September 2022 Summaries

Digital customers are crucial to business success, and maintaining a seamless digital experience is vital to retain them. Business analytics tools are essential in improving this experience by providing insights into how IT impacts business outcomes, identifying areas for optimization, and enhancing agility by allowing IT teams to focus on what matters most. These tools work by linking IT metrics with business outcomes, such as funnel progression, conversion rates, and net promoter scores, and breaking down silos between IT and business teams to improve communication and collaboration. Business analytics tools are applicable in various use cases, including evaluating business impact, optimizing conversion rates, validating new releases, and monitoring business processes. These tools enhance business observability by integrating IT and business metrics, ensuring both teams share responsibility for business process success. Dynatrace's Software Intelligence Platform exemplifies this approach by offering a comprehensive solution that leverages AI and automation to align IT performance with business goals, ultimately driving better outcomes.

Kubernetes is widely used for managing large-scale distributed applications, but it presents significant observability challenges, making incident resolution complex. Dynatrace's Davis® AI Causation Engine, introduced in version 1.249, enhances Kubernetes performance monitoring by providing automated root cause analysis and targeting orchestration issues in real-time, thus reducing business impact from problems like pod evictions and workload misconfigurations. These improvements allow operations teams to make quicker, more informed decisions, reducing mean time to repair (MTTR) and minimizing disruptions. Additionally, Dynatrace plans to introduce large-scale anomaly detection to streamline the identification of common Kubernetes issues without requiring pre-configured alerts, further simplifying infrastructure management. Organizations leveraging Dynatrace can take advantage of these tools to maintain secure, efficient, and high-performing Kubernetes clusters, as demonstrated by data from a global survey of Kubernetes users.

Organizations are increasingly adopting multicloud architectures to enhance software delivery speed and security, but they face challenges due to the complexity and data explosion associated with these environments. IT teams are often overwhelmed by the volume of resources and the continuous data stream, leading to inefficiencies in cloud management. Observability blind spots, particularly in multicloud settings, pose significant risks to digital transformation efforts, with log management and analytics being particularly problematic. To address these challenges, automation and AIOps are becoming essential, enabling IT teams to focus on more innovative and valuable tasks. The adoption of data lakehouse architecture is highlighted as a solution to manage data complexity and explosion, offering the flexibility of a data lake combined with the querying capabilities of a data warehouse. This approach is believed to help organizations harness large volumes of data efficiently, improving business outcomes and addressing staffing shortages in IT by reducing reliance on manual tasks. The integration of AI and automation in observability and security data management is emphasized as crucial for maximizing data value and fostering faster, more secure innovation.

The blog post discusses the significance of Kubernetes NetworkPolicies in enhancing security by restricting network flow between pods in a Kubernetes cluster. It explains that, by default, all pods can communicate freely, which poses security risks if an attacker gains control of a pod. NetworkPolicies mitigate this risk by allowing administrators to specify connections on the IP address or port level, requiring a Container Network Interface (CNI) that supports them, such as Calico or Cilium. The post outlines the importance of creating a "deny-all" NetworkPolicy to block all traffic initially and then selectively allowing only necessary connections. Best practices also include blocking access to the cloud provider's Metadata API to prevent unauthorized access to sensitive data. The post emphasizes the significance of meaningful naming conventions for NetworkPolicies and provides a detailed example of creating and applying NetworkPolicies to control pod communications. It concludes with a reminder of the steps to effectively implement NetworkPolicies, ensuring the use of a compatible CNI, establishing a deny-all policy, defining allowed connections, and testing the policies for effectiveness.

Dynatrace's latest enhancement to mobile app monitoring, Session Replay, allows developers to experience a movie-like replay of user sessions ending in a crash, aiding in identifying root causes on both iOS and Android platforms. This tool is crucial as mobile computing has become dominant across various industries, and user experience greatly impacts business success, with app crashes being a major reason for app uninstalls. By using Session Replay, developers can reproduce and address crashes more efficiently, thereby improving user satisfaction and engagement. The feature also facilitates better communication across teams by allowing application owners and non-technical users to comprehend crash impacts. To implement Session Replay, apps must be instrumented, and Dynatrace provides tools for auto-instrumentation and custom event capture, ensuring that data privacy is maintained through configurable masking options. This enhancement not only improves troubleshooting and optimization but also enhances business outcomes by providing a detailed recreation of user sessions, helping teams trace errors back to their origins quickly.

Site reliability engineering (SRE) is an essential component of digital transformation that aims to integrate reliability and resiliency throughout the software development lifecycle, yet many organizations find achieving SRE maturity challenging. The 2022 State of SRE Report, based on a survey of 450 SREs, highlights that while SRE is evolving, its maturity is hindered by a reactive approach focused on tasks like reducing mean time to repair and maintaining automation code. A cultural shift is necessary for organizations to advance SRE maturity, involving breaking down silos and prioritizing reliability across the company, not just within the SRE team. Additionally, service-level objectives (SLOs) are crucial for measuring application performance, but the proliferation of data sources and tools complicates data management and observability. Reducing SRE toil requires embracing a cultural transformation, selecting platforms with automation and AI capabilities, and fostering cross-team collaboration, with tools like Dynatrace offering AI-driven solutions to streamline SRE processes and enhance observability.

Dynatrace Managed version 1.250 introduces several new features and enhancements, including cross-environment tracing to enhance end-to-end monitoring capabilities and updates to the Dynatrace Managed section of the End-of-support news page, which details support policies. The release also includes improvements in internet proxy configuration for air-gapped deployments, sign-in page mode configuration, and extended automation through the Cluster Token Management scope. Adjustments have been made to space values for cluster node storage to ensure deployment health, and navigation improvements have been implemented. The release adds support for Rocky Linux 9.0 while dropping support for Ubuntu 21.04 for new installations, with a plan to discontinue support for several other operating systems by 2026. Various resolved issues include enhancements in application security, cloud automation, and cluster management, addressing problems like usage reporting errors, alerting configuration issues, and memory leaks. Additionally, the release addresses vulnerabilities by updating the Apache Commons Text library, and numerous cumulative updates are included to consolidate previous fixes and improvements.

The DevSecOps maturity model serves as a guideline for organizations to integrate security into their DevOps processes, thereby enhancing their ability to counter cyber threats and improve software quality. By incorporating security considerations at every stage of the software development lifecycle, DevSecOps enables teams to maintain speed and agility without compromising security. The OWASP DevSecOps maturity model outlines a multistage approach to achieving maturity, but it may overlook certain aspects such as monitoring and release validation. Organizations often face challenges such as siloed operations, cultural resistance, disparate tools, and fragmented data when implementing DevSecOps practices. However, a robust DevSecOps maturity model can offer numerous benefits, including faster innovation, better software quality, reduced issue identification time, and improved resource management. Tools like Dynatrace Application Security can aid in achieving DevSecOps maturity by providing continuous observability and automated vulnerability detection, allowing IT teams to focus on strategic initiatives and reducing security risks.

OneAgent version 1.249 introduces several new features and enhancements, including the detection of Milan and Jakarta AWS regions as EC2 cloud types and the auto-instrumentation of SwiftUI controls for iOS 8.249. It adds support for OpenTelemetry versions 1.8-1.11.0 and the Golang toolchain 1.19, along with Amazon Linux 2023 and Rocky Linux 9. This release is notable for being the last to support certain technologies like JBoss Enterprise Application Platform 6 for Java, which has been de-supported since 2019. Future support changes include the discontinuation of various operating systems, such as Amazon Linux 2023 and Windows Server 2012 R2, by mid-2026. The release also resolves a range of issues across components such as .NET, PHP, and JavaScript, enhancing stability and security, including improved input validation for Real User Monitoring (RUM). Additionally, cumulative updates address several vulnerabilities and technical glitches, contributing to a more robust and secure platform.

Dynatrace provides a comprehensive solution for mobile application monitoring, offering app owners detailed insights into user interactions, app performance, and user experience across different regions and device versions. By integrating native mobile app monitoring with end-to-end observability, Dynatrace allows app owners to track and analyze key performance indicators (KPIs) related to business goals and application health. The platform captures detailed telemetry data, automatically highlights issues, and isolates root causes, enabling teams to optimize user journeys and enhance app performance. For example, a major Peruvian bank used Dynatrace to boost mobile app transactions by 30% and overall usage by 250% in six months. Dynatrace's capabilities, such as Apdex ratings and user session query language (USQL), provide granular insights into user behavior and facilitate the creation of custom dashboards for tracking user journeys and identifying conversion barriers. Additionally, Dynatrace offers privacy controls to comply with data protection laws, and its Davis AI helps differentiate between satisfied and frustrated user sessions, allowing for targeted improvements.

Dynatrace is enhancing its observability tools, emphasizing the integration of manual data analysis with automated AIOps for comprehensive insights. As digital systems grow, traditional dashboards and AIOps are seen as complementary rather than opposing methods, necessitating a balance for effective data analytics. Dynatrace's Data Explorer, introduced to facilitate data visualization and analysis, has been upgraded with features like dynamic filtering, auto-extended filtering, and customizable visualizations to ease use and improve functionality. These enhancements aim to reduce manual configuration and maintenance, offering a smarter, more interactive platform for monitoring complex cloud ecosystems. The improvements include the ability to customize number formats and colors, along with new chart types, which collectively simplify the visualization of metrics and enhance user experience. With upcoming updates, Dynatrace plans to phase out older custom chart functionalities, urging users to transition to the new Data Explorer for a more streamlined analytics process that aligns with modern enterprise needs.

The Dynatrace SaaS version 1.251 release, published on September 9, 2022, introduced several enhancements and features aimed at improving Kubernetes infrastructure monitoring, service detection settings, and distributed traces management. Key updates include the availability of Kubernetes services overview and details pages within Dynatrace, the introduction of a new column in Kubernetes namespace and cluster list pages, and mandatory permissions for listing services. Additionally, the release reworked Kubernetes dashboard presets and improved service detection settings, while the Data Explorer now offers templates to streamline query creation. The update also addressed 14 resolved issues across various components, such as ActiveGate, Cluster, and the user interface, enhancing functionality and user experience.

Dynatrace has announced support for Amazon VPC Flow Logs through Amazon Kinesis Data Firehose, allowing real-time streaming of network traffic data to its platform, which enhances the analysis and management of cloud environments. This integration eliminates the need for custom solutions and reduces costs by enabling direct delivery of flow logs to Dynatrace, bypassing other data sources like S3 and CloudWatch, and facilitating enriched log metadata for better analysis. Dynatrace, recognized as a leader in application performance monitoring, leverages its AI engine, Davis, to automatically detect performance anomalies, providing users with enhanced problem detection and analysis capabilities by correlating VPC Flow Logs data. This development underscores the commitment of AWS and Dynatrace to deliver quality solutions, enabling IT professionals to better monitor, visualize, and automate cloud operations across hybrid and multicloud environments.

Retailers face significant challenges during Black Friday due to overwhelming and unpredictable peak loads, which can threaten website performance and customer experience. Observability platforms have been adopted to address these issues, allowing companies to identify root-cause problems and ensure seamless customer interactions. The rise in ecommerce has highlighted the importance of maintaining an uninterrupted online presence, as poor performance can lead to substantial financial losses and customer attrition. A case study involving a North American electronics retailer illustrates the limitations of traditional monitoring tools, which often fail to capture the full scope of user experience issues, leading to missed opportunities for improvement. The adoption of modern observability technologies, such as Dynatrace, enables organizations to automate responses to demand increases and unexpected changes, enhancing operational efficiency and allowing teams to focus on innovation. Additionally, the growing concerns around cyberthreats, exemplified by vulnerabilities like Log4Shell, underscore the need for robust security measures as part of the observability strategy. Best practices for navigating peak loads include establishing real-time monitoring, conducting synthetic monitoring, and proactively testing systems to identify potential weaknesses before high-traffic events.

Dynatrace University (DTU) serves as a pivotal resource for organizations aiming to navigate the complexities of dynamic cloud environments by offering end-to-end observability, integrated AIOps, and application security training. As organizations face the challenges of evolving technology stacks and workforce turnover, DTU provides an accessible platform for on-demand and live guided learning, enabling users to develop necessary skills to effectively utilize the Dynatrace Platform. The rise in virtual learning has bolstered DTU's reach, with over 50,000 global learners in 2021, making it comparable in size to a top 20 traditional university. DTU is particularly valued by large organizations, such as a major insurance company and a global ERP organization, for its performance planning and customizable learning paths, which facilitate employee education and service plan integration. Additionally, a financial institution leverages DTU's blended learning approach to manage its vast user base across multiple regions, finding that microlearning enhances participant engagement in expert-led training. The platform's alignment with all Dynatrace modules ensures that customers can maximize their strategic investments, offering a comprehensive digital learning experience that includes guides, labs, and interactive virtual servers.