January 2022 Summaries

In the agile DevOps methodology, shift left and shift right are pivotal testing concepts that enhance software development and delivery. Shift left involves incorporating testing and quality assurance early in the development cycle, allowing teams to detect and fix bugs sooner, which reduces costs and accelerates time to market. It emphasizes collaboration, early bug detection, and improved customer satisfaction by ensuring software meets user requirements from the outset. Conversely, shift right extends testing into the production environment, focusing on real-world conditions to ensure software reliability and performance. It provides insights through realistic user testing, continuous feedback, and enhanced monitoring, helping teams address issues that may not appear in earlier stages. Both approaches are crucial in microservices architecture, allowing for comprehensive testing and feedback loops that improve software quality. Organizations benefit from using both strategies to achieve continuous integration and delivery, supported by automated full-stack observability and monitoring. This combination not only enhances agility and efficiency but also ensures robust application security and improved user experiences.

At Dynatrace Perform 2022, the focus was on the transformative impact of modern observability on serverless architecture, emphasizing its role in enhancing innovation and efficiency while alleviating complexities associated with digital transformation. As organizations increasingly adopt serverless architecture to expedite modernization without the burden of managing underlying infrastructure, they face challenges in managing performance and security due to the dynamic nature of microservices across multicloud platforms. Dynatrace showcased its AI-powered observability platform, which provides real-time, causation-based insights, allowing DevOps and engineering teams to pinpoint and resolve issues swiftly. This approach reduces the time spent on manual troubleshooting, enabling teams to focus on strategic initiatives. The event also highlighted the integration of open-source observability tools like OpenTelemetry, addressing challenges related to data silos and offering comprehensive visibility across distributed environments. The conference underscored the importance of an end-to-end view of multicloud environments, demonstrating how AI can enhance application performance and facilitate strategic cloud migration.

Dynatrace's SaaS release version 1.235, announced in January 2022, brings several updates and enhancements, including the end-of-support for TLS 1.0 and 1.1 for RUM data starting April 2022 and the discontinuation of Session Replay masking v1 from version 1.240. It introduces new features like automatic connection of log data to traces, a stacked graph visualization in the Data Explorer, and the ability to add images to dashboards. Application Security has been improved by automating OneAgent reporting of software components, eliminating the need for manual enabling for new customers. The release also addresses various resolved issues, including security vulnerabilities, bug fixes, and enhancements in cloud automation, cluster management, and application security. Notably, it mandates the use of HTTPS endpoints for setting up user session exports and OAuth2 authentication access token URLs, while also correcting issues related to request naming, Kubernetes event timing, and metric data display.

DevOps orchestration is a crucial strategy for developers, site reliability engineers, and DevOps teams to maintain code quality while developing at the speed required by business demands. It involves integrating tools and automating the software delivery life cycle (SDLC) to balance speed and quality effectively. DevOps orchestration, especially when combined with cloud automation, addresses the challenges of accelerating development cycles and fragmented tool environments, which can hinder innovation and affect code quality. By breaking down silos and automating tasks, DevOps orchestration allows for smoother collaboration and higher-quality software development. Andreas Grabner and Lauren Horwitz from Dynatrace highlight in a video how this approach ensures that there is no compromise between speed and quality if service level objectives (SLOs) are used as guardrails. The concept and benefits of DevOps orchestration are further explored in the Perform 2022 conference, which offers insights into various DevOps themes, including DevOps and DevSecOps advancements.

AIOps capabilities have become crucial for IT teams managing the complex nature of multicloud and hybrid cloud environments, with causation-based AIOps representing a significant advancement over traditional correlation-based methods. As organizations face increasing pressure to deliver high-quality software rapidly while ensuring seamless digital experiences, the move to cloud architectures has increased data and systems to manage, often leading to reliance on manual processes and static dashboards for issue resolution. AIOps offers a solution by providing continuous, intelligent automation that reduces the complexity of IT operations, allowing IT teams to automatically identify and resolve issues without needing manual intervention. In a preview for Dynatrace Perform 2022, Joel Alcon and Lauren Horwitz discuss how causation-based AI fundamentally changes the landscape of cloud observability, emphasizing Dynatrace's innovative approach to AIOps in enhancing IT efficiency and adaptability.

Despite transitioning from an in-person event in Las Vegas to a virtual format, Perform 2022 will maintain its high-quality training, leveraging Dynatrace University's year-round virtual offerings. Attendees can expect live sessions with Dynatrace experts, interactive hands-on training, and a wide array of content covering various technology areas within Dynatrace. The virtual format eliminates travel costs and offers convenience without compromising the quality of learning. Perform 2022 features 23 unique sessions catering to all experience levels, with recommended sessions for newcomers focusing on getting started with Dynatrace and Digital Experience Management analytics. Additionally, there is a certification promotion allowing attendees to earn free certification attempts based on their participation, with specific guidelines for obtaining promotional codes and deadlines for redemption.

At Dynatrace Perform 2022, the importance of application security and vulnerability management in cloud-based services was highlighted, particularly focusing on the challenges posed by dynamic multicloud environments and fast-paced software innovation. The conference emphasized the need for modern vulnerability management strategies that incorporate AI, automation, and contextual intelligence to effectively address runtime vulnerabilities, as demonstrated by the widespread impact of the Log4Shell vulnerability. Traditional methods, often manual and slower, may not adequately protect complex cloud-native environments, which require real-time observability and a DevSecOps approach that includes both "shift left" and "shift right" strategies for identifying and prioritizing vulnerabilities. The event underscored the necessity of a comprehensive, automatic, and intelligent approach to vulnerability management to maintain security without hindering innovation.

The Department of Defense (DoD) is advancing its cloud capabilities through the Joint Warfighter Cloud Capability (JWCC) contract, replacing the canceled Joint Enterprise Defense Infrastructure (JEDI) initiative due to evolving requirements and industry advances. This new multi-vendor, multicloud approach aims to enhance military missions by leveraging resources from major providers like Amazon Web Services, Google Cloud, Microsoft, and Oracle, who must meet stringent cybersecurity standards. The shift to a multicloud environment is seen as a strategic move to mitigate the risk of cyberattacks and ensure the resilience of military operations. The DoD recognizes the critical role of the cloud in executing military strategies, emphasizing the need for real-time data to support decision-making. Companies like Dynatrace offer solutions for cloud observability, providing continuous monitoring and intelligence to enhance security and operational efficiency across complex multicloud environments.

Real User Monitoring (RUM) and Synthetic Monitoring are integral components of Dynatrace Digital Experience Monitoring, with RUM focusing on user experience and Synthetic Monitoring providing additional benefits such as availability and performance baseline monitoring. The latest Dynatrace release simplifies initiating Synthetic Monitoring by allowing users to activate 24/7 monitoring from three global locations with a single click via an application overview page. This process involves automatically creating synthetic monitors for the top three user entry actions, which are monitored every 15 minutes from these locations. Users have the flexibility to customize these monitors by adding locations or adjusting frequency and can opt for a manual setup to tailor monitoring to specific entry actions. The availability card for setting up these monitors becomes visible only if certain conditions are met, such as having change monitoring permissions and no existing synthetic monitors. Future updates will enhance this feature by automatically selecting synthetic locations based on real user geographic data and determining whether applications are internally accessible to choose suitable monitoring locations.

Dynatrace addresses the challenges faced by organizations adopting agile software development and continuous delivery by providing a cohesive platform for Site Reliability Engineering (SRE) teams. The platform facilitates cross-team collaboration by enabling these teams to define and manage Service-Level Objectives (SLOs) and Service-Level Indicators (SLIs) to ensure reliable and scalable software systems. Dynatrace offers an all-in-one SLO API to help scale the definition of SLOs, utilizing a unified observability platform where stakeholders can work together to meet service levels automatically. The process involves identifying success metrics, configuring SLIs, and using calculated metrics for performance measures. Dynatrace emphasizes the importance of an entity selector in SLO creation for problem analysis and highlights the significance of setting SLO targets and evaluation timeframes. The platform also provides tools like MONACO for automating SLO deployment, enabling organizations to manage error budgets and predict potential issues proactively.

Dynatrace Managed version 1.234 introduces several key updates, enhancements, and resolved issues specific to its platform. The release discontinues support for new installations on Centos/RHEL/Oracle Linux 7.6 and introduces Kubernetes events integration for enhanced observability. A new dtCookie format is automatically enabled, with a maximum user actions per minute limit imposed for environments using the old format. Performance improvements include JRE upgrades for cluster node components, and OpenID Connect (SSO) signature validation is now configurable, with user group assignments refreshing every 30 minutes. The release also addresses numerous resolved issues spanning application security, cluster management, and data inconsistencies, improving various functionalities like permission handling, bug fixes, and security vulnerabilities. Cumulative updates in builds 1.234.111 through 1.234.124 further rectify issues such as caching problems, metric data display errors, and dimension suggestions in Data Explorer.

The Dynatrace SaaS release notes for version 1.234, published on January 14, 2022, include several important updates and enhancements. Key announcements include the end-of-support for TLS 1.0 and 1.1 for RUM data starting April 2022 and the end-of-life for Session Replay masking v1 with version 1.240. The release introduces the ability to export log data in JSON or CSV formats from the log viewer, with a limit of 1,000 records per export. Users can now specify resolution for graphs and heatmaps on dashboards. The update also features twelve resolved issues, including a vulnerability fix by updating Log4j in the Elasticsearch client to version 2.17.1 and various bug fixes related to infrastructure alerts, PurePath navigation, and entitySelector queries. Improved functionalities also address display issues and permission handling in the Application Security process group overview. Additionally, the "Data explorer" page has been stabilized to prevent crashes when using the "Single value" visualization with "Last value" enabled.

Organizations worldwide are rapidly adopting Kubernetes due to its performance benefits, including cost-effective dense scheduling of containers and application isolation. However, configuring Kubernetes clusters to balance availability, performance, and affordability presents significant challenges. A webinar featuring Henrik Rexed from Dynatrace discussed how combining Dynatrace observability and Akamas AI-powered optimization can address these issues. The Akamas approach uses AI to autonomously optimize Kubernetes microservices, illustrated through a case study on Google Online Boutique, where optimization led to improved cost efficiency by 77% and enhanced service throughput by 19%. This approach highlights the value of AI-driven autonomous optimization in achieving optimal performance and stability while minimizing costs, extending beyond Kubernetes to include other IT stack components.

DevOps practices are crucial for enhancing business speed and innovation by integrating developers and operations teams to achieve more agile IT solutions, yet the complexity and pace of software development can hinder these benefits. At Dynatrace Perform 2022, the focus will be on how automation can mitigate these challenges by streamlining DevOps workflows, reducing toolchain silos, and improving efficiency and code quality. The conference will showcase advancements in using AI and automation to enhance software development processes, enabling DevOps and site reliability engineering (SRE) teams to work more cohesively and address the speed-quality tradeoff effectively. Automation aids in overcoming the fragmentation caused by manual DevOps practices, addressing security vulnerabilities, and ensuring robust software delivery. By leveraging AI and automated processes, organizations can accelerate development without compromising quality, as the DevOps track will demonstrate through new integrations and strategies aimed at boosting productivity and innovation while maintaining high standards of code quality and security.

Real user monitoring (RUM) is a performance monitoring process that captures detailed data on user interactions with applications, providing insights into actual user experiences and identifying performance issues before they affect a large number of users. By injecting code into applications, RUM collects metrics on load actions and user sessions, offering a comprehensive view of user journeys and helping teams identify areas for improvement. While RUM provides valuable insights, it also generates vast amounts of data, requiring effective query and visualization tools to quickly identify key information. However, RUM has limitations, such as its dependency on active user engagement and difficulty in establishing performance baselines, which can be complemented by synthetic monitoring. The integration of RUM with an all-in-one observability platform enhances visibility into user experiences, allowing organizations to trace performance issues back to infrastructure or application problems. An effective RUM solution should provide full session capture, session replay capabilities, and integration with business context data to understand the business impact of application performance, enabling teams to deliver better digital experiences.

In response to the widespread Log4j vulnerability, known as Log4Shell, Dynatrace swiftly deployed its Application Security solutions to detect, prioritize, and remediate the threat across its systems. Log4Shell, a critical flaw in the Apache Log4j 2 Java library, allows remote code execution, posing substantial risk due to its high CVSS severity score and ease of exploitation. Dynatrace's platform, equipped with real-time monitoring and AI-driven prioritization, enabled the security team to quickly identify affected systems and implement remediation strategies, ensuring rapid response and coordination among security and engineering teams. This proactive approach allowed Dynatrace to efficiently manage the vulnerability, mitigate risks, and maintain clear communication with customers, while also preparing for future security challenges through continuous updates and strategy adjustments.

Business transactions rely heavily on the performance of applications, especially in microservices architectures where asynchronous communication via messaging systems is crucial. Dynatrace's OneAgent version 1.231 introduces enhanced analytics capabilities that provide DevOps teams with comprehensive visibility into messaging systems, enabling easier troubleshooting of anomalies. This improved observability allows for monitoring of queue entities, identifying unbalanced message processing, and preventing issues like queue overflows. With technology-specific views for platforms such as RabbitMQ and ActiveMQ, DevOps teams gain insights into connected producer and consumer services, helping them identify root causes of messaging anomalies. The system's metrics, including response time and failure rate, are accessible through Dynatrace's interface, where users can set alerts for specific metrics. These enhancements offer significant benefits for maintaining system reliability and efficiency in both traditional and modern IT environments.

The release notes for Dynatrace OneAgent version 1.231, published on January 10, 2022, introduce new analytics capabilities focused on messaging system-related anomalies to enhance observability in microservices-based applications. This release emphasizes the integration of log monitoring with Dynatrace's PurePath technology for improved AI-driven cloud analytics, enabling faster analysis and optimization of cloud-native and hybrid applications. The update also highlights an upgrade to the Extension Framework from Python 3.6 to Python 3.8, which may require redeployment of certain extensions. Additionally, the release includes support changes for various operating systems, such as the addition of support for Windows Server 2022 and planned discontinuation of support for certain Linux and Windows versions. Several enhancements and resolutions are addressed, including improved handling of the oneagentnettracer on Linux and specific fixes across Java, .NET, PHP, AI causation engine, and infrastructure monitoring components. The cumulative updates also resolve specific issues like memory leaks, logging module crashes, and problems with automatic updates.

Raphael Pionke, a DevOps Engineer at T-Systems MMS, outlines a modernized approach to application performance and resiliency testing, focusing on white-box testing using JMeter and Dynatrace. This methodology aims to address complex cloud architectures by providing visibility from the end-user to code-level, thereby facilitating proactive problem resolution and scalability optimization. The white-box testing approach integrates JMeter with Dynatrace to enhance automation and transparency, using tools like the W3C Trace Context for detailed traceability and Dynatrace's AI for root cause analysis. Automating the test infrastructure setup with Terraform and Ansible, along with using a self-developed test management tool, ensures efficient test result tracking and analysis. Future enhancements include collaboration with Dynatrace on the open-source project Keptn, to improve automation and integration across various DevOps tools and processes.