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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.
Today, we discuss C# code quality and a variety of errors by the example of CMS DotNetNuke. We're going to dig into its source code. The source code is available on GitHub. You're going to need a cup of coffee. DotNetNuke. DotNetNuke is an open-source content management system (CMS) written mainly in C#.
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.
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.
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.
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?
Key components of GitOps are declarative infrastructure as code, orchestration, and observability. Many observability solutions don’t support an “as code” approach. Dynatrace enables software intelligence as code. Observability is required for effective collaboration and automation.
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.
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.
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.
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 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.
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, 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.
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?
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.
Although you can code the logic that governs communication directly into the microservices, a service mesh abstracts that logic into a parallel layer of infrastructure using a proxy called a sidecar, which runs alongside each service. A service mesh enables DevOps teams to manage their networking and security policies through code.
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.
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.
Every automated workflow consists of easy configuration, extensive trigger options, out-of-the-box actions and integrations, and unprecedented extensibility by leveraging webhooks, JavaScript code, and application actions powered by AppEngine. Example workflow for event-driven vulnerability reporting and escalation.
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.
The show surrounding logs function provides Dynatrace users with the ability to dive deeper and surface context-specific log lines of the components and services linked to the problem—all without a single line of code or complex query language knowledge. Advanced analytics are not limited to use-case-specific apps.
Organizations can customize quality gate criteria to validate technical service-level objectives (SLOs) and business goals, ensuring early detection and resolution of code deficiencies. Ultimately, quality gates safeguard code viability as it advances through the delivery pipeline. But how do they function in practice?
Although IT teams are thorough in checking their code for any errors, an attacker can always discover a loophole to exploit and damage applications, infrastructure, and critical data. Examples of zero-day vulnerabilities. For example, within a week of the discovery of the Log4Shell vulnerability, Microsoft reported more than 1.8
OpenPipeline allows you to create custom endpoints for data ingestion and process the events in the pipeline (for example, adding custom pipe-dependent fields to simplify data analysis in a later phase). Give your pipeline a name, for example, Automated discovery from Security Investigator. Go to OpenPipeline.
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).
With a critical CVSS rating of 9.8 , Spring4Shell leaves affected systems vulnerable to remote code execution (RCE). In the example below, we have a simple DemoObject class that contains a string attribute message. The Spring Framework exposes the class member of the object the parameter is bound to, for example: [link].
Years later, a few configuration management solutions came into play that required heavy amounts of coding, but proved that the industry was moving toward compartmentalized automation solutions. These evaluations that I hard-coded into a script were now embedded into the back-end of Ansible’s modular approach.
They enable product delivery and SRE teams to turn functionality on and off at runtime without deploying new code. This decoupling of code deployment from feature release is a crucial enabler for modern Continuous Delivery practices. This is just one example; applications of any size or maturity can use OpenFeature.
Indeed, according to one survey, DevOps practices have led to 60% of developers releasing code twice as quickly. But increased speed creates a tradeoff: According to another study, nearly half of organizations consciously deploy vulnerable code because of time pressure. Increased adoption of Infrastructure as code (IaC).
Code changes are often required to refine observability data. This results in site reliability engineers nudging development teams to add resource attributes, endpoints, and tokens to their source code. This example is a good starting point for exploratory analysis with context-aware Dynatrace Davis insights.
The following is an example of a query using the Dynatrace Query Language (DQL) to find out when BSOD issues are being written to Windows System logs. The Dynatrace platform establishes context across all observability data sources – metrics, events, logs, traces, user sessions, synthetic probes, runtime security vulnerabilities, and more.
Four types of tools are commonly used to detect software vulnerabilities: Source-code tests that are used in development environments. Source code tests. Products that scan source code before the container is built are known as Software Composition Analysis (SCA) tools and Static Application Security Test (SAST) tools.
This expansion of Davis AI complements the proven Dynatrace predictive AI model (for example, forecasting and anomalies) and our causal AI model (for example, determination of a problem’s root cause, security risks, user impact, and steering automation), which are at the core of the Dynatrace platform.
For example, nearly two-thirds (61%) of technology leaders say they will increase investment in AI over the next 12 months to speed software development. For example, 73% of technology leaders are investing in AI to generate insight from observability, security, and business events data.
In this article I will provide you with an Entity Framework example, typically resembling the type of code you'd see being used out there, against the typical Hyperlambda equivalent. Watch me run you through the code below.
In addition to requiring a high degree of custom coding, feature flags can rapidly accrue technical debt that can be opaque to diagnose. Using scripting tags, feature flags work without having to deploy new code. Deploy code without releasing it to end users such as new functionality hidden by default behind a feature flag.
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