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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.
Leverage AI for proactive protection: AI and contextual analytics are game changers, automating the detection, prevention, and response to threats in real time. In dynamic and distributed cloud environments, the process of identifying incidents and understanding the material impact is beyond human ability to manage efficiently.
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.
As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. Last year Dynatrace research revealed that the average multi-cloud environment spans 12 different platforms and services, exacerbating the issue of data silos.
Dynatrace continues to deliver on its commitment to keeping your data secure in the cloud. What’s next Next, the enhanced data separation and encryption features are planned for release to all customers on Azure and then to all customers on Google Cloud.
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.
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!
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.
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.
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.
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.
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 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.
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-cloudarchitectures — generate exponentially increasing data volumes.
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.
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….
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.
Azure observability and Azure data analytics are critical requirements amid the deluge of data in Azure cloud computing environments. As digital transformation accelerates and more organizations are migrating workloads to Azure and other cloud environments, they need observability and data analytics capabilities that can keep pace.
Key takeaways from this article on modern observability for serverless architecture: As digital transformation accelerates, organizations need to innovate faster and continually deliver value to customers. Companies often turn to serverless architecture to accelerate modernization efforts while simplifying IT management.
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.
As a result, organizations are weighing microservices vs. monolithic architecture to improve software delivery speed and quality. Traditional monolithic architectures are built around the concept of large applications that are self-contained, independent, and incorporate myriad capabilities. What is monolithic architecture?
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 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.
Organizations continue to turn to multicloud architecture to deliver better, more secure software faster. But IT teams need to embrace IT automation and new data storage models to benefit from modern clouds. As they enlist cloud models, organizations now confront increasing complexity and a data explosion.
In today's fast-paced digital landscape, organizations are increasingly embracing multi-cloud environments and cloud-native architectures to drive innovation and deliver seamless customer experiences.
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. Clair said.
Lifting and shifting applications from the data center to the cloud delivers only marginal benefits. Because cloud computing breaks application functions into many microservices, porting monolithic applications to the cloud unchanged can slow them down. So, what is cloud-native architecture, exactly?
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.
Cloud application security is becoming more of a critical issue as cloud-based applications gain popularity. The cloud allows a modular approach to building applications, enabling development and operations teams to create and deploy feature-rich apps very quickly. What is cloud application security?
A critical security threat for cloud-native architectures SSRF is a web security vulnerability that allows an attacker to make a server-side application send requests to unintended locations. SSRF can lead to unauthorized access to sensitive data, such as cloud metadata, internal databases, and other protected resources.
This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. What is RabbitMQ? What is Apache Kafka?
Without observability, the benefits of ARM are lost Over the last decade and a half, a new wave of computer architecture has overtaken the world. ARM architecture, based on a processor type optimized for cloud and hyperscale computing, has become the most prevalent on the planet, with billions of ARM devices currently in use.
Managing cloud performance is increasingly challenging for organizations that spread workloads across a greater variety of platforms. And according to recent data from Enterprise Strategy Group, 59% of survey respondents indicated spending on public cloud applications would increase in 2023. ” Three years ago, Tractor Supply Co.
I am excited to announce that ISG analysts positioned Dynatrace highest for portfolio attractiveness and competitive strength in the Cloud-Native Observability Solutions Quadrant. . Dynatrace’s deep understanding of enterprises’ cloud-native needs stand s out .
Why unified observability boosts productivity While journalctl is a powerful local tool with local filtering capabilities, it doesn’t scale well, especially considering the globally distributed components of today’s hybrid/cloud-hosted environments.
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.
Today’s organizations face increasing pressure to keep their cloud-based applications performing and secure. Cloud application security remains challenging because organizations lack end-to-end visibility into cloudarchitecture.
How can you reduce the carbon footprint of your hybrid cloud? Evaluating these on three levels—data center, host, and application architecture (plus code)—is helpful. energy-efficient data centers—cloud providers—achieve values closer to 1.2. Is the solution to just move all workloads to the cloud? Want to learn more?
Cloud-native technologies, including Kubernetes and OpenShift, help organizations accelerate innovation. Open source has also become a fundamental building block of the entire cloud-native stack. Why cloud-native applications, Kubernetes, and open source require a radically different approach to application security.
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.
Challenges The cloud network infrastructure that Netflix utilizes today consists of AWS services such as VPC, DirectConnect, VPC Peering, Transit Gateways, NAT Gateways, etc and Netflix owned devices. IP addresses within the cloud can move from one EC2 instance or Titus container to another over time. What is BPF?
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.
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