies and the apparent lack of service health visibility. With the introduction of Real User Monitoring (RUM) and Application Performance Monitoring (APM), the team is now equipped to detect incidents, isolate and prioritize events, and accelerate root cause analysis effectively. This strategic move has enabled the team to transform from a reactive to a forward-thinking approach, enhancing their ability to recover quickly from disruptions and adopt new operating models seamlessly. The adoption of Splunk's observability tools has proven to be a critical step in the team's journey towards digital resilience. Observability has provided the team with end-to-end visibility, allowing them to monitor the health of their services and correlate events across different teams and microservices. This unified perspective is essential for resolving issues rapidly and efficiently. As a result of these efforts, the team has reported significant improvements in key performance indicators, including faster page load times, increased engineering efficiency, and improved core web vitals. The team's utilization of RUM has been particularly impactful, allowing them to track automated user sessions across websites and applications. For instance, when trial sign-ups on the website were failing, RUM enabled the engineering team to quickly identify and resolve the issue, minimizing the potential impact on users. Similarly, APM has been instrumental in ensuring the performance of business-critical workflows. During a product release, APM allowed the team to address an outage alert swiftly, maintaining a 99.9% uptime and boosting engineering productivity by 50%. Overall, the integration of RUM and APM has set the stage for a more resilient digital presence, empowering the Growth Engineering Team to deliver superior customer experiences and drive business success. This journey towards digital resilience serves as a testament to the power of observability tools in optimizing user experience and operational efficiency.   Maximizing Digital Resilience: The Power of APM and RUM in Action     Comprehensive Application Performance Monitoring and User Experience Analysis With Splunk APM and RUM Discover how Splunk APM provides distributed tracing and detailed error capture, while RUM offers real-time user experience monitoring and error tracking. Watch now Application Performance Monitoring (APM) and Real User Monitoring (RUM) are crucial for ensuring service health and optimizing user experiences. Internally, Splunk leverages its own suite of products, including APM and RUM, to monitor and enhance its service offerings. The utilization of these tools within the growth engineering team is shared, demonstrating the tangible advantages of APM and RUM. APM and RUM collectively provide comprehensive visibility into how services perform and how users interact with applications. By integrating both front-end and back-end monitoring, teams can rapidly identify and resolve issues, often before they significantly impact users. These tools offer capabilities ranging from detailed waterfall charts of user interactions to real-time correlation of traces, which are instrumental in accelerating troubleshooting efforts. Splunk's internal stories serve as a testament to the effectiveness of APM and RUM. One case study highlights the ability of RUM to detect and address a failure in website sign-up forms, allowing for a swift resolution that was three times faster, thereby preserving the company's brand reputation. Another example showcases how APM enabled the back-end engineering team to maintain a 99.9% uptime and improve engineering productivity by 50% despite increased traffic. The integration of APM with RUM is particularly beneficial, as it connects front-end user experiences with back-end service performance, providing a holistic view of the system. This end-to-end visibility is crucial for monitoring complex ecosystems, such as Splunk.com, with its multitude of endpoints. By using APM and RUM in unison, teams can now monitor and optimize Splunk's digital ecosystem more efficiently. The success of APM and RUM integration at Splunk is quantifiable, with impressive key performance indicators (KPIs) such as a 50% faster page load times, a 25% increase in engineering efficiency, and a 60% improvement in core web vitals. These statistics underline the transformative impact of Splunk's observability tools in creating resilient digital experiences and fostering a forward-thinking approach to service health management.   Understanding APM and RUM Capabilities for Optimized User Experience     Maximizing User Satisfaction and Business Goals With APM and RUM Correlation Discover how the correlation of Application Performance Monitoring (APM) and Real User Monitoring (RUM) provides end-to-end visibility, effective root cause analysis, and improved performance optimization. Watch now Enhancing Digital Experiences with APM and RUM   The implementation of Splunk Application Performance Monitoring (APM) and Splunk Real User Monitoring (RUM) within growth engineering teams has led to significant advancements in page load times and engineering efficiency. Through the implementation of these tools, businesses have observed a significant enhancement in core web vitals and the ability to proactively identify issues. This shift towards a more foresighted approach has been facilitated by the adoption of observability, resulting in a transformation of operational strategies. The integration of APM and RUM has empowered teams to gain comprehensive insights into both front-end and back-end systems, enabling a unified approach to issue resolution. This has led to a more efficient and effective method for addressing incidents, with teams now able to quickly zoom in on the performance of critical workflows and preemptively address potential issues before they escalate. The success stories shared emphasize how APM and RUM have revolutionized the monitoring and optimization of digital services, ensuring high availability and optimal performance. These tools not only provide real-time visibility into user experiences but also facilitate faster troubleshooting and resolution, ultimately enhancing the digital experience for both users and the organization. Conclusion  In conclusion, the implementation of Application Performance Monitoring (APM) and Real User Monitoring (RUM) is demonstrated to be essential for achieving digital resilience. Enhanced user experiences and operational efficiency are achieved through the adoption of these monitoring tools. Organizations are empowered to proactively identify and resolve issues, thereby maintaining high service uptime and improving core web vitals. The integration of APM and RUM enables a seamless correlation between user interactions and application performance, offering a comprehensive observability solution. This approach results in significant improvements in page load times and engineering productivity. By leveraging the capabilities of APM and RUM, a transformation in incident management is facilitated, transitioning from a reactive to a preemptive stance in digital service delivery. Speakers Sudhaker Adusumilly, Senior Director, Head of Growth Engineering, Splunk Sandeep Kampa, Sr DevOps Engineer, Growth Engineering, Splunk   Looking for More? Watch the full Demo   In this demo, Sandeep Kampa, Sr DevOps Engineer at Splunk discusses the powerful capabilities of Splunk APM and RUM, demonstrating how they can revolutionize application performance and user experience. He showcases key features such as service maps, error tracking, and the correlation between APM and RUM for comprehensive front-end and back-end analysis. The walkthrough includes a practical example of troubleshooting a real-world issue with the integrated tools, highlighting their ability to reduce resolution time and improve operational efficiency. Watch now   Watch the full Tech Talk   ... View more

Watch Now!   In this Extending Observability Content to Splunk Cloud Tech Talk, you'll see how to leverage Splunk Observability data in your Splunk Cloud Platform! Key Takeaways Improve your Splunk platform’s capabilities with Splunk Observability Cloud Accelerate root cause analysis with Related Content in Splunk Cloud Platform Enhance troubleshooting with Splunk Infrastructure Monitoring and Splunk Application Performance Monitoring Replay Tech Talk ... View more

How to Use Logs from Splunk Platform in Splunk Observability   Logs play a critical role in identifying why there is a problem and, when combined with metrics and traces, help build strong observable systems. For that reason, ITOps and engineering teams need a robust and well-rounded logging tool to be able to successfully perform troubleshooting and monitoring across any environment. However, they often have to deal with tool sprawl that leads to a broken troubleshooting workflow and longer time to resolve or over complicated solutions that require coding or infrastructure overhead. In this webinar, find out how to combine Splunk Platform’s power with Splunk Observability Cloud. You’ll learn how easily you can extend logs from Splunk Cloud Platform or Splunk Enterprise into Splunk Observability Cloud, a solution purposefully built for hybrid troubleshooting. You’ll also discover how seamless it is to correlate logs with traces and metrics in a single point-and-click interface for improved visibility of your environment.   The Convergence and Power of Observability in Modern Software Development Understanding the convergence of Observability as a powerful group of different parts that work together to improve business resiliency and solve operational inefficiency. Watch Now Maximizing the Power of Splunk Cloud and Observability Cloud Integration Learn how to seamlessly connect and leverage the combined benefits of Splunk Cloud and Observability Cloud for streamlined log analytics and real-time monitoring. Watch Now Understanding Log Observer Connect in Splunk Cloud Explore the process of setting up Log Observer Connect between Splunk Observability Cloud and Splunk Cloud, including prerequisite steps and role-based access control. Watch Now Automating Network Level Connection for Observability Cloud and Splunk Cloud Learn how to automate the secure network level connection between Observability Cloud and Splunk Cloud for seamless data access and troubleshooting. Watch Now   Want to see more? Watch the Full Webinar   Key Takeaways: How to centralize your log management across ITOps and engineering use cases Enjoy a no-code and intuitive interface to easily explore log data Combine logs with Infrastructure Monitoring (metrics) and APM (traces) for faster root cause analysis Speakers: Joanna Zouhour, Product Marketing Manager Observability, Splunk Subu Baskaran, Principal Product Manager, Splunk     ... View more

    Unleash the power of Splunk Observability   Watch Now In this can't miss Tech Talk! The Splunk Growth Engineering team will demonstrate one of our newest capabilities, Session Replay feature, which offers an unparalleled level of transparency into your users' journey. We will also share how our Application Performance Monitoring (APM) can help diagnose issues with your backend systems, and demonstrate how to connect frontend user flows with backend application systems. Join us to understand the capabilities of Splunk Observability Real User Monitoring (RUM) and APM Products and unleash the power of Splunk Observability. ... View more

Let’s talk thresholding. Have you set up static thresholds? Tired of static thresholds triggering false alarms? Interested in dynamic, adaptive thresholds but don’t know where to start or spend too much time setting them up? Dive into the world of adaptive thresholds and discover how they help drive accurate alerting so you can reduce false positives, cut alert noise, and better understand when something’s gone wrong. The best part? Find out how ML-Assisted Thresholding takes it a step further by configuring adaptive thresholds for you at the click of a button. It’s time to embrace the new era of alerting and efficiency!   Topics covered include: The importance of adaptive thresholding in ITSI How to use machine learning to help automatically create adaptive thresholds in Splunk ITSI Behind-the-scenes of the recommendation algorithm powered by Splunk AI Who will benefit: Splunk Administrator, VP of IT Operations, IT Operations Manager, IT Operations Engineer, IT Operations Analyst, IT Systems Engineer IT Systems Analyst, Systems Administrator, IT Support Analyst, Incident Responders, Platform Engineers, Frontend Engineers, Site Reliability Engineers, DevOps Engineers, Directors of Web and eCommerce, Director of UI / UX and Mobile App Developers and more! ... View more

  Watch on-demand    Prevent unplanned downtime with Splunk | Featuring Travelport Distributed ecosystems, tool sprawl, and high customer expectations.  Those are only a few of the many challenges that make it increasingly more difficult for ITOps, engineers, and developers to fully track what happens in their environments. In this context, it can take too long to detect incidents, diagnose them, and resolve them before they impact customers. Find out how Travelport , a Splunk customer, leverages Splunk to address infrastructure and application issues with a unified and integrated observability approach. Learn about real-world troubleshooting and monitoring use cases and see how the combination of Splunk Cloud and Splunk Observability helps Travelport extract meaningful insights and take action promptly.  What you’ll learn: Perform real-time troubleshooting with logs and traces in a codeless environment Improve incident response journey with glass tables view of your IT environment Collect key insights in dashboards for better business performance analysis Who will benefit: Splunk Administrators, Frontend Engineers, Site Reliability Engineers, DevOps Engineers, Directors of Web and eCommerce , Director of UI / UX, Mobile App Developers, Platform Engineers. IT Operations Engineers, Systems Administrators and many more. About Travelport Travelport is a global technology company that powers bookings for hundreds of thousands of travel suppliers worldwide. Buyers and sellers of travel are connected by the company’s next generation marketplace, Travelport+, which simplifies how brands connect, upgrades how travel is sold, and enables modern digital retailing. Headquartered in the United Kingdom and operating in more than 165 countries around the world, Travelport is focused on driving innovation that simplifies the complex travel ecosystem. ... View more

Observability Unveiled: Navigating OpenTelemetry's Framework and Deployment Options A recent Tech Talk, Starting With Observability: OpenTelemetry Best Practices explored the foundational technology and framework of OpenTelemetry, focusing on its role in enabling observability practices and its rapid adoption across various industries. It emphasizes the value of deploying the OpenTelemetry collector and the recommended modes of deployment, such as Agent mode and Gateway mode. Hear from Splunk’s Observability Practitioner Director, Greg Leffler, as he delves into tips and tricks for using OpenTelemetry, understanding the deployment options, and choosing the appropriate deployment model based on the specific needs of the environment.   Exploring OpenTelemetry: An Introduction to Observability Understanding OpenTelemetry: A Framework for Observability Learn about OpenTelemetry, a foundational technology used for observability, its components, integrations, and benefits for monitoring and instrumentation across various platforms and languages. OpenTelemetry, a buzzword that has caught the attention of many, is the focus of this edition of Splunk's Tech Talks. The session delves into the foundational technology and framework that OpenTelemetry offers for observability. It is designed to capture distributed traces, metrics, logs, and more, providing a standard way to enrich data. OpenTelemetry also stands out for its flexibility and lack of vendor lock-in, offering users full control over their telemetry data. The webinar highlights the importance of OpenTelemetry, emphasizing its role in enabling observability practices and its rapid adoption across various industries. According to the speaker, a significant number of organizations are already embracing observability, recognizing its value and the need for dedicated experts in this field. The session also addresses the process of getting started with OpenTelemetry, emphasizing the significance of deploying the OpenTelemetry collector and the recommended modes of deployment, such as Agent mode and Gateway mode.   Implementing OpenTelemetry: Starting Small and Necessary Configurations Getting Started With Splunk and OpenTelemetry: Installation and Configuration Guide Learn how to easily start using Splunk with OpenTelemetry, whether you're a Splunk customer or not, by following the installation and configuration steps. When implementing OpenTelemetry, it is crucial to start small and gradually expand a deployment. By beginning with one service or a specific environment, it allows for a better understanding of the benefits and facilitates easier configuration adjustments. Additionally, it is important to ensure the necessary extensions are enabled for seamless troubleshooting and data processing. Extensions such as health checks, zpages, and batch are essential for monitoring the performance of data pipelines. Moreover, log collection via the Filelog receiver can be considered to reduce the number of needed agents. By following these steps, the implementation of OpenTelemetry can be streamlined and optimized for effective observability. It's also worth noting that the majority of business cases involve the deployment of OpenTelemetry in Agent mode, which is the most common and recommended approach for collecting host metric data and forwarding application data to the observability backend. However, in complex environments or where scalability and network traffic need to be managed, deploying the collector in gateway mode might be more suitable.   Tips and Tricks for Using OpenTelemetry   When using OpenTelemetry, it is essential to consider the deployment mode that best suits your environment. The most common mode is the Agent mode, which is deployed onto every machine to collect host metric data and forward application data to the observability backend. However, for complex environments with potential network constraints or scalability concerns, the Gateway mode is a viable option. In Gateway mode, the agent can still run on every machine but it would then direct data to a gateway machine before sending it to the observability backend, addressing issues related to network traffic and data volume. Additionally, in cases where the collector is not required, such as in platform-as-a-service systems, applications can directly emit data to endpoints without the need for a collector. Understanding the deployment options and choosing the appropriate mode based on the specific needs of the environment is crucial for effective implementation of OpenTelemetry.   Understanding OpenTelemetry Collector Deployment Modes Understanding the OpenTelemetry Collector and Ecosystem Explore the functionalities of the OpenTelemetry collector, including receivers, processors, extensions, and exporters, enabling efficient data collection and integration with various formats and destinations. The OpenTelemetry collector offers two deployment modes: Agent mode, Gateway mode. You can also use OpenTelemetry without the collector (No Collector mode). In Agent mode, the collector is deployed on every machine to collect host metric data and application data, forwarding it to the observability backend. This mode is recommended for most common environments. On the other hand, Gateway mode involves running the agent on every machine but instead of sending data directly to the observability backends, sending it to a gateway machine, which then communicates with the observability backend. Gateway mode is suitable for complex environments, large-scale deployments, and where network traffic and overall performance of the observability infrastructure need careful consideration. Alternatively, in scenarios where the collector is not required, such as in platform as a service systems, applications can directly emit data to endpoints without the need for a collector. This 'No Collector' mode is ideal for simple applications that require minimal instrumentation. Understanding these deployment modes is crucial for optimizing the performance and efficiency of the OpenTelemetry collector in different deployment scenarios. Ready to Watch the Full Recording? Starting With Observability: OpenTelemetry Best Practices Watch the Replay Exploring Key Features of OpenTelemetry Exploring Zpages: A Hidden Gem in OpenTelemetry Collector Discover the power of Zpages in OpenTelemetry Collector for monitoring and troubleshooting pipelines with ease. OpenTelemetry offers a range of powerful features designed to enhance observability and streamline the collection of crucial data. One noteworthy feature is the zpages extension, which provides valuable troubleshooting insights. By enabling the zpages extension during the setup of the OpenTelemetry pipeline, users gain access to configz, statsz, rpcz, and tracez. These components offer real-time visibility into the internal workings of the collector, simplifying the troubleshooting process and providing essential performance metrics. The OpenTelemetry pipeline is a core component that supports receivers, processors, and exporters. Receivers are capable of accepting data in various formats, including OTLP, Jaeger, Prometheus, and more, ensuring flexibility in data ingestion. Processors allow for data manipulation, such as sampling, batching, and filtering, offering extensive customization options. Exporters then facilitate the transfer of data out of the collector, enabling seamless integration with observability backends. Furthermore, OpenTelemetry promotes both automatic and manual instrumentation.   Getting Started with OpenTelemetry Getting Started With Splunk and OpenTelemetry: Installation and Configuration Guide Learn how to easily start using Splunk with OpenTelemetry, whether you're a Splunk customer or not, by following the installation and configuration steps. To begin with, automatic instrumentation should be the starting point for any OpenTelemetry deployment. It eliminates the need to understand OpenTelemetry in significant depth, making it a suitable option for most use cases. However, if automatic instrumentation is not available for certain languages or frameworks, manual instrumentation can be added as needed. For Splunk customers, using resources such as the Cloud Native Computing Foundation Slack and Splunk Professional Services is highly recommended. The Slack channel provides real-time access to OpenTelemetry experts for specific problem-solving, and Splunk Professional Services can offer valuable support and guidance, with assigned experts on Splunk and OpenTelemetry available. Joining the Cloud Native Computing Foundation Slack is essential for gathering insights and assistance related to OpenTelemetry. The community-focused program allows users to engage with knowledgeable individuals and stay updated on the latest developments. It's crucial to initiate an OpenTelemetry deployment as soon as possible, as it provides an opportunity to gain experience and familiarity with the system.   Deploying the Collector: Platforms and Steps Getting Started With Splunk Observability Cloud: Deploying the OpenTelemetry Collector Learn how to deploy the OpenTelemetry Collector in Splunk Observability Cloud and configure it using the provided configuration files. The OpenTelemetry collector can be deployed on various platforms, including Linux, Windows, Kubernetes, and EC2. The deployment can also be carried out in different modes, such as agent or gateway. It is recommended to use the agent mode for deployment. In Splunk Observability Cloud, the process begins with clicking on the 'Add Integration' option and selecting OpenTelemetry as the integration of choice. This initiates a wizard to set up the collector. The installation process requires the use of cURL to download the setup script, which then determines the realm of the observability cloud install and the access token to be used. Additionally, the Java zero-touch instrumentation can be enabled during the deployment process. Once the deployment is completed, the configuration file for the collector is located at /etc/otel/collector/config.yaml, and the environment variables are specified at /etc/otel/collector/splunk-otel-collector.conf.   Configuring the Splunk OpenTelemetry Collector Getting Started With Splunk Observability Cloud: Deploying the OpenTelemetry Collector Learn how to deploy the OpenTelemetry Collector in Splunk Observability Cloud and configure it using the provided configuration files. The configuration of the Splunk OpenTelemetry Collector is an essential aspect of setting up and optimizing your observability platform. The collector relies on two key configuration files, which hold critical information about the collector's operation and behavior. The configuration file for the collector is located at /etc/otel/collector/config.yaml, while the environment variables are specified in /etc/otel/collector/splunk-otel-collector.conf. These files play a pivotal role in determining how the collector runs, the pipeline structure, and the settings for extensions and environment variables. Making changes to these configuration files is a straightforward process. Any modifications to the configuration files must be followed by a restart of the collector to ensure the changes take effect. It is important to note that the OpenTelemetry Auto Instrumentation package can also be installed, which automatically updates the ld.so.preload file, enabling automatic instrumentation of Java applications by simply restarting them.   Exploring OpenTelemetry Features: Enabling zpages Discover the power of Zpages in OpenTelemetry Collector for monitoring and troubleshooting pipelines with ease. Enabling zpages in the OpenTelemetry collector provides valuable insights into the system's performance and operation. These zpages offer a comprehensive view of the collector's inner workings and facilitate efficient monitoring and troubleshooting. Introduction to zpages: Zpages are an essential feature of the OpenTelemetry collector, allowing users to gain visibility into various aspects of the system. They offer detailed information about the collector's configuration, pipeline, extensions, and traces, enabling users to better understand the behavior of the collector. Different types of zpages: There are several types of zpages, each serving a specific purpose. These include the config zpage, pipeline zpage, extensions zpage, feature zpage, and trace zpage. Each type provides distinct insights into the functioning of the OpenTelemetry collector, allowing for comprehensive analysis and monitoring.   Conclusion In conclusion, OpenTelemetry plays a pivotal role in enabling observability practices, offering a standard way to enrich data and providing flexibility without vendor lock-in. The deployment of the OpenTelemetry collector, with a focus on agent mode and gateway mode, is crucial for effective data collection and forwarding to the observability backend. The deployment process involves utilizing cURL to download the setup script, determining the realm of the observability cloud install, and configuring the access token. Related Tech Talk OpenTelemetry: What’s Next. Logs, Profiles, and More. Watch the Replay   ... View more

    Hear from Morgan McLean, director of product management and one of the co- founders of OpenTelemetry. You'll learn about OpenTelemetry's new logging functionality, including its two logging paths, the benefits of each, and real-world production examples. We'll show the power of the next wave of OpenTelemetry enhancements, including profiling and the insights that this unlocks in combination with distributed traces, and how we're extending your observability to client applications. We'll wrap up with a Q&A and answer your most pressing questions. Related Tech Talk | Starting With Observability: OpenTelemetry Best Practices Watch the replay ... View more

Tech Talk Starting With Observability: OpenTelemetry Best Practices   In this Tech Talk , learn about what OpenTelemetry is, why it’s the future of Observability, and how to use the Splunk Distribution of the OpenTelemetry collector in a demo. We’ll also discuss design considerations about an OpenTelemetry collector deployment and the components of the OpenTelemetry pipeline and how they can impact performance.   Read the Tech Talk blog, Observability Unveiled: Navigating OpenTelemetry's Framework and Deployment Options   ... View more

During a recent Observability Tech Talk, attendees tuned in to discover Splunk's approach to digital experience monitoring. Splunk experts Connor Tye and Nivedita Narayanan discussed the different elements of Splunk's Digital Experience Monitoring (DEM) portfolio and how it can help you optimize your customer experience. You'll want to keep reading and explore how Splunk's DEM can help detect, alert and take action to address issues quickly and effectively.   Dive into the details of Splunk's Digital Experience Monitoring "Optimizing Digital Experiences with Splunk Observability: Combining Synthetics and RUM for Performance Monitoring"   The Importance of Digital Experience Monitoring for E-commerce Sites   Learn why robust digital experience monitoring is crucial to prevent card abandonment and build user trust in e-commerce sites. Digital experience monitoring is an important part of observing the performance and usability of websites and web applications. It is essential in today's world of digital transformation, where users demand seamless experiences and optimal performance from all the services they interact with. Splunk Observability offers two complimentary products for digital experience monitoring: Synthetics and Splunk Real User Monitoring (RUM). Synthetics uses synthetic data to identify potential performance issues before they reach customers, while RUM provides real user insights to understand how users are interacting with the service. Combining these two products provides a well-rounded solution for monitoring pre-production and production environments. Digital experience monitoring is no longer about simply monitoring uptime, as expectations have changed and the way services are hosted has evolved. With digital experience monitoring, we can monitor changes in page load time, web vitals, and third party services, thus ensuring a smooth user experience.   Monitoring Website User Experiences with Digital Experience Monitoring (DEM)   Digital Experience Monitoring (DEM) is a great tool to help identify user experiences. In this talk, we will discuss how DEM can be used to monitor a website. We will use a simple e-commerce website as an example. As a service owner of the website, we have noticed a delay in getting to checkout, which is not a great user experience. We will use Synthetics and RUM to measure the impact on the website. Synthetics is a set of transactions put together that each translate to steps and actions. For example, when a user searches for a product, adds it to their cart and checks out, these are three different actions that can be grouped into three transactions. RUM uses real data from users to build a portfolio. It requires little instrumentation and can be set up in less than an hour. With DEM, it doesn't matter what infrastructure is being used - on-prem, hybrid, or cloud customers can all access the service. Let's use DEM to monitor our website and see how it can help improve user experience.   Synthetic Monitoring Tool: Understand Your Workflow Performance and Identify Issues Quickly.   A great way to understand the performance of any workflow is to use the Synthetic Monitoring tool. This tool gives you a visual representation of what the user saw, as well as metrics associated with it. Through the waterfall feature, you can easily determine what elements of the page may be lagging and causing issues. Additionally, you can set up business transactions to specifically evaluate the checkout flow. For example, if the checkout flow is taking too long, you can immediately identify it in the Synthetic Monitoring tool. Moreover, if you have an APM product set up, you can further drill down to the root cause of the issue. Additionally, if the same page is instrumented for Real User Monitoring (RUM), you can get a landscape view of the different environments the issue is happening across. Through this, you can get a better understanding of how many people are using your product, and the different platforms and devices that are supported. This helps to provide a uniform user experience across the globe. "Proactively Identifying Performance Issues with Synthetic Monitoring and RUM Data"   Troubleshooting User Sessions in Multiple Browsers and Regions   Explore user sessions across different browser versions and regions to identify delays and troubleshoot performance issues. Synthetic monitoring allows us to proactively identify issues before users encounter them and ensure consistent performance across diverse usage scenarios. The Synthetics Run results page automatically populates RUM data and matches it with the normalized URL of the synthetic test. We can localize the RUM distribution to the same country or area as the test ran from, or switch to global users. RUM field data allows us to optimize user experience by providing insights into how real users interact with the application across geographic locations, devices, and browser platforms. This helps us to identify performance bottlenecks, pinpoint areas of improvement, and prioritize fixes for maximum user impact. "Optimizing the User Experience through Splunk's Synthetic Monitoring and Web Optimization Tools."   Splunk Synthetic Monitoring and Web Optimization: Enhancing User Experience   Learn how Splunk synthetic monitoring and web optimization can help identify and fix performance issues, ensuring a seamless user experience on your website.   Splunk's Synthetic Monitoring and Web Optimization capabilities provide users with the ability to test features and user journeys before they go live, receive step-by-step guidance to immediately improve their end-user experience, and prioritize issues based on severity and ease of solving them. Web optimization extends the monitoring process by providing insights into how to improve performance, and benchmarking pages against lighthouse scores, core web vitals and industry standards. It also allows users to compare pre- and post-code change performance, to test the results of new code. Web Optimization also assigns defects to the relevant roles, such as developers or designers, and orders them based on severity. "Splunk Synthetic Monitoring Enhances Setup Process with Record Functionality and Integrations"   Enhanced Integrations and Alerting Capabilities for Splunk Synthetic Monitoring   Discover the latest integrations and features of Splunk Synthetic Monitoring, including enhanced metric sending, dashboarding, alerting, and on-call notifications. Splunk recently introduced a new feature in synthetic monitoring to make the setup process faster. This feature, known as the Chrome Script Importer, enables users to record all interactions on a page and submit the file for the test setup. Additionally, Splunk synthetic monitoring has released five integrations with other Splunk products. These integrations allow users to send metrics via HECs for Splunk enterprise or cloud, send metrics to Splunk infrastructure monitoring, and view a digital experience in Splunk ITSI. Splunk on call also allows users to send on- call alert notifications to multiple team members. To further help users, Splunk provides guides, videos, and an eBook with best practices to optimize performance. Splunk also has a community site with answers and resources for digital experience monitoring. Lastly, Splunk ideas allows customers to submit new product enhancements or vote for existing ideas. We hope you have gained a better understanding of the features and capabilities of Rigor and Synthetics. If you have any other questions, please reach out and we'll be happy to help.   Want to learn more? Watch the full Tech Talk on-demand, From Clicks to Conversions: Tune Performance from the User Perspective. Also view resources and all the pressing questions submitted during the live event.    ... View more