April 2021 Summaries

Dynatrace ONE is a customer experience program designed to enhance customer success, achieved by integrating various support functions like Technical Support, Consulting, and Customer Success into a single group with an Agile mindset. To further this initiative, a new Dynatrace Support Center and ticketing system are being introduced to streamline customer interactions by unifying all support resources and communications into a single platform. This transition will involve migrating open support tickets and conversations to the new system, with closed tickets archived for a year. The new system aims to provide more intelligent assistance by leveraging AI to offer context-aware content and reduce resolution times by 50% when engaging through live, in-product assistance. This phased rollout will eventually consolidate all support communications into a single channel within Dynatrace, eliminating the need for separate forms or portals, thereby enhancing collaboration and efficiency in resolving customer queries.

Machine Learning (ML) and Artificial Intelligence (AI) have gained significant traction across various domains, yet they face skepticism due to their often opaque decision-making processes, particularly in complex models like deep neural networks considered as black boxes. Explainable AI (XAI) seeks to provide comprehensible explanations for these models, which is crucial for trust and addressing biases, especially in sensitive areas such as job opportunities and criminal justice. Methods like Global Surrogates and Local Surrogates provide different levels of interpretation by training simpler models on the outputs of complex ones, while SHAP uses Shapley values from game theory to fairly assign feature contributions, providing both local and global interpretations. Despite computational challenges, SHAP is widely adopted due to its solid theoretical foundation. The importance of explainability is highlighted in platforms like Dynatrace, which automatically analyzes metrics and could use XAI methods for identifying key factors in custom Key Performance Indicators (KPIs). This approach allows for a flexible and robust system that can adapt to future enhancements in metric aggregation and analysis.

Since 2020, the demand for digital services has surged, prompting organizations to adopt DevOps practices, particularly continuous integration and continuous delivery (CI/CD), to enhance speed and reliability in bringing new products to market. CI/CD encompasses processes that enable developers to automate development, testing, and deployment, fostering better collaboration and efficiency throughout the software development life cycle. Continuous integration allows developers to frequently merge code into a shared repository, reducing issues like "merge hell" and ensuring up-to-date, testable builds. Continuous delivery focuses on keeping code in a deployable state, making releases predictable and seamless, while continuous deployment automates the release of tested code directly to production. These practices not only streamline development but also provide a competitive edge by accelerating time to market and allowing rapid iteration of features. Organizations utilizing CI/CD benefit from increased efficiency, optimized processes, and the ability to swiftly respond to user feedback, with advanced tools like the Dynatrace Software Intelligence Platform enhancing observability and integration across the development pipeline.

The OneAgent release version 1.215 introduces several updates and changes, affecting Dynatrace SaaS customers, particularly with the switch in the domain for internal communication from *.live.ruxit.com to *.live.dynatrace.com starting June 30, 2021. This release provides support for OpenTelemetry in Java and Go, enhancements for monitoring on z/OS, and resolves numerous issues across different components, including .NET, Java, PHP, and Infrastructure Monitoring. It also outlines future deprecations of support for certain operating systems and technologies like OpenTelemetry for Go and Node.js. Additionally, the release includes cumulative updates that address problems such as service crashes, incorrect metrics, and improved module stability, alongside specific fixes for mobile session replay and JavaScript performance improvements.

In the complex landscape of software release management, Dynatrace offers a robust solution that aids organizations in automating the collection and analysis of release data, crucially answering the question, "To release or not to release?" As organizations strive for faster delivery cycles within agile and DevOps cultures, Dynatrace's Release Analysis integrates with tools like Jira, GitHub, and Kubernetes to provide comprehensive insights into deployment stages, version behavior, and potential release risks. It facilitates decision-making by highlighting issues, tracking changes, and correlating deployment events, all while supporting integration with various technologies to streamline metadata capture. The tool also plans to enhance its capabilities by incorporating security risk visibility, technology version searches, and more analytical features involving user behavior and dependency graphs, thereby promoting informed and automated release strategies.

In the rapidly evolving digital landscape where every organization is transforming into a software company, DevOps emerges as a crucial framework for delivering high-quality digital services swiftly and reliably. Gene Kim, a renowned DevOps expert, underscores the significance of implementing lean principles in technology value streams to achieve efficient deployment processes and maintain world-class security and stability. Despite its benefits, organizations face challenges in adopting DevOps, such as a lack of understanding of the necessary infrastructure, data accessibility issues, and resistance to new operational methods. Kim, along with research collaborators, highlights that organizations embracing DevOps outperform others by deploying updates more frequently and resolving issues swiftly, thus enhancing business value. High-performing organizations, integrating security into continuous delivery, have demonstrated superior profitability, market share, and productivity compared to their counterparts.

Organizations embarking on the journey to optimize Google's Core Web Vitals for SEO face challenges related to scale, focus, and communication. Dynatrace's Business Insights team addresses these obstacles by offering analytical views and an approach to help site owners turn Core Web Vitals data into actionable insights. The team has developed the Insights Analytics Engine to provide precise metrics and specialized views for monitoring Core Web Vitals performance, enabling organizations to focus on key pages that impact traffic and business outcomes. These views facilitate the identification of optimization priorities and provide competitive insights by comparing an organization's performance with that of competitors. To support this, Google offers guidelines for improving metrics such as Largest Contentful Paint, First Input Delay, and Cumulative Layout Shift, though more specific recommendations are often needed for effective implementation. The Insights team also aids in tracking progress over time through automated dashboards and expanded reporting capabilities, enhancing the ability to communicate results to stakeholders and ensure continuous improvement in user experience and search rankings.

Monitoring and observability are crucial for developers, architects, and Site Reliability Engineers across various languages and runtimes. The blog highlights the experiences of Stephan Dannewitz, a DevOps & Site Reliability Engineer at avodaq AG, who implemented monitoring and observability for the Python-based video chat platform CARY using Dynatrace’s Software Intelligence platform. The article details how Dannewitz leveraged Dynatrace’s OneAgent SDK for Python auto-instrumentation, which enables automated distributed tracing without modifying Python code, thus benefiting developers in troubleshooting and extracting business metrics. The process involves installing the OneAgent on Kubernetes clusters, configuring auto-instrumentation for Python frameworks like Django, analyzing data through automated metrics, and setting Service Level Objectives (SLOs) to ensure operational efficiency. The blog encourages developers to explore these tools and practices to enhance the observability of their Python applications, offering insights into tagging and metadata management to improve deployment and monitoring strategies.

Dynatrace Managed version 1.216 introduces several enhancements and fixes to improve performance, security, and usability. Key features include the ability to store synthetic browser monitor screenshots on the cluster for new installations, the initiation of self-service update packages management to optimize disk space, and enhanced performance of the Licensing and Environments overview page in the Cluster Management Console. User behavior information is now part of deployment health monitoring, and license-related notifications are streamlined for cluster administrators. In terms of security, sign-in events are no longer reported to Cluster Mission Control, and several resolved issues improve the handling of UI components, such as the corrected display of session replay permissions and the improved permission checks for accessing host and process group settings. The release also addresses various bugs, including fixed deployment status displays, corrected sorting of tables, and the handling of large metric requests to prevent errors. Additionally, significant changes in operating systems support are outlined, with several Linux distributions set to lose support in upcoming years.

The OneAgent version 1.213 release, initiated on April 8, 2021, introduces several significant updates, including the removal of older installation parameters in favor of a new command-line format and enhanced customization features for log directories across various operating systems. Notable advancements include the ability to control auto-injection and auto-update via the OneAgent command line interface, as well as added support for Java-related technologies like Jedis Redis 3. The release notes highlight technology support changes, with this version being the final one to support specific OpenTelemetry versions for Go and Node.js, and future plans to phase out support for certain operating systems like Amazon Linux and Debian by 2025 and 2026. The update resolves multiple issues across OneAgent modules, including those related to .NET, Java, and Mainframe, improving stability and performance. Additionally, the release addresses vulnerabilities and bugs in different components, enhancing system reliability and functionality across environments such as iOS and JavaScript.

Automatic and intelligent mobile app monitoring is essential for organizations to deliver seamless and GDPR-compliant experiences, as consumer expectations for flawless mobile interactions increase. The complexity of using multiple monitoring tools often hampers quick identification of performance issues, highlighting the need for unified monitoring strategies. GDPR, which protects EU citizens' data privacy, requires compliance from any organization dealing with EU data, complicating monitoring further due to potential privacy violations. Companies like Dynatrace offer solutions that anonymize sensitive data and provide session replay capabilities, enhancing data privacy compliance by default. These measures allow organizations to manage personal data responsibly while optimizing user experience and business outcomes, positioning them effectively for a mobile-first future.

Azure Functions is a serverless compute service from Microsoft that allows users to run code in response to specific events or conditions, much like other FaaS platforms such as AWS Lambda and Google Cloud Functions. It automates the management of computing resources, enabling teams to focus on developing event-driven applications without worrying about infrastructure. However, while Azure Functions is well-suited for routine tasks such as processing orders and IoT data, it may not be ideal for computationally intensive or infrequent, time-sensitive tasks due to potential costs and cold start delays. Observability challenges also arise in the serverless model, as monitoring Azure Functions is limited to Azure's ecosystem, making it difficult to maintain end-to-end visibility across complex multicloud environments. To address these challenges, the Dynatrace Software Intelligence Platform offers enhanced observability, leveraging automation and AI to provide deep visibility into Azure Functions and their interactions, thereby optimizing performance and improving end-user experiences.

AWS Lambda is a serverless compute service launched in 2014 that allows organizations to run code in response to specific events without managing the underlying infrastructure, offering significant cost savings and efficiency improvements. It integrates seamlessly with the broader Amazon Web Services (AWS) ecosystem, enabling applications to scale rapidly and connect to various AWS offerings such as data processing, machine learning, and real-time stream processing. Despite its benefits, AWS Lambda presents observability challenges due to its complex microservices architecture, which can make tracking and managing functions difficult. To mitigate these issues, solutions like Dynatrace provide enhanced observability through automation and AI, offering detailed insights into function performance, user experience, and business outcomes. This enables development and DevOps teams to innovate more quickly and effectively, maintaining a balance between leveraging serverless capabilities and ensuring comprehensive monitoring and analysis.