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By automating OneAgent deployment at the image creation stage, organizations can immediately equip every EC2 instance with real-time monitoring and AI-powered analytics. By ingesting EventBridge events into the Dynatrace platform, customers can leverage AI-powered contextual insights to gain a deeper understanding of their cloud environments.
Following the launch of Dynatrace® Grail for Log Management and Analytics , we’re excited to announce a major update to our Business Analytics solution. Business events deliver the industry’s broadest, deepest, and easiest access to your critical business data. Business events, Grail, and OneAgent.
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.
DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. Clearly, continuing to depend on siloed systems, disjointed monitoring tools, and manual analytics is no longer sustainable.
As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Fortunately, CISOs can use security analytics to improve visibility of complex environments and enable proactive protection. What is security analytics? Why is security analytics important? Here’s how.
This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries. This architecture also means you are not required to determine your log data use cases beforehand or while analyzing logs within the new logs app.
Kubernetes teams lack simple, consistent, vendor-agnostic architectures for analyzing observability signals across teams. Second, embracing the complexity of OpenTelemetry signal collection must come with a guaranteed payoff: gaining analytical insights and causal relationships that improve business performance.
Its AI-driven exploratory analytics help organizations navigate modern software deployment complexities, quickly identify issues before they arise, shorten remediation journeys, and enable preventive operations. AI-driven analytics transform data analysis, making it faster and easier to uncover insights and act.
RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. What is RabbitMQ? What is Apache Kafka?
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. By following key log analytics and log management best practices, teams can get more business value from their data.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. A log is a detailed, timestamped record of an event generated by an operating system, computing environment, application, server, or network device.
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.
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.
Analytical Insights Additionally, impression history offers insightful information for addressing a number of platform-related analytics queries. Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset.
Today’s digital businesses run on heterogeneous and highly dynamic architectures with interconnected applications and microservices deployed via Kubernetes and other cloud-native platforms. Common questions include: Where do bottlenecks occur in our architecture? Dynatrace extends its unique topology-based analytics and AIOps approach.
IT pros want a data and analytics solution that doesn’t require tradeoffs between speed, scale, and cost. With a data and analytics approach that focuses on performance without sacrificing cost, IT pros can gain access to answers that indicate precisely which service just went down and the root cause. Event severity.
An example of a critical event-based messaging service for many businesses is adding a product to a shopping cart. In serverless and microservices architectures, messaging systems are often used to build asynchronous service-to-service communication. Finally, you can configure and activate them there. New to Dynatrace?
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In this blog post, we explain what Greenplum is, and break down the Greenplum architecture, advantages, major use cases, and how to get started. Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. The Greenplum Architecture.
Grail needs to support security data as well as business analytics data and use cases. With that in mind, Grail needs to achieve three main goals with minimal impact to cost: Cope with and manage an enormous amount of data —both on ingest and analytics. Grail architectural basics. Work with different and independent data types.
As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. All data in context : By bringing together metrics, logs, traces, user behavior, and security events into one platform, Dynatrace eliminates silos and delivers real-time, end-to-end visibility.
These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues. Further, these resources support countless Kubernetes clusters and Java-based architectures. where an error occurred at the code level.
Also, these modern, cloud-native architectures produce an immense volume, velocity, and variety of data. Logs and events play an essential role in this mix; they include critical information which can’t be found anywhere else, like details on transactions, processes, users and environment changes.
Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. Causal AI is a different approach that goes beyond event correlations to understand the underlying reasons for trends and patterns. That’s where causal AI can help. Causal AI is particularly effective in observability.
Automate disk resizing operations with Davis AI predictive analytics The Dynatrace Site Reliability Engineering (SRE) team was looking for a way to automatically adjust disk space for cloud volumes on a regular basis to avoid over- or under-provisioning them. Hyperscaler security events are ingested into Grail as BizEvents.
Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the cloud network infrastructure to address the identified problems. Flow Collector consumes two data streams, the IP address change events from Sonar via Kafka and eBPF flow log data from the Flow Exporter sidecars. What is BPF?
Security analysts are drowning, with 70% of security events left unexplored , crucial months or even years can pass before breaches are understood. After a security event, many organizations often don’t know for months—or even years—when, why, or how it happened. Discover more insights from the 2024 CISO Report.
Causal AI—which brings AI-enabled actionable insights to IT operations—and a data lakehouse, such as Dynatrace Grail , can help break down silos among ITOps, DevSecOps, site reliability engineering, and business analytics teams. Logs are automatically produced and time-stamped documentation of events relevant to cloud architectures.
As dynamic systems architectures increase in complexity and scale, IT teams face mounting pressure to track and respond to conditions and issues across their multi-cloud environments. Dynatrace news. As teams begin collecting and working with observability data, they are also realizing its benefits to the business, not just IT.
In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. Check out the guide from last year’s event. IT pros need a data and analytics platform that doesn’t require sacrifices among speed, scale, and cost. We’ll post news here as it happens!
A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. Because events in cloud-native environments take place instantaneously, and there is so much data to digest, IT and operations teams often can’t identify problems before customers experience them.
With a growing number of cloud-native applications built on containers and microservices-based architectures, the number and variety of databases become complex and difficult to manage at scale. With auto-adaptive baselining in response to the specific requirements of your environment, Davis® only raises alerts for important events.
Additionally, predictions based on historical data are reactive, solely relying on past information to anticipate future events, and can’t prevent all new or emerging issues. Automatic root cause detection Modern, complex, and distributed environments generate a substantial number of events.
We’re delighted to share that IBM and Dynatrace have joined forces to bring the Dynatrace Operator, along with the comprehensive capabilities of the Dynatrace platform, to Red Hat OpenShift on the IBM Power architecture (ppc64le). Having all data in context tremendously simplifies analytics and problem detection.
Table name Default bucket logs default_logs events default_events metrics default_metrics bizevents default_bizevents dt.system.events dt_system_events entities spans (in the future) The default buckets let you ingest data immediately, but you can also create additional custom buckets to make the most of Grail.
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This enables unified observability because logs are indispensable for troubleshooting apps, monitoring infrastructure, auditing or investigating security incidents, tracking business events, and many other use cases. Native support for OpenTelemetry (OTLP) logs also supports enterprises that have highly diverse technical architectures.
Trace your application Imagine a microservices architecture with hundreds of dependencies. This architecture also means you’re not required to determine your log data use cases beforehand or while analyzing logs within the new logs app. Interact with data intuitively and easily and benefit from immediate, AI-supported insights.
Cloud-native technologies and microservice architectures have shifted technical complexity from the source code of services to the interconnections between services. Heterogeneous cloud-native microservice architectures can lead to visibility gaps in distributed traces. Dynatrace news. Deep-code execution details.
Output plugins deliver logs to storage solutions, analytics tools, and observability platforms like Dynatrace. While Fluentd solves the challenges of collecting and normalizing Kubernetes events and logs, Kubernetes performance and availability problems can rarely be solved by investigating logs in isolation.
Network traffic growth is the main reason for increasing spending, largely because of the adoption of hybrid and multi-cloud architectures. It’s more complex than it sounds.” As cloud entities multiply, along with greater reliance on microservices and serverless architectures, so do the complex relationships and dependencies among them.
As we did with IBM Power , we’re delighted to share that IBM and Dynatrace have joined forces to bring the Dynatrace Operator, along with the comprehensive capabilities of the Dynatrace platform, to Red Hat OpenShift on the IBM Z and LinuxONE architecture (s390x).
Traditional log management solution challenges Survey data suggests that teams need a modern approach to log management and analytics, which requires a unified log management solution. Dynatrace Log Management and Analytics provides a unified and comprehensive log management solution. during 2021–2026.
As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. It allows for the breaking up of heavy monolithic architectures into multiple serverless “functions.” Simplify error analytics. Understand and optimize your architecture. Dynatrace news.
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