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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. The missed SLO can be analytically explored and improved using Davis insights on an out-of-the-box Kubernetes workload overview.
We introduced Digital Business Analytics in part one as a way for our customers to tie business metrics to application performance and user experience, delivering unified insights into how these metrics influence business milestones and KPIs. A sample Digital Business Analytics dashboard. Dynatrace news.
Cloud-native technologies and microservice architectures have shifted technical complexity from the source code of services to the interconnections between services. Deep-code execution details. Dynatrace news. Observability for heterogeneous cloud-native technologies is key. Always-on profiling in transaction context.
With PurePath ® distributed tracing and analysis technology at the code level, Dynatrace already provides the deepest possible insights into every transaction. By unifying log analytics with PurePath tracing, Dynatrace is now able to automatically connect monitored logs with PurePath distributed traces. How to get started.
Recently, a critical vulnerability was discovered in Apache Struts, a widely used Java-based web application framework. 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.
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. Introduction Apache Struts 2 is a widely used Java framework for web applications, valued for its flexibility and Model-View-Controller (MVC) architecture.
Dynatrace provides automatic and intelligent observability without touching any code through auto-instrumentation, thereby helping you to better understand potential issues that may impact your end users’ experience.
Dynatrace has been building automated application instrumentation—without the need to modify source code—for over 15 years already. Driving the implementation of higher-level APIs—also called “typed spans”—to simplify the implementation of semantically strong tracing code. What Dynatrace will contribute.
focused on technology coverage, building on the flexibility of JMX for Java and Python-based coded extensions for everything else. While Python code can address most data acquisition and ingest requirements, it comes at the cost of complexity in implementation and use-case modeling. Dynatrace Extensions 1.0 Extensions 2.0
As an example, many retailers already leverage containerized workloads in-store to enhance customer experiences using video analytics or streamline inventory management using RFID tracking for improved security. In this case, Davis finds that a Java Spring Micrometer metric called Failed deliveries is highly correlated with CPU spikes.
These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues. Further, these resources support countless Kubernetes clusters and Java-based architectures. where an error occurred at the code level.
This information specifies which function in the source code relates to a vulnerability. Let’s assume the Java library shown in figure 1 is affected by vulnerability CVE-2024-XYZ. To give users additional insights, Dynatrace provides vulnerable function usage information for certain vulnerable software packages.
Developers use generative AI to find errors in code and automatically document their code. They can also use generative AI for cybersecurity, write prototype code, and implement complex software systems. But as the Black Hat 2023 agenda indicates, generative AI also introduces new security risks. A new CISO report explains why.
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. This technique is shown in the code snippet below.
This means, you don’t need to change even a single line of code in the serverless functions themselves. From here you can use Dynatrace analytics capabilities to understand the response time, or failures, or jump to individual PurePaths. In upcoming sprints, additional improvements will include: Support for Java-based functions.
Utilizing an additional OpenTelemetry SDK layer, this data seamlessly flows into the Dynatrace environment, offering advanced analytics and a holistic view of the AI deployment stack. How OpenLLMetry works OpenLLMetry supports AI model observability by capturing and normalizing key performance indicators (KPIs) from diverse AI frameworks.
More than 20 leading cloud and operations analytics vendors have added support to their products — including Dynatrace, which is one of the top contributors to the project. OpenTelemetry was purposely conceived to complement — and not compete with — existing analytical tools. The other option is semi-automatic instrumentation.
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).
Teams are embracing new technologies and continuously deploying code. But what if you could see what’s running in production in real-time, continuously analyzing all services for vulnerabilities, and prioritizing those based on what code is called? They also can’t provide deep insights unless you have source code access.
Another nifty Session Replay feature is the ability to capture custom events—events that are not typically captured by default—irrespective of whether the codebase is Java or Kotlin. By examining the specific actions that a user took and the outcome, teams can trace errors back to features or code and address the root causes.
OneAgent also provides Spring Micrometer metrics with best-in-class distributed tracing, plus memory and garbage collector analysis for Spring Java applications and microservices. Either way, the Dynatrace Micrometer Registry adds proper topology without modifying your code. This bit of special sauce deserved a short explanation.)
Lack of context —most vulnerability scanners don’t provide runtime context and key information like whether vulnerable code is used at runtime. Starting with Dynatrace version 1.225 and OneAgent version 1.207, you can detect runtime vulnerabilities and assess risks across Java, Node.js,NET, How to get started.
It is known for its flexibility and large feature set, as well as supporting databases utilizing a Java Database Connectivity (JDBC) driver, rendering it a default tool for both DBAs and developers. Data visualization and analytics tools with a direct integration with Tableau are possible. Built-in version control (Git integration).
While memory allocation analysis can show wasteful or inefficient code, it can also reveal different problems, one of which we’ll examine in this blog post. We recently extended the pre-shipped code-level API definitions to group logical parts of our code so they’re consistently highlighted in all code-level views.
To ensure observability, the open source CNCF project OpenTelemetry aims at providing a standardized, vendor-neutral way of pre-instrumenting libraries and platforms and annotating UserLAnd code. New OpenTelemetry metrics exporters provide the broadest language support on the market.
For example, the open source Java library at the heart of the Log4Shell crisis in 2021 was patched within days given the pervasiveness of the code. How vulnerabilities are evaluated – platform module Learn the mechanism that Dynatrace Application Security uses to generate third-party vulnerabilities and code-level vulnerabilities.
Log4Shell is a software vulnerability in Apache Log4j 2 , a popular Java library for logging information in applications. The vulnerability enables a remote attacker to execute arbitrary code on a service on the internet if the service runs certain versions of Log4j 2.
Enterprise data stores grow with the promise of analytics and the use of data to enable behavioral security solutions, cognitive analytics, and monitoring and supervision. Consider Log4Shell, a software vulnerability in Apache Log4j 2 , a popular Java library.
I worked on providing code-level insights for Java and.NET services and applications before shifting gears and joining the OpenTelemetry community back in May 2019. This entails prioritizing the roadmap, conducting code reviews, and submitting code contributions. years ago.
Such additional telemetry data includes user-behavior analytics, code-level visibility, and metadata (including open-source data). PurePath 4 integrates OpenTelemetry Go data for enterprise-grade collection and contextual AI analytics. Unlock extended end-to-end traceability for OpenTelemetry-instrumented Go applications.
I have been using it at my current tour through different conferences ( Devoxx , Confitura ) and meetups, ( Cloud Native , KraQA , Trojmiasto Java UG ) where I’ve promoted keptn. Dynatrace log analytics gives us access to the logs in the context of the current problem.
Impact : This issue affects only those extensions that use native libraries called from Python code distributed with the extension. New analytics view for message queues. Fixed an issue in which the Kubernetes detail page crashed with a 403 status code for management zones users. (APM-341209). Extension-specific advisory.
Instrumentation involves adding code to your application to collect this tracking information, akin to installing security cameras in a store to monitor customer movement and behavior. OpenTelemetry supports a variety of languages, including Java, Python, JavaScript, and more, making it accessible to most applications.
NET , Java , JavaScript/Node.js , and Python. Our metric exporters allow for ingestion of OpenTelemetry-instrumented custom metrics into the Dynatrace open analytics and AI platform, giving you precise and actionable analytics across the entire software life cycle.
The addition of the Digital Business Analytics module to the Dynatrace Software Intelligence Platform gives you a new way to understand the impact that application errors, performance and user behavior have on your business. inside any of your microservices via HTTP headers or payloads, Java/.Net Net method parameters, etc).
But its underlying goal is quite humble and straightforward: it wants to enable you to observe an IT system (for example, a web application, infrastructure, or services) and gain insight to its behavior, such as performance, error rates, hot spots of executed instructions in code, and more. Those are prime candidates for their own spans.
The next level of observability: OneAgent In the first two parts of our series, we used OpenTelemetry to manually instrument our application and send the telemetry data straight to the Dynatrace analytics back end. However, this method limited us to instrumenting the code manually and collecting specific sets of data we defined upfront.
The supported programming languages for PostgreSQL include.Net, C, C++, Delphi, Java, JavaScript (Node.js), Perl, PHP, Python and Tcl, but PostgreSQL can support many server-side procedural languages through its available extensions. We found that Java is the most popular programming language for PostgreSQL, being leveraged by 31.1%
Join Etleap , an Amazon Redshift ETL tool to learn the latest trends in designing a modern analytics infrastructure. Learn what has changed in the analytics landscape and how to avoid the major pitfalls which can hinder your organization from growth. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and.NET.
Join Etleap , an Amazon Redshift ETL tool to learn the latest trends in designing a modern analytics infrastructure. Learn what has changed in the analytics landscape and how to avoid the major pitfalls which can hinder your organization from growth. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and.NET.
Join Etleap , an Amazon Redshift ETL tool to learn the latest trends in designing a modern analytics infrastructure. Learn what has changed in the analytics landscape and how to avoid the major pitfalls which can hinder your organization from growth. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and.NET.
For heads of IT/Engineering responsible for building an analytics infrastructure , Etleap is an ETL solution for creating perfect data pipelines from day one. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and.NET. It runs natively on.Net, and provides a native.Net, COM & ODBC apis for integration.
Join Etleap , an Amazon Redshift ETL tool to learn the latest trends in designing a modern analytics infrastructure. Learn what has changed in the analytics landscape and how to avoid the major pitfalls which can hinder your organization from growth. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and.NET.
Join Etleap , an Amazon Redshift ETL tool to learn the latest trends in designing a modern analytics infrastructure. Learn what has changed in the analytics landscape and how to avoid the major pitfalls which can hinder your organization from growth. Client libraries are available for Node, Ruby, Python, PHP, Go, Java and.NET.
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