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But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise. With the Dynatrace modern observability platform, teams can now use intuitive, low-code/no-code toolsets and causal AI to extend answer-driven automation for business, development and security workflows.
What developers want Developers want to own their code in a distributed, ephemeral, cloud, microservices-based environment. This ownership starts with understanding how their code behaves in all environments, resolving issues, and writing and optimizing code in a high-quality, secure, and timely manner.
The VS Code extension Dynatrace Apps is here to streamline your development process and simplify app building. Now you can easily query live data directly within VS Code using the Dynatrace Query Language (DQL). Re)Using queries within your app Once you are happy with your query’s result, you can easily use it in your app code.
In this article, we will explore how AI can assist in these areas, providing codeexamples to tackle complex queries. Leveraging AI can revolutionize query optimization and predictive maintenance, ensuring the database remains efficient, secure, and responsive.
All-new Dynatrace code-level vulnerability detection All-new Dynatrace code-level vulnerability detection evaluates all requests passing through your applications to identify vulnerabilities. The deep insights into application code provided by OneAgent® help track potentially vulnerable data flow within an application.
For example, user behavior helps identify attacks or fraud. Another example is when anomaly detection identifies services impacted by ransomware. Runtime Security integrates seamlessly with static code analyzers, container scanners, and application security testing tools.
The IT world is rife with jargon — and “as code” is no exception. “As code” means simplifying complex and time-consuming tasks by automating some, or all, of their processes. Today, the composable nature of code enables skilled IT teams to create and customize automated solutions capable of improving efficiency.
Below are some of the best practices with codingexamples to optimize performance in Azure Cosmos DB. Likewise, in Azure Cosmos DB, optimization is crucial for maximizing efficiency, minimizing costs, and ensuring that your application scales effectively.
Netflix was thrilled to be the premier sponsor for the 2nd year in a row at the 2023 Conference on Digital Experimentation (CODE@MIT) in Cambridge, MA. For example, how do we estimate the effects of innovations on retention a year later without running all our experiments for a year?
For this example, we go to Simple Workflows and select Trigger > Davis event trigger to find these out-of-memory errors. If you ever need more advanced capabilitieslike custom code, multi-step logic, or conditional tasksyou can seamlessly upgrade to standard workflows with just a few clicks. Theyre free and unlimited.
Broken Apache Struts 2: Technical Deep Dive into CVE-2024-53677The vulnerability allows attackers to manipulate file upload parameters, possibly leading to remote code execution. This allows attackers to manipulate file upload parameters, leading to unauthorized file placement and potentially remote code execution (RCE).
No problem—out-of-the-box templates simplify the setup of time series data based on service requests, and templates can quickly be adapted for more fine-grain needs, for example, to consider only single endpoints. At the same time, dedicated configuration-as-code support in Monaco and Terraform will provide a scalable, automated solution.
In my previous article on Pydantic, I introduced you to Logfire in one of the codeexamples as an observability platform designed to provide developers with insights into Python applications.
In this example use case, we’re investigating a high CPU load issue in an application using OpenTelemetry. Dynatrace OpenPipeline is configured to ingest logs from our environment, including the AdService example service. Live Debugger allows you to set non-breaking breakpoints, capturing critical data snapshots in real time.
A natural solution is to make flows configurable using configuration files, so variants can be defined without changing the code. Unlike parameters, configs can be used more widely in your flow code, particularly, they can be used in step or flow level decorators as well as to set defaults for parameters.
To pinpoint the exact requests that are failing, the e-commerce team filters by excluding successful HTTP 200 status codes. You can even walk through the same example above. The team filters further by the “/cart/checkout endpoint” attribute value. This refinement reveals that only a few requests are failing.
Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft. Metric definitions are often scattered across various databases, documentation sites, and code repositories, making it difficult for analysts and data scientists to find reliable information quickly.
Let's explore the key features of these platforms and examine some codeexamples to illustrate their practical applications. These platforms provide developers with powerful tools to monitor, debug, and optimize AI agents, ensuring their reliability, efficiency, and scalability.
These vulnerabilities, identified as CVE-2025-1097 , CVE-2025-1098 , CVE-2025-24514 , and CVE-2025-1974 , allow unauthenticated remote code execution (RCE) and unauthorized access to secrets stored across all namespaces in a Kubernetes cluster. Please see the sample queries appendix for an example query to get started.
Amazon Bedrock , equipped with Dynatrace Davis AI and LLM observability , gives you end-to-end insight into the Generative AI stack, from code-level visibility and performance metrics to GenAI-specific guardrails. The specific foundation model version (for example, anthropic.claude-v1 or amazon.nova).
Segments can implement variables to dynamically provide, for example, a list of entities to users, such as available Kubernetes clusters, for unmatched flexibility and dynamic segmentation. For example, a segment for Service Errors in Azure Region can be applied instantly by selecting it from the dropdown. What are Dynatrace Segments?
And by “sample” we mean “an example”, like food samples in your local grocery store. One of the main reasons this feature exists is just like with food samples, to give you “a taste” of the production quality ETL code that you could encounter inside the Netflix data ecosystem. This is one way to build trust with our internal user base.
Most of these leverage the unique capability of Dynatrace OneAgent® to extract business data from in-flight application payloads — without writing any code. Since then, many of our customers have embraced the opportunity to explore and adopt new business analytics use cases.
Here is an example: Simple example: SYCL matrix multiply microbenchmark (Click for interactive SVG.) The gray "-" frames just help highlight the boundary between CPU and AI/GPU code. A daily tool for developers, with most of the visualization in the language of the developer : source code functions.
At the same time, Snyk focuses on various artifacts, conducting comprehensive assessments of code and container repositories. During the SDLC development phases, various artifacts of your applications are assessed for security issues from their code phase throughout the build and deploy phases until they run in your production.
To provide automated feedback for developers, the concept of quality gates for static code analysis in continuous integration is widely adopted throughout the industry. The developer must pause their current engineering work to address the reported issue and consider the code changes they worked on a few days or weeks prior.
For example, it supports string and numerical values, enabling a multitude of different use cases. Use color coding to tell a story. For example, set the value range for CPU consumption from 0% to 100%. They have become a quasi-standard in the industry, especially for infrastructure monitoring visualizations. Min and max limits.
This is one example of the many use cases we’re exploring. For example, it can help DevOps and platform engineering teams write code snippets by drawing on information from software libraries. It highlights the potential of GPT technology to drive “information democracy” even further.
This vulnerability, published as CVE-2024-53677 on December 11, 2024, affects the file upload mechanism, allowing for path traversal and potential remote code execution. This critical flaw allows for a path traversal attack, enabling attackers to upload malicious files and potentially execute arbitrary code on the server.
Dynatrace Configuration as Code enables complete automation of the Dynatrace platform’s configuration, ensuring that software is secure and reliable. With Configuration as Code, developers can manage their observability and security tasks with config files that can be developed alongside source code conveniently and at scale.
One of the most common examples is the adoption of microservices. It enables teams to quickly pinpoint the root cause of issues, fix them and optimize the application performance, giving them the confidence to deliver code faster. When organizations move toward the cloud, their systems also lean toward distributed architectures.
In this article, I will shed some light on what differentiates unit testing from other methods and will bring examples of when we can or cannot do without unit testing. We'll also touch upon automation testing , which plays an important role in ensuring code reliability and quality.
Onboarding teams using self-service Kubernetes selectors is one of the best examples of how Dynatrace embraces cloud native technologies to increase automation, reduce bureaucracy, and encourage agility. The following example drives the point home. Embracing cloud native best practices to increase automation. Putting it all together.
Did you always want to know more about instrumentation, metrics, and your options for coding with open standards? Are you a Java developer and looking for a working example to get started instrumenting your applications and services?
Example: Exception analysis Understanding patterns, especially regarding exceptions, is no easy feat. For example, you can filter to understand endpoint performance where exception messages contain the string, access denied. These powerful insights can easily be transformed into interactive dashboards.
The automated extraction of ownership information, for example, from Kubernetes annotations, is therefore essential. Dynatrace ownership functionality supports configuration-as-code via its proprietary Monaco (Monitoring as code) CLI or Terraform. An example via Monaco can be found in this public GitHub repository.
Evaluating these on three levels—data center, host, and application architecture (plus code)—is helpful. And while these examples were resolved by just asking a few questions, in many cases, the answers are more elusive, requiring real-time and historical drill-downs into the processes and dependencies specific to each host.
Let's take the example of an online marketplace application. These services can be developed and maintained separately, promoting code modularity and enhancing overall system agility.
But developers need code-level visibility and code-level data.” That’s not how I envision code-level observability,” Laifenfeld said. Laifenfeld argued that developers shouldn’t bear the burden of the additional workload when their focus is their code: “Learning Kubernetes as a developer is not easy,” she said.
Similar to the observability desired for a request being processed by your digital services, it’s necessary to comprehend the metrics, traces, logs, and events associated with a code change from development through to production. Code : The branch for the new feature in a GitHub repository is merged into the main branch.
Dynatrace code modules, enabled via Dynatrace webhook, provide distributed tracing and code-level visibility for applications deployed on Kubernetes. With this approach: Red Hat OpenShift infrastructure (control plane and worker nodes) and workloads are instrumented automatically without manual code change.
With this additional context—for example, location in code, initial and transition states, interaction types, and more—Dynatrace makes sense of the user journey and the technical components in use. Example user action generated by an app using Jetpack Compose auto-instrumentation. Auto-capture support has been expanded.
For example: Exploitation awareness: Identify vulnerabilities based on whether they’re exposed to critical assets or have exploits circulating in the wild. Unified vulnerabilities view The new Dynatrace platform consolidates third-party and code-level vulnerabilities into a single, intuitive view.
Managing Auto-Instrumentation in Pods The Operator automatically injects and configures auto-instrumentation for your applications, which enables you to collect telemetry data without modifying your source code. Instrumentation Instrumentation is the process of adding code to software to generate telemetry signalslogs, metrics, and traces.
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