November 2022 Summaries

Dynatrace illustrates that achieving NoOps, the concept of fully automated IT operations requiring minimal human intervention, is feasible through the adoption of infrastructure-as-code and modern AIOps. By utilizing its AI engine, Davis®, Dynatrace automates the detection, identification, and resolution of issues, significantly reducing the mean time to repair (MTTR). This approach has enabled Dynatrace to increase release frequency and reduce production bugs, demonstrating the practicality of NoOps at scale. Dynatrace has also developed the Cloud Automation control plane, which extends these capabilities to DevOps pipelines, enabling teams to automate repetitive tasks and focus on innovation. Additionally, the company introduced Monitoring-as-code (Monaco) to streamline application monitoring processes. While NoOps may not completely eliminate the need for operations staff, it allows organizations to reduce operational costs and improve application performance by focusing on customer satisfaction.

AWS Compute Optimizer has incorporated third-party metrics from Dynatrace to enhance user insights into memory usage of Amazon EC2 instances, which is set to be announced at the Amazon re:Invent 2022 conference. This integration allows Dynatrace to dynamically feed real-time memory metrics to AWS Compute Optimizer, facilitating automatic detection and adjustment of memory allocation to optimize application performance and reduce cloud costs. By continuously discovering new EC2 instances and providing detailed insights into memory consumption, the system can recommend adjustments for both overprovisioned and underprovisioned memory scenarios. This capability enables users to right-size memory allocation, ensuring efficient resource use and improved service reliability. With this advancement, alongside support for numerous other AWS services, the platform offers a comprehensive observability solution that empowers AWS customers to make intelligent, automated decisions about their cloud resource management.

Dynatrace is collaborating with AWS as a launch partner for Amazon Lambda SnapStart, a new capability that significantly reduces startup latency for AWS Lambda functions by resuming execution environments from encrypted snapshots rather than initializing them from scratch. This innovation addresses latency issues in application modernization, particularly for APIs and microservices, by improving startup times up to tenfold at the 99th latency percentile. Dynatrace's Software Intelligence Platform, available through the AWS Marketplace, provides end-to-end observability and AI-driven insights, which aid IT teams in optimizing AWS Lambda functions and enhancing user experience by monitoring metrics such as CPU, memory, and network health without manual configuration. The platform offers seamless integration with AWS services like EC2, ECS, and Fargate, ensuring enterprise scalability and reducing the operational burden on cloud operations and site reliability engineering teams. Dynatrace's real-time analytics, facilitated by the Davis AI engine, help in automatically detecting and analyzing performance metrics, thus supporting application modernization efforts with intelligent automation and precise root-cause analysis.

Amplify PowerUP, a partner enablement event by Dynatrace, focused on advancing cloud modernization through observability, highlighting the importance of integrating observability as a core component rather than an add-on. The event featured keynote speeches from Dynatrace executives, including CEO Rick McConnell and VP Michael Allen, who emphasized the role of partners in driving innovation and business success. Michael Allen discussed Dynatrace's strong financial performance and introduced the new Services Endorsement Program, designed to enhance partner capabilities and meet increasing customer demands. Rick McConnell underscored the significance of partnerships for Dynatrace's growth and the necessity of delivering precise, AI-powered digital interactions to meet high user expectations. Steve Tack, SVP of Product Management, introduced Grail, a new data lakehouse that unifies observability, security, and business data, and discussed the evolving role of observability and the capabilities of Davis, Dynatrace's AI engine. The event also included sessions on partner enablement strategies, with insights from Matt Keenan, Isabel Carvahlo, and technical experts on leveraging marketing tools, training, and certifications to enhance partner effectiveness and generate revenue. The event concluded with a look forward to continued collaboration and innovation in 2023.

During the holiday shopping season, IT teams aim for the ambitious goal of five-nines availability, or 99.999% uptime, to ensure systems can handle peak loads without disruption. This level of availability, often pursued by site reliability engineers, is challenging due to the complexity of modern IT environments that include cloud-native technologies, multicloud settings, and edge processing. While achieving five-nines availability is seen as the ultimate benchmark, it is often unrealistic and costly, prompting organizations to aim for more attainable uptime goals that still satisfy user expectations. To improve system reliability, teams employ strategies like gathering observability data, establishing service-level objectives, integrating infrastructure monitoring, applying AI for real-time root-cause analysis, and automating IT operations. Despite the challenges, organizations can enhance their system availability by adopting an AIOps platform approach, which enables proactive problem detection and remediation under peak loads.

The continuous memory profiler introduced by Dynatrace helps developers identify and address the root causes of performance issues related to Java garbage collection, which can impact scalability and CPU usage. Java's garbage collection, though beneficial for automatic memory management, can lead to reduced application performance when objects are frequently or slowly collected. The profiler provides insight into the execution tree and methods responsible for object creation, allowing developers to pinpoint and optimize code by reducing unnecessary allocations. This optimization speeds up applications by decreasing garbage collection time and frequency, ultimately leading to better resource utilization. The tool is particularly useful in large applications where understanding the intricate details of memory management can be challenging. The profiler requires Java 11+ and can be activated through the Dynatrace platform, providing users with a detailed analysis of memory allocation hotspots and guidance for code improvement.

Service-level objectives (SLOs) are crucial in ensuring that microservices-based architectures meet agreed-upon service levels by setting specific, measurable targets that guide teams in delivering reliable and responsive software. Alongside service-level agreements (SLAs), service-level indicators (SLIs), and error budgets, SLOs help organizations maintain performance standards, assess release risks, and make informed decisions about software development. SLOs are used to strike a balance between innovation and reliability by defining acceptable downtime and allowing teams to automate processes and detect issues proactively. They are vital for maintaining software quality, improving decision-making, and promoting automation throughout the software delivery lifecycle. Effective SLOs are adaptable, aligned with business goals, and supported by automated tools like Dynatrace, which facilitate the creation, management, and monitoring of SLOs to ensure they meet evolving user expectations and technical requirements.

Work-life balance is crucial for mental health and well-being, yet many organizations fall short in supporting this balance, as shown by a Deloitte study, which found that 32% of respondents prioritize work over personal commitments, and only 48% feel their organization values their life outside of work. Dynatrace is actively addressing this by prioritizing employee well-being, offering paid time off, quarterly Wellness Days for employees to disconnect, and Volunteer Days for community service. In the "Get to Know Dynatracers" series, Hannah Seelye, an Investor Relations Analyst at Dynatrace, shares her positive experience with the company's authentic culture and her passion for volunteering. During the COVID-19 pandemic, Seelye became a mentor with Minds Matter Boston to support students from low-income families, exemplifying how Dynatrace's initiatives enable employees to pursue meaningful activities outside of work.

Dynatrace Managed version 1.254, released on November 16, 2022, introduces several new features and enhancements, including the ability to identify root causes for cluster events related to transaction storage truncation and configuration changes now visible in the Audit Log UI. Users can opt out of sending personal information to Mission Control, affecting access to Dynatrace support services. The update adds support for operating systems such as Debian 11, Rocky Linux 9.1, and SUSE Enterprise Linux 15.4, while announcing the end of support for several older Linux versions by 2026. The release resolves numerous issues across various components, improving application security, cluster management, synthetic monitoring, and more, with cumulative updates addressing specific problems like improper display warnings, session tag updates, and configuration migration errors. The release also fixes issues with user permissions, performance degradation, and user interface elements, ensuring enhanced functionality and stability for Dynatrace Managed users.

Averages, often used in application performance monitoring, can be misleading due to their inability to accurately represent data distributions, especially when outliers skew results. Averages assume a bell curve distribution, which rarely occurs in real-world applications that often have long-tail distributions with a few outliers significantly affecting the average. This can lead to misinterpretations and ineffective performance management. Percentiles, on the other hand, offer a more accurate representation by showing specific points in the data set, capturing the true performance experience for the majority of transactions. They are particularly useful for automatic baselining and alerting, as they provide a clearer picture of performance trends and degradations without the volatility and false positives associated with averages. Percentiles also aid in performance tuning by allowing targeted improvements on specific transaction segments, making them superior to averages in understanding and optimizing application performance.

OneAgent version 1.253, released on November 14, 2022, introduces several updates and enhancements across various platforms and technologies. Key developments include the discontinuation of 32-bit architecture support for ARMv7 devices in OneAgent for iOS, new support for SQLite3 and PostgreSQL in Node.js and Go, respectively, and enhancements in container monitoring with extended support for cgroup v2 in Kubernetes and Docker environments. Additionally, AWS Nitro hypervisor detection is now available on Windows and Linux, and there is improved tracing support for SQS-triggered AWS Lambda in Python and Node.js. The release also addresses multiple resolved issues, such as improved metric calculations, stability enhancements for .NET modules, and input validation improvements for Real User Monitoring. Future support changes are announced for several operating systems, including Linux and Windows versions, with support ending between 2025 and 2026.

DevOps teams often face challenges with overwhelming alerts from observability tools due to insufficient context about the problem's impact and root cause. Effective alerting should be automatic, accurate, and actionable, providing timely root cause analysis, which is often hindered by fragmented information from multiple tools and lack of coordination among teams. The Dynatrace platform, with its AI engine Davis, enhances root cause analysis by automatically identifying root causes and impacted entities using a comprehensive dependency map. However, it often lacks the human context necessary for a complete understanding of incidents, such as release authorizations or configuration changes. By utilizing the Dynatrace events API, teams can enrich problem reports with human factor data, facilitating more precise root cause detection and efficient incident response. This integration allows for automatic ticket routing and workflow triggering, ensuring the right teams are informed and equipped to address issues effectively.

Dynatrace has expanded its PurePath distributed tracing and code-level analysis technology to support OpenTelemetry data, service mesh, and serverless computing, enabling end-to-end transaction capturing and enhanced observability in cloud-native environments. This innovation facilitates collaboration across development, operations, and application teams while leveraging Dynatrace Davis, a unique AI engine, for automated root-cause analysis and issue remediation. As enterprises undergo digital transformation, the increasing complexity of technologies such as microservices, Kubernetes, and Functions as a Service necessitates comprehensive observability solutions to maintain optimal user experiences and accelerate time to market. PurePath 4 integrates seamlessly with various technologies, providing zero-configuration monitoring, automatic topology analysis, and minimal overhead. Dynatrace offers an open platform with AI-driven automation at its core, supporting open standards and enabling granular access management, making it suitable for large-scale enterprises. The platform's continuous enhancements, such as automatic anomaly detection and in-depth analytics, empower teams to quickly identify and resolve application issues, ultimately improving business outcomes.

As companies pursue digital transformation, cloud services like AWS Lambda are essential for modernizing application architectures, and Dynatrace enhances this process by providing tools for comprehensive observability. The complexity of applications using numerous microservices necessitates end-to-end observability for optimal performance and root-cause analysis. Dynatrace offers Lambda Layers to facilitate distributed tracing and capture metrics and logs from Amazon CloudWatch, providing a unified view with AI-powered analysis. The new AWS Lambda Telemetry API simplifies the observability pipeline by integrating logs, traces, and metrics into a single interface, reducing operational complexity and total cost of ownership. This enhancement allows for improved insights into AWS Lambda's execution lifecycle, better monitoring policy enforcement, lower cloud monitoring costs, and seamless integration for users. The API extension, developed with input from Dynatrace, enables more efficient telemetry signal capture and supports large-scale monitoring while enhancing the speed and accuracy of alerting on metric anomalies.

OpenTelemetry, while powerful, can be complex to implement, but a basic setup involves three core components: a trace generator application, an OpenTelemetry collector, and a storage backend such as Dynatrace. This tutorial guides users through establishing a simplified OpenTelemetry architecture using Docker, where a new network, tracegen-demo-net, is created for ease of container communication. The process involves running a collector container named otelcol and a trace generator container, which emits trace data to the collector, configured to pass the data to Dynatrace. Users can observe the generated traces in Dynatrace's Distributed Traces interface. Although manual setup is demonstrated, deploying the OneAgent on hosts can simplify the process by reducing the need for application instrumentation and an OpenTelemetry collector.

Digital experience monitoring (DEM) enables organizations to enhance user experiences by providing insights into application performance and user behavior across digital channels such as web, mobile, and IoT. By utilizing tools like synthetic transaction monitoring, real-user monitoring, and endpoint monitoring, DEM offers a comprehensive view of user interactions and application functionality, helping businesses optimize customer journeys and improve key performance indicators (KPIs) like conversion rates and user retention. It extends traditional metrics, logs, and traces observability by incorporating real-time user experience data, allowing companies to better prioritize issues and align IT efforts with business outcomes. Business observability, when integrated with DEM, connects technology KPIs with business metrics, facilitating informed decision-making and trend analysis to enhance revenue and fulfillment of business objectives. Intelligent AIOps solutions, such as Dynatrace, leverage AI to automatically identify and prioritize issues, enhancing DEM's effectiveness in preemptively addressing potential problems and refining the user experience.

Organizations are increasingly focused on cloud application modernization as they transition to cloud environments, a process highlighted at AWS re:Invent 2022. With more than 90% of organizations utilizing cloud computing and Gartner predicting that cloud-native platforms will underpin 95% of new digital initiatives by 2025, the shift to the cloud is essential for maintaining competitiveness. However, transitioning IT infrastructure to the cloud involves challenges, as many organizations start with legacy technologies that need modernization to benefit from serverless environments' flexibility, scalability, and cost-effectiveness. The complexity of cloud migration is significant, with 93% of technologists finding modernization challenging and only 35% considering their strategies effective. Effective cloud modernization requires ongoing assessment and optimization rather than a simple "lift and shift" approach, which may lead to performance and security issues. A critical aspect of this process is the development of a cloud migration strategy that ensures system performance and security while leveraging modern observability platforms to maintain visibility over cloud infrastructure. AIOps technology aids in managing the large data volumes generated by cloud-native technology stacks, allowing IT teams to automate processes, prioritize issues, and proactively address potential problems. Additionally, DevOps and site reliability engineering can benefit from automation and observability to streamline continuous delivery pipelines, increasing innovation throughput and enabling faster, more confident development.

Kubernetes automatically monitors the health of pods within a cluster through liveness and readiness probes, but this internal checking doesn't ensure that services exposed via Ingress are meeting external service level agreements (SLAs). Inspired by Christian Heckelmann, a Senior Systems Engineer, the text explores two solutions for automating external SLA checks to address potential issues with Ingress configurations that could lead to SLA violations. The first solution involves using GitLab Pipelines to create Dynatrace Synthetic Tests, which automatically validate SLAs and SLOs by testing service endpoints from various external locations. If any SLA issues are detected, details are reported back to the pipeline using the Dynatrace Problem API. The second solution leverages the Kubernetes Operator Framework through the Synop Operator, which automates the creation of Dynatrace Synthetic Tests for every Ingress deployment, ensuring continuous SLA monitoring regardless of who makes the changes. This approach allows for automated SLA Ingress Monitoring and problem notification routing via tags, enhancing operational efficiency and contributing to the NoOps and ACM communities. Christian's work will be showcased at the Dynatrace Perform Las Vegas 2020 conference, where he will discuss building resiliency into continuous delivery pipelines with AI and automation.

The blog post guides readers through the process of transitioning a monolithic application, TicketMonster, into microservices using the OrdersService as the first candidate. The authors employ the strangler pattern to integrate the microservice with the existing monolith while controlling traffic via feature flags, allowing for incremental testing and deployment on OpenShift. This approach facilitates risk mitigation through canary releases, progressively directing traffic from the monolithic backend-v1 to the newly developed backend-v2. The post emphasizes the use of Dynatrace for monitoring and validation, ensuring seamless integration and performance assessment of the new service. Additionally, it highlights the need to eventually remove feature flags and fully decouple datastores to eliminate technical debt while encouraging readers to apply these practices to their own monolithic applications.

In the evolving landscape of web and mobile applications, traditional metrics like APDEX and W3C navigation timings have become inadequate for assessing user experience due to changes in technology such as AngularJS and the shift to single-page applications. The proposed User/Customer Experience Index provides a modern framework that evaluates user experience through five key components: User Journey, User Action Performance, Errors, User Behavior, and User Environment. By focusing on these ingredients, the User Experience Index measures satisfaction levels by analyzing user actions, performance, and errors, as well as behavioral patterns and environmental factors like network conditions. This approach allows for a nuanced understanding of user satisfaction, categorizing experiences as satisfied, tolerating, or frustrated, which helps improve user experience by addressing technical issues rather than superficial changes.

A Dynatrace customer from a leading US insurance company shared a success story demonstrating how Dynatrace Davis significantly improved their Mean Time to Repair (MTTR). The incident began when Dynatrace detected an abnormal failure rate in an application on Glassfish, due to malformed SQL executed against a DB2 database on an IBM AS400 Mainframe. The anomaly detection algorithm verified the issue's impact on end-user experience and service level agreements before notifying the appropriate team via integrated platforms like Slack and ServiceNow. The customer utilized Dynatrace's PurePath feature to trace the problem to a SQL Syntax Error Exception, allowing rapid identification and communication with the development team. Within minutes, a fix was deployed, confirmed effective, and the incident was resolved before major user traffic began, showcasing an optimized incident response process and the power of Dynatrace's full-stack monitoring.

Government and public-sector organizations are facing significant challenges as they adopt cloud-native technologies, resulting in a data explosion that surpasses human management capabilities and complicates data observation and analysis across distributed technology stacks. According to a Dynatrace report, 75% of CIOs acknowledge that data growth from cloud-native environments is overwhelming, with 92% noting their IT landscapes change rapidly, making it difficult to provide secure, efficient applications for citizens. Organizations rely on multiple monitoring tools, yet achieve end-to-end observability for only 10% of their technology stack, creating operational blind spots and hindering quick issue resolution. The report highlights the need for advanced analytics and automation, such as AISecOps, to manage these complexities, enhance service delivery, and alleviate workforce pressures, with 96% of CIOs recognizing automation's role in addressing skills shortages and reducing burnout. This automation could potentially save staff up to 38% of their time currently spent on IT operations, underscoring the importance of innovative solutions to support digital transformation and improve citizen satisfaction.

Digital immunity has become a strategic priority for organizations seeking to develop secure, resilient, and high-quality software that contributes to business revenue and enhances user experience. As software development increasingly drives business value, methodologies like DevOps and DevSecOps are adopted to integrate development, security, and operations, enabling teams to identify errors early and enhance digital immunity. This approach includes elements such as observability, autonomous testing, chaos engineering, and application security, which together ensure robust software performance and protection against vulnerabilities. The Dynatrace platform exemplifies this by offering end-to-end observability and automation tools that facilitate the seamless integration of security in the DevOps lifecycle, ultimately enabling organizations to innovate rapidly and securely while maintaining high standards of software quality and user satisfaction.

Amplify PowerUP 2022, an online and free partner enablement event organized by Dynatrace, aims to enhance partner services by exploring the theme of modernization through observability. Scheduled across three time zones, the event features curated Mainstage sessions with expert speakers, including Dynatrace's VP Worldwide Partners, Michael Allen, CEO Rick McConnell, and SVP of Product Management, Steve Tack. The event promises insights into building pipelines, acquiring new clients, and driving revenue, alongside showcasing the latest Dynatrace platform innovations such as Grail. Attendees can benefit from a free certification voucher, and those who achieve Professional Certification gain exclusive access to the Partner Pro Club. The agenda includes sessions on the Partner Program, platform evolution, competitive strategies, and a technical Bootcamp, designed to empower partners and enhance their capabilities to deliver Dynatrace solutions effectively.

Mean time to repair (MTTR) is a critical metric for DevOps and ITOps teams, encompassing various aspects such as mean time to respond, resolve, and recovery, which are essential for managing and reducing system outages. These metrics, alongside others like mean time to detect (MTTD), mean time to acknowledge (MTTA), mean time to failure (MTTF), and mean time between failures (MTBF), play a crucial role in measuring and improving the reliability and efficiency of incident management processes. A 2022 Outage Analysis report highlighted the increasing financial consequences of outages, emphasizing the importance of these metrics in minimizing downtime and maintaining service continuity. MTTR and related metrics are integral to the four stages of IT incident management: identification, containment, resolution, and maintenance, and they help organizations anticipate issues, respond promptly, and implement sustainable fixes. Advanced tools like the Dynatrace Software Intelligence platform leverage artificial intelligence and automation to enhance incident management by providing real-time monitoring, root-cause analysis, and automated responses, ultimately improving metrics like MTTR and supporting the broader goals of site reliability engineering (SRE).