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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. Once the data is formatted, it is ingested into Dynatrace Business Analytics using the Dynatrace SDK.
Developers are key stakeholders in modern observability. In this blog post, we will see how Dynatrace harnesses the power of observability and analytics to tailor a new experience to easily extend to the left, allowing developers to solve issues faster, build more efficient software, and ultimately improve developer experience!
This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. To facilitate easier access to incrementality results, we have developed an interactive tool powered by this framework.
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Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. What is log monitoring? What is log analytics? Log monitoring vs log analytics. Dynatrace news. billion in 2020 to $4.1
This blog post will explore these exciting developments and what they mean for organizations. By automating OneAgent deployment at the image creation stage, organizations can immediately equip every EC2 instance with real-time monitoring and AI-powered analytics.
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Dynatrace enables various teams, such as developers, threat hunters, business analysts, and DevOps, to effortlessly consume advanced log insights within a single platform. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
Membership in MISA is nomination-only and reserved for independent software vendors who develop security solutions that effectively integrate with MISA-qualifying Microsoft Security products. They can automatically identify vulnerabilities, measure risks, and leverage advanced analytics and automation to mitigate issues.
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By following key log analytics and log management best practices, teams can get more business value from their data. Challenges driving the need for log analytics and log management best practices As organizations undergo digital transformation and adopt more cloud computing techniques, data volume is proliferating.
The Dynatrace platform automatically captures and maps metrics, logs, traces, events, user experience data, and security signals into a single datastore, performing contextual analytics through a “power of three AI”—combining causal, predictive, and generative AI. The result?
Administrators who work with developers and product managers responsible for instrumenting, managing, and achieving the business goals of applications benefit from Dynatrace log management. Log management with adherence to industry-specific compliance requirements is more critical than ever. Want to learn more?
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But as most developers know, its the observability backend that reveals the value of your data and instrumentation strategy. The OpenTelemetry community created its demo application, Astronomy Shop, to help developers test the value of OpenTelemetry and the backends they send their data to.
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I’ve always been intrigued by monitoring the inner workings of technology to better understand its impact on the use cases it enables and supports. Executives drive business growth through strategic decisions, relying on data analytics for crucial insights. Common business analytics incur too much latency.
What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.
Today, development teams suffer from a lack of automation for time-consuming tasks, the absence of standardization due to an overabundance of tool options, and insufficiently mature DevSecOps processes. This leads to frustrating bottlenecks for developers attempting to build and deliver software.
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In this blog post, we look at these enhancements, exploring methods for monitoring your Kubernetes environment and showcasing how modern dashboards can transform your data. Leverage dashboards to monitor your environment in real time through log data.
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. This allows ITOps to measure each user journey’s effectiveness and efficiency.
Exploding volumes of business data promise great potential; real-time business insights and exploratory analytics can support agile investment decisions and automation driven by a shared view of measurable business goals. Traditional observability solutions don’t capture or analyze application payloads. What’s next?
Most business processes are not monitored. Undue reliance on log files as the primary business data source adds development overhead for implementation and maintenance, further limiting agility. If you can collect the relevant data (and that’s a big if), the problem shifts to analytics. First and foremost, it’s a data problem.
Although most organizations invest in innovative mobile app development, not many allocate enough resources toward delivering and measuring the high-quality user experiences customers expect. Mobile analytics can help organizations optimize their mobile application performance, earning customer accolades and increasing revenue in the process.
Much of the software developed today is cloud native. Organizations need to unify all this observability, business, and security data based on context and generate real-time insights to inform actions taken by automation systems, as well as business, development, operations, and security teams. Enter Grail-powered data and analytics.
A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. Additionally, Runtime Application Protection provides the ability to protect from attacks while giving development teams much-needed time to remediate these vulnerabilities.
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With extended contextual analytics and AIOps for open observability, Dynatrace now provides you with deep insights into every entity in your IT landscape, enabling you to seamlessly integrate metrics, logs, and traces—the three pillars of observability. Dynatrace extends its unique topology-based analytics and AIOps approach.
As a result, API monitoring has become a must for DevOps teams. So what is API monitoring? What is API Monitoring? API monitoring is the process of collecting and analyzing data about the performance of an API in order to identify problems that impact users. The need for API monitoring.
Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. RabbitMQ can be deployed in distributed environments and includes monitoring tools through a built-in dashboard and CLI. RabbitMQ supports high message volumes but may experience performance drops under heavy loads.
Wouldn’t it be great if I had an industry-leading software intelligence platform to monitor these apps, pinpoint root causes of slow performance or errors, and gain insights about my users’ experience? At Dynatrace we live and breathe the concept of “Drink Your Own Champagne” (DYOC), so of course, I want to use Dynatrace to monitor my apps.
IBM i, formerly known as iSeries, is an operating system developed by IBM for its line of IBM i Power Systems servers. It also includes various built-in software components for database management, security, and application development. It’s all monitored remotely ! What is IBM i? Nothing is installed on your IBM i systems.
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