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We are excited to share that Dynatrace has been named the Cloud Security Platform of the Year in the prestigious 2024 CyberSecurity Breakthrough Awards. Additionally, 68% of CISOs struggle with vulnerability management because the complexity of their software supply chain and cloud ecosystem is beyond human capability.
Adopting AI to enhance efficiency and boost productivity is critical in a time of exploding data, cloud complexities, and disparate technologies. At this year’s Microsoft Ignite, taking place in Chicago on November 19-22, attendees will explore how AI enables and accelerates organizations throughout their cloud modernization journeys.
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.
As cloud complexity increases and security concerns mount, organizations need log analytics to discover and investigate issues and gain critical business intelligence. But exploring the breadth of log analytics scenarios with most log vendors often results in unexpectedly high monthly log bills and aggressive year-over-year costs.
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 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 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.
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.
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.
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. This enables companies to ingest, analyze, and retain massive quantities of data with powerful analytics and AI-powered answers.
Dynatrace Cloud Native Full Stack injection for Kubernetes, now officially released, provides unparalleled flexibility and scale for onboarding teams to AI-powered observability. The foundation of this flexibility is the Dynatrace Operator ¹ and its new Cloud Native Full Stack injection deployment strategy. Dynatrace news.
This is where Davis AI for exploratory analytics can make all the difference. FinOps: Track irregularities in cloud spending or resource usage, enabling cost optimization and preventing budget overruns. The forecast is created instantly, even for large data sets, and updates dynamically whenever filter settings are changed.
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.
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.
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.
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.
Data processing in the cloud has become increasingly popular due to its scalability, flexibility, and cost-effectiveness. This article will explore how these technologies can be used together to create an optimized data pipeline for data processing in the cloud.
New cloud-native technologies make observability more important than ever…. Dynatrace PurePath 4 extends automatic distributed tracing to OpenTelemetry and the latest cloud-native technologies. In this example you can see on the left side that the Envoy payment service is running on a Linux host, deployed in the Google cloud.
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.
A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. With the rising complexity of cloud-native environments, manual investigation and response are too slow and inaccurate. What can you do with Dynatrace Security Analytics?
Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. This decoupling simplifies system architecture and supports scalability in distributed environments. Kafka achieves scalability by distributing topics across multiple partitions and replicating them among brokers.
Today’s organizations flock to multicloud environments for myriad reasons, including increased scalability, agility, and performance. 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.
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.
To make this happen, enterprises are shifting an unprecedented volume of workloads onto cloud platforms such as Microsoft Azure. The benefits of migrating on-premises workloads to the Cloud become evident quickly. Observability is inherent to any cloud strategy. Thoughtful reinvestment. Observability with AI and automation.
Multicloud strategy: Balancing potential with complexity in modern IT ecosystems In the ever-changing digital world, cloud technologies are crucial in driving business innovation and adaptability. They offer unmatched flexibility and scalability to meet the fluctuating demands of the market.
All these micro-services are currently operated in AWS cloud infrastructure. Finally, provisioning our infrastructure itself is also becoming an increasingly complex task, so our data teams contribute to tools for diagnosis and automation of our cloud capacity management.
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. This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes. What Exactly is Greenplum? At a glance – TLDR.
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.
Know anyone who needs cloud? I wrote Explain the Cloud Like I'm 10 just for them. According to 451 Research’s Voice of the Enterprise: Data & Analytics, 28% of businesses run analytics on their employee behavior data, roughly the same number that analyze IT infrastructure data. Do you like this sort of Stuff?
How can you reduce the carbon footprint of your hybrid cloud? energy-efficient data centers—cloud providers—achieve values closer to 1.2. Is the solution to just move all workloads to the cloud? There might not be enough cloud capacity where you need it. How will your organization respond to this global challenge?
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.
Know anyone looking for a simple book explaining the cloud? book: Explain the Cloud Like I'm 10. As a result, most organizations are down-sampling, which can impact the accuracy of machine models and created unnecessary steps to the predictive analytics process. Do you like this sort of Stuff? Please support me on Patreon.
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. Seamless integration with AWS Firehose Dynatrace is also enhancing our observability logs offerings for AWS services for cloud-native applications.
Increasingly, organizations are turning to modern observability platforms to address the complexity of, and gain visibility into, cloud environments. Further, automation has become a core strategy as organizations migrate to and operate in the cloud. Check out the guide from last year’s event. What is a data lakehouse?
Exploring artificial intelligence in cloud computing reveals a game-changing synergy. This article delves into the specifics of how AI optimizes cloud efficiency, ensures scalability, and reinforces security, providing a glimpse at its transformative role without giving away extensive details.
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. Observability relies on telemetry derived from instrumentation that comes from the endpoints and services in your multi-cloud computing environments.
They’re unleashing the power of cloud-based analytics on large data sets to unlock the insights they and the business need to make smarter decisions. From a technical perspective, however, cloud-based analytics can be challenging. Cloud complexity leads to data silos Most organizations are battling cloud complexity.
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. It’s based on cloud-native architecture and built for the cloud.
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? Declarative APIs.
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.
Today, Dynatrace is announcing that it has successfully achieved Google Cloud Ready – AlloyDB designation in support of an extended integration to Google Cloud’s AlloyDB for PostgreSQL. Google Cloud Ready – AlloyDB is a new designation for the solutions of Google Cloud’s technology partners that integrate with AlloyDB.
In this AWS re:Invent 2023 guide, we explore the role of generative AI in the issues organizations face as they move to the cloud: IT automation, cloud migration and digital transformation, application security, and more. In general, generative AI can empower AWS users to further accelerate and optimize their cloud journeys.
With the exponential rise of cloud technologies and their indisputable benefits such as lower total cost of ownership, accelerated release cycles, and massed scalability, it’s no wonder organizations clamor to migrate workloads to the cloud and realize these gains.
As organizations strive to digitally transform, innovate, and grow in today’s fast-paced environment, they have increasingly turned to cloud technologies to enable business goals. Yet, as organizations embark on a cloud migration journey, they face overwhelming cloud complexity and an explosion of data to manage.
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