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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.
I realized that our platforms unique ability to contextualize security events, metrics, logs, traces, and user behavior could revolutionize the security domain by converging observability and security. Collect observability and security data user behavior, metrics, events, logs, traces (UMELT) once, store it together and analyze in context.
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.
DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. Moreover, teams are constantly dealing with continuously evolving cyberthreats to data both on premises and in the cloud.
Key insights for executives: Optimize customer experiences through end-to-end contextual analytics from observability, user behavior, and business data. Consolidate real-user monitoring, synthetic monitoring, session replay, observability, and business process analytics tools into a unified platform. Google or Adobe Analytics).
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. To close these critical gaps, Dynatrace has defined a new class of events called business events.
For more: Read the Report We live in an era of rapid data generation from countless sources, including sensors, databases, cloud, devices, and more. To keep up, we require real-time analytics (RTA), which provides the immediacy that every user of data today expects and is based on stream processing.
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?
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.
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.
Second, embracing the complexity of OpenTelemetry signal collection must come with a guaranteed payoff: gaining analytical insights and causal relationships that improve business performance. The missed SLO can be analytically explored and improved using Davis insights on an out-of-the-box Kubernetes workload overview.
The annual Google Cloud Next conference explores the latest innovations for cloud technology and Google Cloud. This year, Google’s event will take place from April 9 to 11 in Las Vegas. As organizations continue to expand within cloud-native environments using Google Cloud, ensuring scalability becomes a top priority.
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.
Dynatrace automatically puts logs into context Dynatrace Log Management and Analytics directly addresses these challenges. You can easily pivot between a hot Kubernetes cluster and the log file related to the issue in 2-3 clicks in these Dynatrace® Apps: Infrastructure & Observability (I&O), Databases, Clouds, and Kubernetes.
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.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes.
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.
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. Put simply, context is king.
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.
Exploratory analytics with collaborative analytics capabilities can be a lifeline for CloudOps, ITOps, site reliability engineering, and other teams struggling to access, analyze, and conquer the never-ending deluge of big data. These analytics can help teams understand the stories hidden within the data and share valuable insights.
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.
This is where Davis AI for exploratory analytics can make all the difference. The following example will monitor an end-to-end order flow utilizing business events displayed on a Dynatrace dashboard. FinOps: Track irregularities in cloud spending or resource usage, enabling cost optimization and preventing budget overruns.
Business events are a special class of events, new to Business Analytics; together with Grail, our data lakehouse, they provide the precision and advanced analytics capabilities required by your most important business use cases. What are business events? This diagram shows a few examples of business events.
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.
In many cases, events are generated as these workloads go through different phases of their life cycles. For instance, events appear when the scheduler performs actions to bring workloads back to a desired state. For better or worse, every Kubernetes user learns about the CrashLoopBackOff and ImagePullBackOff events.
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.
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. Event severity.
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.
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 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. Discovery using global search.
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.
With up to 70% of security events going uninvestigated, security analysts need all the help they can get. After a security event, many organizations often don’t know for months (or even years) when why or how it happened. But this limited approach causes challenges in today’s hybrid multicloud reality.
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. Logs provide information you can’t find anywhere else.
Not only that, teams struggle to correlate events and alerts from a wide range of security tools, need to put them into context, and infer their risk for the business. In this blog post, we’ll use Dynatrace Security Analytics to go threat hunting, bringing together logs, traces, metrics, and, crucially, threat alerts.
To stay competitive in an increasingly digital landscape, organizations seek easier access to business analytics data from IT to make better business decisions faster. Five constraints that limit insights from business analytics data. Digital businesses rely on real-time business analytics data to make agile decisions.
The Dynatrace platform now enables comprehensive data exploration and interactive analytics across data sets (trace, logs, events, and metrics)empowering you to solve complex use cases, handle any observability scenario, and gain unprecedented visibility into your systems.
Logs complement metrics and enable automation Cloud practitioners agree that observability, security, and automation go hand in hand. The increasing complexity of cloud service architectures requires a rock-solid understanding of the activity, health status, and security of cloud services.
RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. What is Apache Kafka?
Carbon Impact leverages business events , a special data type designed to support the real-time accuracy and long-term granularity demands common to business use cases. Some use cases benefit from dashboards or ad-hoc analytics, complementing the insights from Carbon Impact.
Grail data lakehouse delivers massively parallel processing for answers at scale Modern cloud-native computing is constantly upping the ante on data volume, variety, and velocity. Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. Kubernetes makes spans longer,” Ortner explains.
With unified observability and security, organizations can protect their data and avoid tool sprawl with a single platform that delivers AI-driven analytics and intelligent automation. Predictive AI, meanwhile, makes predictions about future events based on patterns from historical data. This is Davis CoPilot.
As more organizations invest in a multicloud strategy, improving cloud operations and observability for increased resilience becomes critical to keep up with the accelerating pace of digital transformation. American Family turned to observability for cloud operations. Step 2: Instrument compute and serverless cloud technologies.
However, when working with Kubernetes, its distributed and ephemeral nature means that logs are scattered across multiple nodes and pods, making it difficult to ensure that all logs are preserved, easily accessible, and enriched with necessary context for future analytics. Flexibly choose the level of observability you need.
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