The biggest event of Dynatrace, Perform 2024, is over, and no matter whether you made it to Las Vegas to see it live, watched it virtually, or missed it altogether, we got you! Below, you'll find the summary of the most important and exciting things announced during Perform. Enjoy the read 📰
For all Perform coverage, check the Dynatrace Perform 2024 Guide.
📝 Blogs
- Cloud observability now mandatory for organizations to thrive amid digital disruption
- Mitigating risk with AI observability: Dynatrace empowers organizations to embrace AI for all use ca...
- Cloud cost optimization: Dynatrace helps organizations manage cloud cost
- Generative AI model observability, cloud modernization take center stage with partners at Dynatrace ...
📰 Press releases
- Dynatrace Teams with Lloyds Banking Group to Reduce IT Carbon Emissions
- Dynatrace Releases OpenPipeline for its Analytics and Automation Platform
- Dynatrace Unveils Data Observability for its Analytics and Automation Platform
- Dynatrace Launches AI Observability for Large Language Models and Generative AI
Product news
- Dynatrace OpenPipeline: Stream processing data ingestion converges observability, security, and busi...
- Dynatrace accelerates business transformation with new AI observability solution
- Introducing Dynatrace built-in data observability on Davis AI and Grail
- Trace, diagnose, resolve: Introducing the Infrastructure & Operations app for streamlined troublesho...
- Dynatrace launches Databases app to provide DBA insights across all databases
- Speed up evidence-driven security investigations and threat hunting with Dynatrace Security Investig...
- Observe and optimize multicloud environments with the Dynatrace Clouds app
- Kubernetes health at a glance: One experience to rule it all
- Dynatrace extends AI-powered observability for SAP together with PowerConnect
- Embrace enterprise-wide observability and security with Foundation & Discovery
📃 Documentation
- Dynatrace use cases. See a selection of Dynatrace use cases available to the teams to make their work observable and easier. You can use these cases to provide automation and self-service capabilities to your users—the development teams.
- Data Observability. Data Observability, borrowing ideas from Software Observability, pertains to the ability to understand the full data lifecycle in an organization. It involves monitoring and managing the internal state of data systems from ingestion to storage and usage. It is about gaining insight into the data pipeline, understanding how data evolves, and identifying any issues that could compromise its integrity or reliability.
- AI/ML Observability. AI Observability is the practice of monitoring, analyzing, and visualizing the internal states, inputs, and outputs of artificial intelligence (AI) models that are embedded and used within modern applications. The goal of AI Observability is to gain insights and understand the behavior, performance, and cost of AI models to ensure their correctness, reliability, and effectiveness. Moreover, the result of AI Observability enables organizations to report their usability of AI for compliance purposes.
- Platform Engineering. Platform engineering is a modern, core engineering discipline that has emerged to accelerate the development and deployment of software that is resilient and effective at scale. The goal is to operationalize DevSecOps and SRE practices by providing an internal self-service platform with development workspace templates to reduce the cognitive load on engineering teams and provide fast development feedback cycles out of the box.
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Test pipeline observability. This guide presents a straightforward method for utilizing Dynatrace to observe and analyze test pipelines effectively. Aimed at both technical and non-technical stakeholders, it focuses on how to efficiently monitor test activities, enabling teams to concentrate on critical aspects of the testing process.
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Release validation. Business-critical services require a thorough validation before deploying to production as potential faults have a negative impact on overall productivity. The flexibility of various Dynatrace tools can help you to optimize your delivery process.
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Data-driven cloud tuning. By adopting these best practices, you can streamline cloud usage, leading to significant cost savings and a more efficient cloud infrastructure. Simple yet impactful, this use case is about smart resource management for optimal financial and operational benefits.
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Predictive Kubernetes operations. In today's dynamic IT landscape, managing disk space effectively in Kubernetes-based services is crucial for maintaining optimal performance and avoiding potential data loss. This best practice guide demonstrates how teams can utilize Dynatrace workflows for automated disk resizing to proactively manage disk space within Kubernetes environments.
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Instant Intrusion Response. Time is crucial when dealing with security incidents. This page shows how you can use Dynatrace to speed up your incident response in two phases.
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Automated bug triaging and ticketing. In today's continuous delivery approach, even with extensive QA processes, some errors are only discovered through real-life infrastructure usage as close to production as possible. Dynatrace allows R&D teams to easily react to bugs or problems by integrating a Jira ticket creation workflow to resolve team dependencies.
We recognize the collection may not be complete, so feel free to share your must-read or must-watch materials, as well as your thoughts and feedback, in the comments 👇
Thank you, and see you around the Community!
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