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DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. Indeed, around 85% of technology leaders believe their problems are compounded by the number of tools, platforms, dashboards, and applications they rely on to manage multicloud environments.
Azure observability and Azure data analytics are critical requirements amid the deluge of data in Azure cloud computing environments. Dynatrace recently announced the availability of its latest core innovations for customers running the Dynatrace® platform on Microsoft Azure, including Grail. Digital transformation 2.0
As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. This lack of visibility creates blind spots and makes it difficult to ensure the health of applications running on serverless technologies. x runtime versions of Azure Functions running in an Azure App Service plan.
What is Azure Functions? Similar to AWS Lambda , Azure Functions is a serverless compute service by Microsoft that can run code in response to predetermined events or conditions (triggers), such as an order arriving on an IoT system, or a specific queue receiving a new message. The growth of Azure cloud computing.
As organizations adopt microservices architecture with cloud-native technologies such as Microsoft Azure , many quickly notice an increase in operational complexity. To guide organizations through their cloud migrations, Microsoft developed the Azure Well-Architected Framework. Dynatrace news. Operational excellence.
As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. This lack of visibility creates blind spots and makes it difficult to ensure the health of applications running on serverless technologies. x runtime versions of Azure Functions running in an Azure App Service plan.
This method of structuring, developing, and operating complex, multi-function software as a collection of smaller independent services is known as microservice architecture. ” it helps to understand the monolithic architectures that preceded them. Understanding monolithic architectures. Microservices benefits.
This method of structuring, developing, and operating complex, multi-function software as a collection of smaller independent services is known as microservice architecture. ” it helps to understand the monolithic architectures that preceded them. Understanding monolithic architectures. Microservices benefits.
Many organizations are taking a microservices approach to IT architecture. However, in some cases, an organization may be better suited to another architecture approach. Therefore, it’s critical to weigh the advantages of microservices against its potential issues, other architecture approaches, and your unique business needs.
As adoption rates for Microsoft Azure continue to skyrocket, Dynatrace is developing a deeper integration with the platform to provide even more value to organizations that run their businesses on Azure or use it as a part of their multi-cloud strategy. Azure Batch. Azure DB for MariaDB. Azure DB for MySQL.
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….
Considering that the big three cloud vendors (AWS, GCP, and Microsoft Azure) all now offer their own flavor of managed Kubernetes services, it is easy to see how it has become ever more prolific in the “cloud-native architecture” space. Like all cloud-native technologies, Kubernetes can be a challenge to test locally.
AI-powered automation and deep, broad observability for serverless architectures. In addition to existing support for AWS Lambda , this support now covers Microsoft Azure Functions and Google Cloud Functions as well as managed Kubernetes environments, messaging queues, and cloud databases across all major cloud providers. Visit our?
The phrase “serverless computing” appears contradictory at first, but for years now, successful companies have understood the benefit of using serverless technologies to streamline operations and reduce costs. In a serverless architecture, applications are distributed to meet demand and scale requirements efficiently.
To drive better outcomes using hybrid cloud architectures, it helps to understand their benefits—and how to orchestrate them seamlessly. What is hybrid cloud architecture? Hybrid cloud architecture is a computing environment that shares data and applications on a combination of public clouds and on-premises private clouds.
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.
According to Forrester Research, the COVID-19 pandemic fueled investment in “hyperscaler public clouds”—Amazon Web Services (AWS), Google Cloud Platform and Microsoft Azure. Further, Forrester predicted that 25% of developers will use serverless technologies and nearly 30% will use containers regularly by the end of 2021.
While Kubernetes is still a relatively young technology, a large majority of global enterprises use it to run business-critical applications in production. Findings provide insights into Kubernetes practitioners’ infrastructure preferences and how they use advanced Kubernetes platform technologies. Java, Go, and Node.js
You also might be required to capture syslog messages from cloud services on AWS, Azure, and Google Cloud related to resource provisioning, scaling, and security events. These include traditional on-premises network devices and servers for infrastructure applications like databases, websites, or email.
Example 1: Architecture boundaries. This multinational information technology service and consulting company was asked to help a global automotive manufacturer with the management goal of measuring service flow performance. First, they took a big step back and looked at their end-to-end architecture (Figure 2).
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.
At this year’s Perform, we are thrilled to have our three strategic cloud partners, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), returning as both sponsors and presenters to share their expertise about cloud modernization and observability of generative AI models. What will the new architecture be?
In today’s rapidly evolving technological landscape, organizations are increasingly embracing cloud-native architectures and leveraging the power of Kubernetes for application deployment and management. And then, we will set up the end-to-end service communication on multi-cloud Kubernetes clusters on AWS and Azure.
While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. This is simply not possible with conventional architectures. Data management.
While many companies now enlist public cloud services such as Amazon Web Services, Google Public Cloud, or Microsoft Azure to achieve their business goals, a majority also use hybrid cloud infrastructure to accommodate traditional applications that can’t be easily migrated to public clouds. build microservices-based architecture.
It requires specialized talent, a new technology stack to manage and deploy models, an ample budget for rising compute costs, and end-to-end security. Figure 1: Sample RAG architecture While this approach significantly improves the response quality of GenAI applications, it also introduces new challenges.
The digital future that’s on the horizon is reliant on the power of the cloud and will require the best technology to match. Hyperscale is the ability of an architecture to scale appropriately as increased demand is added to the system. But what does that look like? What is hyperscale? The post What’s the hype with hyperscale?
As cloud-native, distributed architectures proliferate, the need for DevOps technologies and DevOps platform engineers has increased as well. It coincides with the advent of complex, distributed cloud technologies such as microservices and containers. Microsoft Azure. A DevOps platform engineer is a more recent term.
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. With Dynatrace, there is no need to think about schema and indexes, re-hydration, or hot/cold storage concepts.
Modern IT organizations are generating more data from more tools and technologies than ever. Data is proliferating in separate silos from containers and Kubernetes to open source APIs and software to serverless compute services, such as AWS and Azure.
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.
Popular examples include AWS Lambda and Microsoft Azure Functions , but new providers are constantly emerging as this model becomes more mainstream. Finally, because the delivery of compute resources happens entirely in the cloud, the technology enables enterprises to go serverless at the local level. Architectural complexity.
Thanks to its event-driven architecture, Keptn can pull SLIs (=metrics) from different data sources and validate them against the SLOs. You can also start creating dashboard templates to faster onboard applications of a certain type or technology stack.
To understand the root cause, the Dynatrace AI engine, Davis®, uses AI-driven PurePath technology to analyze the journey of an individual user request in the browser and trace all the way to the back end to see how it’s contributing to the problem, down to the line of code that was called. More about Kubernetes. Get your free eBook now!
Hyperconverged infrastructure (HCI) is an IT architecture that combines servers, storage, and networking functions into a unified, software-centric platform to streamline resource management. Specialized vendors combine the system and software of different providers into prepackaged tool sets and technologies that help streamline operations.
But DIY projects require extensive planning and careful consideration, including choosing the right technology stack, outlining the application’s framework, selecting a design system for the user interface, and ensuring everything is secure, compliant, and scalable to meet the requirements of large enterprises.
FinOps aligns technology initiatives with business objectives while maintaining financial transparency and accountability to reduce unnecessary cloud spend and lower costs. Suboptimal architecture design. This proactive alerting involves combining many technologies that already exist in the Dynatrace platform.
And how can you verify this performance consistently across a multicloud environment that also uses Microsoft Azure and Google Cloud Platform frameworks? Workflows are powered by a core platform technology of Dynatrace called the AutomationEngine.
The devil is in the detail, though because of the sheer number, breadth, and volatility of technologies used in modern architectures and the immense volume, velocity, and variety of data they produce. Explore and access 500+ automatically and natively supported technologies.
Spiraling cloud architecture and application costs have driven the need for new approaches to cloud spend. A McKinsey & Company FinOps study indicated that “enterprises often don’t develop at-scale FinOps capabilities until their spending on cloud architecture reaches $100 million per year.”
Automatically collect and evaluate business, service, and architectural indicator metrics to promote or roll back deployments. Kubernetes deployments can be managed using a combination of both the open-source Azure Kubernetes set context Action and Kubernetes deployment GitHub Action. SLO validation – ?Automatically
Container technology enables organizations to efficiently develop cloud-native applications or to modernize legacy applications to take advantage of cloud services. This clinic will walk you through Dynatrace’s log monitoring and analytics capabilities, with a specific focus on Kubernetes and cloud-native architectures.
That’s why, in part, major cloud providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform are discussing cloud optimization. Traditional cloud monitoring methods can no longer scale to meet organizations’ demands, as multicloud architectures continue to expand. But it is also about process automation.
When American Family Insurance took the multicloud plunge, they turned to Dynatrace to automate Amazon Web Services (AWS) event ingestion, instrument compute and serverless cloud technologies, and create a single workflow for unified event management. Step 2: Instrument compute and serverless cloud technologies. ski explains.
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