This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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. With ASR, and other new and enhanced technologies we introduce, rigorous analytics and measurement are essential to their success.
Metadata enrichment improves collaboration and increases analytic value. The Dynatrace® platform continues to increase the value of your data — broadening and simplifying real-time access, enriching context, and delivering insightful, AI-augmented analytics. Our Business Analytics solution is a prominent beneficiary of this commitment.
Adopting AI to enhance efficiency and boost productivity is critical in a time of exploding data, cloud complexities, and disparate technologies. Dynatrace delivers AI-powered, data-driven insights and intelligent automation for cloud-native technologies including Azure.
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. Metric extraction is a convenient way to create your business metrics, delivering fast, flexible, and cost-effective analytics.
As organizations adopt more cloud-native technologies, the risk—and consequences—of cyberattacks are also increasing. Through this integration, Dynatrace enriches data collected by Microsoft Sentinel to provide organizations with enhanced data insights in context of their full technology stack.
To continue down the carbon reduction path, IT leaders must drive carbon optimization initiatives into the hands of IT operations teams, arming them with the tools needed to support analytics and optimization. We implemented a wasted energy metric in the app to enhance practitioner actionability.
Davis AI contextually aligns all relevant data points—such as logs, traces, and metrics—enabling teams to act quickly and accurately while still providing power users with the flexibility and depth they desire and need. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
Dynatrace recently opened up the enterprise-grade functionalities of Dynatrace OneAgent to all the data needed for observability, including metrics, events, logs, traces, and topology data. Davis topology-aware anomaly detection and alerting for your custom metrics. Seamlessly report and be alerted on topology-related custom metrics.
Indeed, around 85% of technology leaders believe their problems are compounded by the number of tools, platforms, dashboards, and applications they rely on to manage multicloud environments. Clearly, continuing to depend on siloed systems, disjointed monitoring tools, and manual analytics is no longer sustainable.
We’re proud to announce that Ally Financial has presented Dynatrace with its Ally Technology Velocity with Quality award. This is the second time Ally Financial has presented its Ally Technology Partner Awards. Ally Financial is the home of the nation’s largest all-digital bank and is an industry-leading auto financing business.
As businesses increasingly embrace these technologies, integrating IoT metrics with advanced observability solutions like Dynatrace becomes essential to gaining additional business value through end-to-end observability. Both methods allow you to ingest and process raw data and metrics.
Following the launch of Dynatrace® Grail for Log Management and Analytics , we’re excited to announce a major update to our Business Analytics solution. Leveraging existing APM agent and log monitoring capabilities made it reasonably easy to access certain business metrics and metadata to add to IT dashboards.
We’ve introduced brand-new analytics capabilities by building on top of existing features for messaging systems. Now, with technology-specific views, DevOps teams can see messaging system-related anomalies, which significantly simplifies troubleshooting efforts. Easily troubleshoot anomalies with technology-specific views.
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.
As a technology executive, you’re aware that observability has become an imperative for managing the health of cloud and IT services. Observability data presents executives with new opportunities to achieve this, by creating incremental value for cloud modernization , improved business analytics , and enhanced customer experience.
The rapidly evolving digital landscape is one important factor in the acceleration of such transformations – microservices architectures, service mesh, Kubernetes, Functions as a Service (FaaS), and other technologies now enable teams to innovate much faster. New cloud-native technologies make observability more important than ever….
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. If you’ve read about observability, you likely know that collecting the measurements of logs, metrics, and distributed traces are the three key pillars to achieving success.
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. What’s behind it all?
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 analytics? Log analytics is the process of evaluating and interpreting log data so teams can quickly detect and resolve issues.
The complexity of such deployments has accelerated with the adoption of emerging, open-source technologies that generate telemetry data, which is exploding in terms of volume, speed, and cardinality. Dynatrace extends its unique topology-based analytics and AIOps approach.
We introduced Dynatrace’s 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. Only with Dynatrace Digital Busines Analytics.
Log management and analytics is an essential part of any organization’s infrastructure, and it’s no secret the industry has suffered from a shortage of innovation for several years. Current analytics tools are fragmented and lack context for meaningful analysis. Effective analytics with the Dynatrace Query Language.
The only way to address these challenges is through observability data — logs, metrics, and traces. IT pros want a data and analytics solution that doesn’t require tradeoffs between speed, scale, and cost. The next frontier: Data and analytics-centric software intelligence. Enter Grail-powered data and analytics.
With 99% of organizations using multicloud environments , effectively monitoring cloud operations with AI-driven analytics and automation is critical. IT operations analytics (ITOA) with artificial intelligence (AI) capabilities supports faster cloud deployment of digital products and services and trusted business insights.
Building on its advanced analytics capabilities for Prometheus data , Dynatrace now enables you to create extensions based on Prometheus metrics. This allows teams to extend the intelligent observability Dynatrace provides to all technologies that provide Prometheus exporters. Our monitoring coverage already includes ?
The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. With Grail, we have reinvented analytics for converged observability and security data,” Greifeneder says.
In Part 1 we explored how you can use the Davis AI to analyze your StatsD metrics. In Part 2 we showed how you can run multidimensional analysis for external metrics that are ingested via the OneAgent Metric API. In Part 3 we discussed how the Davis AI can analyze your metrics from scripting languages like Bash or PowerShell.
Manual and configuration-heavy approaches to putting telemetry data into context and connecting metrics, traces, and logs simply don’t scale. With PurePath ® distributed tracing and analysis technology at the code level, Dynatrace already provides the deepest possible insights into every transaction. New to Dynatrace?
Dynatrace collects a huge number of metrics for each OneAgent-monitored host in your environment. Depending on the types of technologies you’re running on individual hosts, the average number of metrics is about 500 per computational node. Running metric queries on a subset of entities for live monitoring and system overviews.
With siloed data sources, heterogeneous data types—including metrics, traces, logs, user behavior, business events, vulnerabilities, threats, lifecycle events, and more—and increasing tool sprawl, it’s next to impossible to offer users real-time access to data in a unified, contextualized view. Understanding the context.
A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. As our experience with MOVEit shows, IoCs that remained hidden in logs alone quickly revealed themselves with observability runtime context data, such as metrics, traces, and spans.
already address SNMP, WMI, SQL databases, and Prometheus technologies, serving the monitoring needs of hundreds of Dynatrace customers. are technologically very different, Python and JMX extensions designed for Extension Framework 1.0 Extensions can monitor virtually any type of technology in your environment. Extensions 2.0
Google Cloud Ready – AlloyDB is a new designation for the solutions of Google Cloud’s technology partners that integrate with AlloyDB. With this Google Cloud Ready integration, Dynatrace ensures that AlloyDB for PostgreSQL users can now ingest metrics along with existing Google Cloud data.
DevOps metrics and digital experience data are critical to this. Bringing teams together around DevOps metrics made it easier for M&B to identify how it could create better digital experiences for its customers and optimize revenue. Dynatrace news. Beginnings of BizDevOps.
In fact, according to recent Dynatrace research, 85% of technology leaders say the number of tools, platforms, dashboards, and applications they use adds to the complexity of managing a multicloud environment. This fragmented approach adds complexity and opens the door to security vulnerabilities.
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. Send unified data to Dynatrace for analysis alongside your logs, metrics, and traces.
This massive migration is critical to organizations’ digital transformation , placing cloud technology front and center and elevating the need for greater visibility, efficiency, and scalability delivered by a unified observability and security platform. Thoughtful reinvestment. Organizations then had to manually react to it.
We added monitoring and analytics for log streams from Kubernetes and multicloud platforms like AWS, GCP, and Azure, as well as the most widely used open-source log data frameworks. This also applies to logging use cases for certain technologies. Manual tracking of metrics from logs is too complex at scale.
This nuanced integration of data and technology empowers us to offer bespoke content recommendations. Analytical Insights Additionally, impression history offers insightful information for addressing a number of platform-related analytics queries.
It collects, processes, and outputs log files to and from a wide variety of technologies. Output plugins deliver logs to storage solutions, analytics tools, and observability platforms like Dynatrace. All metrics, traces, and real user data are also surfaced in the context of specific events.
Not only are cyberattacks increasing, but they’re also becoming more sophisticated, with tools such as WormGPT putting generative AI technology in the hands of attackers. In this blog post, we’ll use Dynatrace Security Analytics to go threat hunting, bringing together logs, traces, metrics, and, crucially, threat alerts.
Welcome back to the second part of our blog series on how easy it is to get enterprise-grade observability at scale in Dynatrace for your OpenTelemetry custom metrics. In Part 1 , we announced our new OpenTelemetry custom-metric exporters that provide the broadest language coverage on the market, including Go , .NET record(value); }.
With observability across all these technologies, teams can quickly deploy high-quality releases that deliver outstanding customer experiences. T o get performance insights into applications and efficiently troubleshoot and optimize them, you need precise and actionable analytics across the entire software life cycle.
Customers can also proactively address issues using Davis AI’s predictive analytics capabilities by analyzing network log content, such as retries or anomalies in performance response times. Dynatrace natively supports Syslog using ActiveGate (preferred method) or the OpenTelemetry (OTel) collector.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content