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The rapid evolution of cloud technology continues to shape how businesses operate and compete. This year’s AWS re:Invent will showcase a suite of new AWS and Dynatrace integrations designed to enhance cloud performance, security, and automation.
Organizations are increasingly embracing cloud- and AI-native strategies, requiring a more automated and intelligent approach to their observability and development practices. Thats why Dynatrace will make its AI-powered, unified observability platform generally available on Google Cloud for all customers later this year.
Leverage AI for proactive protection: AI and contextual analytics are game changers, automating the detection, prevention, and response to threats in real time. For executives, these directives present several challenges, including compliance complexity, resource allocation for continuous monitoring, and incident reporting.
Recently, we’ve expanded our digital experience monitoring to cover the entire customer journey, from conversion to fulfillment. Key insights for executives: Optimize customer experiences through end-to-end contextual analytics from observability, user behavior, and business data. Google or Adobe Analytics).
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? Dynatrace news. In fact, the global log management market is expected to grow from 1.9
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.
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.
As organizations adopt more cloud-native technologies, the risk—and consequences—of cyberattacks are also increasing. The Dynatrace platform has been recognized for seamlessly integrating with the Microsoft Sentinel cloud-native security information and event management ( SIEM ) solution.
Minimize security risks by reducing complexity with unified observability : Converging security with end-to-end observability gives security teams the deep, real-time context they need to strengthen security posture and accelerate detection and response in complex cloud environments.
As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Two-thirds say vulnerability management is becoming harder because of complex supply chain and cloud ecosystems. What is security analytics? Why is security analytics important? Here’s how.
The challenge along the path Well-understood within IT are the coarse reduction levers used to reduce emissions; shifting workloads to the cloud and choosing green energy sources are two prime examples. This is partly due to the complexity of instrumenting and analyzing emissions across diverse cloud and on-premises infrastructures.
We’re excited to announce the expansion of the Dynatrace security portfolio with new Cloud Security Posture Management (CSPM) capabilities. Cloud environments are vast and constantly evolving, making manual identification of misconfigurations virtually impossible. Automation and continuous monitoring. The solution?
Logs provide answers, but monitoring is a challenge Manual tagging is error-prone Making sure your required logs are monitored is a task distributed between the data owner and the monitoring administrator. Often, it comes down to provisioning YAML configuration files and listing the files or log sources required for monitoring.
Cloud-native observability is a prerequisite for companies that need to meet these expectations. By unifying log analytics with PurePath tracing, Dynatrace is now able to automatically connect monitored logs with PurePath distributed traces. Dynatrace news. New to Dynatrace? If so, start your free trial today!
The complexity of modern cloud-native environments is ever-increasing. Visualizing data in context while supporting and automating decisions with causal, predictive, and generative AI—all while providing a seamless experience—is where the future of cloud observability lies.
Many enterprise digital marketing teams use the best-in-class web analytics solutions like Adobe Analytics to see which users are abandon ing their journey , how paid search and email campaigns are performing, and to understand user behavior. Dynatrace news. Real-world example: Retail banking. Did I mention it’s really easy?
Enterprises are turning to Dynatrace for its unified observability approach for cloud-native, on-premises, and hybrid resources. The Clouds app provides a view of all available cloud-native services. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
The growing challenge in modern IT environments is the exponential increase in log telemetry data, driven by the expansion of cloud-native, geographically distributed, container- and microservice-based architectures. Organizations need a more proactive approach to log management to tame this proliferation of cloud data.
FinOps , short for Financial Operations, is a methodology combining finance, technology, and business teams to optimize cloud spending and maximize value in cloud environments. Costs and their origin are transparent, and teams are fully accountable for the efficient usage of cloud resources.
This is where Davis AI for exploratory analytics can make all the difference. For example, if you’re monitoring network traffic and the average over the past 7 days is 500 Mbps, the threshold will adapt to this baseline. Using a seasonal baseline, you can monitor sales performance based on the past fourteen days.
The annual Google Cloud Next conference explores the latest innovations for cloud technology and Google Cloud. Google Cloud users will come together to learn from Google experts and partners on topics from generative AI to cloud operations and security.
This is where observability analytics can help. What is observability analytics? Observability analytics enables users to gain new insights into traditional telemetry data such as logs, metrics, and traces by allowing users to dynamically query any data captured and to deliver actionable insights.
However, today’s highly dynamic cloud-native environments with containers, microservices, and platforms like Kubernetes, make it more challenging to ensure that applications are working as expected and that customers are adopting new features and generating conversions. Optimize mobile app performance and maximize business impact.
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.
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.
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!
GigaOm recently released its 2024 Radar Report for Cloud Observability, which includes 21 observability solution providers and analyzes each on their current and emerging features. According to Dynatrace research , on average, companies have 10 different monitoring tools.
With 99% of organizations using multicloud environments , effectively monitoringcloud 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.
Rising cloud complexity has made securing cloud-native and multicloud applications significantly more difficult. With the pace of digital transformation continuing to accelerate, organizations are realizing the growing imperative to have a robust application security monitoring process in place.
The latest Dynatrace report, “ The state of observability 2024: Overcoming complexity through AI-driven analytics and automation ,” explores these challenges and highlights how IT, business, and security teams can overcome them with a mature AI, analytics, and automation strategy.
Real-time streaming needs real-time analytics As enterprises move their workloads to cloud service providers like Amazon Web Services, the complexity of observing their workloads increases. As cloud complexity grows, it brings more volume, velocity, and variety of log data. Managing this change is difficult.
In cloud-native environments, there can also be dozens of additional services and functions all generating data from user-driven events. This is critical to ensure high performance, security, and a positive user experience for cloud-native applications and services. Comparing log monitoring, log analytics, and log management.
New cloud-native technologies make observability more important than ever…. Methods include the observability capabilities of the platforms their applications run on; monitoring tools, OpenTelemetry, OpenTracing, OpenMonitor, OpenCensus, Jaeger, Zipkin, Log, CloudWatch, and more. This is where Dynatrace comes into play.
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. Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes.
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.
Today’s digital businesses run on heterogeneous and highly dynamic architectures with interconnected applications and microservices deployed via Kubernetes and other cloud-native platforms. All this data is then consumed by Dynatrace Davis® AI for more precise answers, thereby driving AIOps for cloud-native environments.
The adoption of cloud computing in the federal government will accelerate in a meaningful way over the next 12 to 18 months, increasing the importance of cloudmonitoring. This is welcome insight as the Cloud First and Cloud Smart initiatives continue to take root. Obstacles to cloudmonitoring.
The growing complexity of modern multicloud environments has created a pressing need to converge observability and security analytics. Security analytics is a discipline within IT security that focuses on proactive threat prevention using data analysis. The ripple effect of increased risk compounds the problem. To begin, St.
Much of the software developed today is cloud native. However, cloud infrastructure has become increasingly complex. 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. Real-time anomaly detection.
Grail – the foundation of exploratory analytics Grail can already store and process log and business events. Introducing Metrics on Grail Despite their many advantages, modern cloud-native architectures can result in scalability and fragmentation challenges. You no longer need to split, distribute, or pre-aggregate your data.
Leveraging cloud-native technologies like Kubernetes or Red Hat OpenShift in multicloud ecosystems across Amazon Web Services (AWS) , Microsoft Azure, and Google Cloud Platform (GCP) for faster digital transformation introduces a whole host of challenges. Dynatrace news. Connecting data siloes requires daunting integration endeavors.
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?
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. The time taken to complete the page load.
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