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When we launched the new Dynatrace experience, we introduced major updates to the platform, including Grail ™, our innovative data lakehouse unifying observability, security, and business data, and Dynatrace Query Language ( DQL ) for accessing and exploring unified data.
There’s a goldmine of business data traversing your IT systems, yet most of it remains untapped. To unlock business value, the data must be: Accessible from anywhere. Data has value only when you can access it, no matter where it lies. Agile business decisions rely on fresh data. Easy to access. Contextualized.
Let’s explore some of the advantages of monitoring GitHub runners using Dynatrace. By integrating Dynatrace with GitHub Actions, you can proactively monitor for potential issues or slowdowns in the deployment processes. This customization ensures that only the relevant metrics are extracted, tailored to the users needs.
To provide maximum freedom in selecting the service-level indicators that matter most to your business, Dynatrace combines SLOs with the power of Dynatrace Grail™ data lakehouse, the central data platform with heterogeneous and contextually linked data. This is where Grail, the Dynatrace central data platform, excels.
Exploratory analytics now cover more bespoke scenarios, allowing you to access any element of test results stored in the Dynatrace Grail data lakehouse. Analyzing the delivered payload (response body), response headers, or even details of requests sent during the monitors execution is invaluable when analyzing the failures root cause.
Take your monitoring, data exploration, and storytelling to the next level with outstanding data visualization All your applications and underlying infrastructure produce vast volumes of data that you need to monitor or analyze for insights.
In this blog post, we’ll walk you through a hands-on demo that showcases how the Distributed Tracing app transforms raw OpenTelemetry data into actionable insights Set up the Demo To run this demo yourself, you’ll need the following: A Dynatrace tenant. If you don’t have one, you can use a trial account.
Imagine you’re using a lot of OpenTelemetry and Prometheus metrics on a crucial platform. You’re gathering a lot of data, but you can’t make sense of it. A histogram is a specific type of metric that allows users to understand the distribution of data points over a period of time.
To understand whats happening in todays complex software ecosystems, you need comprehensive telemetry data to make it all observable. With so many types of technologies in software stacks around the globe, OpenTelemetry has emerged as the de facto standard for gathering telemetry data. But, generating telemetry data is the easy part.
Welcome, data enthusiasts! Whether you’re a seasoned IT expert or a marketing professional looking to improve business performance, understanding the data available to you is essential. In this blog series, we’ll guide you through creating powerful dashboards that transform complex data into actionable insights.
Chances are, youre a seasoned expert who visualizes meticulously identified key metrics across several sophisticated charts. However, your responsibilities might change or expand, and you need to work with unfamiliar data sets. Using a seasonal baseline, you can monitor sales performance based on the past fourteen days.
My goal was to provide IT teams with insights to optimize customer experience by collaborating with business teams, using both business KPIs and IT metrics. Recently, we’ve expanded our digital experience monitoring to cover the entire customer journey, from conversion to fulfillment.
In IT and cloud computing, observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces. As teams begin collecting and working with observability data, they are also realizing its benefits to the business, not just IT. What is observability?
Through this integration, Dynatrace enriches data collected by Microsoft Sentinel to provide organizations with enhanced data insights in context of their full technology stack. In Microsoft Sentinel, security teams benefit from all the signals Dynatrace Davis® AI automatically generates without unsustainable, manual effort.
ABAC has several advantages: Enhanced security , providing granular control over access permissions, significantly reducing the risk of data breaches and unauthorized activities. High granularity by segmenting resource and record-level data, ensuring that access decisions are precise and context-aware.
DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. On average, organizations use 10 different tools to monitor applications, infrastructure, and user experiences across these environments.
More organizations are adopting the OpenTelemetry observability standard in pursuit of a vendor-neutral solution to manual instrumentation, sending data to multiple vendors, and gaining insight into third-party services. This will be used by the OpenTelemetry collector to send data to your Dynatrace tenant.
We are in the era of data explosion, hybrid and multicloud complexities, and AI growth. Dynatrace analyzes billions of interconnected data points to deliver answers, not just data and dashboards sending signals without a path to resolution. Picture gaining insights into your business from the perspective of your users.
Cloud service providers (CSPs) share carbon footprint data with their customers, but the focus of these tools is on reporting and trending, effectively targeting sustainability officers and business leaders. We implemented a wasted energy metric in the app to enhance practitioner actionability.
Digital experience monitoring (DEM) is crucial for organizations to meet this demand and succeed in today’s competitive digital economy. DEM solutions monitor and analyze the quality of digital experiences for users across digital channels.
Monitoring and observability are two key concepts that facilitate this process, offering valuable visibility into the health and performance of systems. In this article, we will explore the differences between monitoring and observability, provide examples to illustrate their applications and highlight their respective benefits.
In fact, according to a Dynatrace global survey of 1,300 CIOs , 99% of enterprises utilize a multicloud environment and seven cloud monitoring solutions on average. What is cloud monitoring? Cloud monitoring is a set of solutions and practices used to observe, measure, analyze, and manage the health of cloud-based IT infrastructure.
For IT infrastructure managers and site reliability engineers, or SREs , logs provide a treasure trove of data. But on their own, logs present just another data silo as IT professionals attempt to troubleshoot and remediate problems. Data volume explosion in multicloud environments poses log issues.
Current synthetic capabilities Dynatrace Synthetic Monitoring is a powerful tool that provides insight into the health of your applications around the clock and as they’re perceived by your end users worldwide. Compared to other solutions I have tested, Dynatrace NAM monitors are the most configurable which is to my liking.
In today’s data-driven world, businesses across various industry verticals increasingly leverage the Internet of Things (IoT) to drive efficiency and innovation. Mining and public transportation organizations commonly rely on IoT to monitor vehicle status and performance and ensure fuel efficiency and operational safety.
As a result, organizations need to monitor mobile app performance metrics that are meaningful and actionable by gaining adequate observability of mobile app performance. There are many common mobile app performance metrics that are used to measure key performance indicators (KPIs) related to user experience and satisfaction.
As an industry leader, Dynatrace promotes primarily using software and AI to deal with this complexity at scale instead of just putting data on dashboards. Does that mean that reactive and exploratory data analysis, often done manually and with the help of dashboards, are dead? Why today’s data analytics solutions still fail us.
But as every business works differently, there is often a need to customize Davis, so that it fits your domain-specific use cases and detects relevant and business-critical anomalies , such as those outlined in the following examples: Detect anomalies within your custom data streams. Modify the time-series data you want to observe.
Cloud-native technologies are driving the need for organizations to adopt a more sophisticated IT monitoring approach to satisfy the competitive demands of modern business. Seeking insights from data Every organization depends on data to make decisions. Business observability is emerging as the answer.
Should business data be part of your observability solution? Technology and business leaders express increasing interest in integrating business data into their IT observability strategies, citing the value of effective collaboration between business and IT.
This challenge has given rise to the discipline of observability engineering, which concentrates on the details of telemetry data to fine-tune observability use cases. To get a more granular look into telemetry data, many analysts rely on custom metrics using Prometheus.
The addition of more and more metrics over time has only made this increasingly complex. As a result, it’s challenging to get business and resources focused on performance and error optimization without supporting data that shows how those optimizations will impact your organization’s financial outcomes.
Additionally, certain tools require auxiliary services to gather performance data before it can be examined and queried. It then collects performance data using existing database services running on your system. It’s all monitored remotely ! Nothing is installed on your IBM i systems.
An hourly rate for Infrastructure Monitoring The Dynatrace Platform Subscription (DPS) offers a flat rate for Infrastructure Monitoring , providing observability for cloud platforms, containers, networks, and data center technologies with no limits on host memory and with AIOps included.
I have ingested important custom data into Dynatrace, critical to running my applications and making accurate business decisions… but can I trust the accuracy and reliability?” ” Welcome to the world of data observability. At its core, data observability is about ensuring the availability, reliability, and quality of data.
Every software development team grappling with Generative AI (GenAI) and LLM-based applications knows the challenge: how to observe, monitor, and secure production-level workloads at scale. Production performance monitoring: Service uptime, service health, CPU, GPU, memory, token usage, and real-time cost and performance metrics.
Traditional monitoring approaches often require manual scripting and integration to get alerted about production-threatening issues in pre-production environments. Your teams want to iterate rapidly but face multiple hurdles: Increased complexity: Microservices and container-based apps generate massive logs and metrics.
In a digital-first world, site reliability engineers and IT data analysts face numerous challenges with data quality and reliability in their quest for cloud control. Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices.
Welcome back to our power dashboarding blog series , data enthusiasts! You can either continue with the custom infrastructure metrics dashboard you created in Part I or use the dashboard we prepared here (Dynatrace login required). Our enhanced host monitoring dashboard that highlights disk usage includes AI forecasting for CPU usage.
Some time ago, at a restaurant near Boston, three Dynatrace colleagues dined and discussed the growing data challenge for enterprises. At its core, this challenge involves a rapid increase in the amount—and complexity—of data collected within a company. Work with different and independent data types. Thus, Grail was born.
The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? Option 1: Log Processing Log processing offers a straightforward solution for monitoring and analyzing title launches.
With the world’s increased reliance on digital services and the organizational pressure on IT teams to innovate faster, the need for DevOps monitoring tools has grown exponentially. But when and how does DevOps monitoring fit into the process? And how do DevOps monitoring tools help teams achieve DevOps efficiency?
Log data—the most verbose form of observability data, complementing other standardized signals like metrics and traces—is especially critical. As cloud complexity grows, it brings more volume, velocity, and variety of log data. They also need a high-performance, real-time analytics platform to make that data actionable.
They can be expensive to implement and maintain, rely on fragile data pipelines, and require highly skilled data analysts to ensure ongoing relevance. Most business processes are not monitored. First and foremost, it’s a data problem.
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