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Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. With Grail, we have reinvented analytics for converged observability and security data,” Greifeneder says. Logs on Grail Log data is foundational for any IT analytics. Grail and DQL will give you new superpowers.”
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.
To unlock the agility to drive this innovation, organizations are embracing multicloud environments and Agile delivery practices. Fragmented monitoring and analytics can’t keep up The continued reliance on fragmented monitoring tools and manual analytics strategies is a particular pain point for IT and security teams.
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.
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.
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.
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.
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.
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 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. They enable developers, engineers, and architects to drive innovation, but they also introduce new challenges."
Software should forward innovation and drive better business outcomes. Conversely, an open platform can promote interoperability and innovation. Legacy technologies involve dependencies, customization, and governance that hamper innovation and create inertia. AI-powered precise answers and timely insights with ad-hoc analytics.
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?
As a result, organizations need software to work perfectly to create customer experiences, deliver innovation, and generate operational efficiency. 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.
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. I can keep track of where I went. Clair said.
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.
These innovations promise to streamline operations, boost efficiency, and offer deeper insights for enterprises using AWS services. By automating OneAgent deployment at the image creation stage, organizations can immediately equip every EC2 instance with real-time monitoring and AI-powered analytics.
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….
They now use modern observability to monitor expanding cloud environments in order to operate more efficiently, innovate faster and more securely, and to deliver consistently better business results. In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics.
However, organizational efficiency can’t come at the expense of innovation and growth. As a result, teams can accelerate the pace of digital transformation and innovation instead of cutting back. 2: Observability, security, and business analytics will converge as organizations strive to tame the data explosion.
In what follows, we define software automation as well as software analytics and outline their importance. What is software analytics? This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. We also discuss the role of AI for IT operations (AIOps) and more.
Organizations continue to turn to multicloud architecture to deliver better, more secure software faster. To combat the cloud management inefficiencies that result, IT pros need technologies that enable them to gain insight into the complexity of these cloud architectures and to make sense of the volumes of data they generate.
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. An advanced observability solution can also be used to automate more processes, increasing efficiency and innovation among Ops and Apps teams.
Teams need a better way to work together, eliminate silos and spend more time innovating. 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.
Also, these modern, cloud-native architectures produce an immense volume, velocity, and variety of data. To connect these siloes, and to make sense out of it requires massive manual efforts including code changes and maintenance, heavy integrations, or working with multiple analytics tools.
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.
Currently, there is a tough balance to achieve: Organizations need to innovate rapidly at scale, yet security remains paramount. Our guide covers AI for effective DevSecOps, converging observability and security, and cybersecurity analytics for threat detection and response. Discover more insights from the 2024 CISO Report.
As part of the Cloud – Native Container Services report, ISG designed the Cloud-Native Observability Quadrant to help organizations select the best observability solution for cloud-native environments that use Kubernetes, service mesh, microservices, and serverless architectures.
In the past, monolith architectures could only be implemented with big bang deployments which result in a slow pace of innovation and significant downtime. Creating dashboards highlighting business analytics of each deployment. From big bang to progressive deployments. Using the Davis assistant for comparison and benchmarking.
Across the cloud operations lifecycle, especially in organizations operating at enterprise scale, the sheer volume of cloud-native services and dynamic architectures generate a massive amount of data. In general, generative AI can empower AWS users to further accelerate and optimize their cloud journeys. So, what is artificial intelligence?
As businesses take steps to innovate faster, software development quality—and application security—have moved front and center. These DevSecOps trends will also aid teams as they integrate security and compliance into processes without slowing innovation or creating additional work for already time-strapped teams. Dynatrace news.
Log collection platforms, such as Fluent Bit, give organizations a much-needed solution for quickly gathering and processing log data to make it available in different backends for further analytics. Dynatrace is tech agnostic, having been purpose-built with cloud-native architectures in mind.
You have to get automation and analytical capabilities.” Modern observability allows organizations to eliminate data silos, boost cloud operations, innovate faster, and improve business results. Traditional cloud monitoring methods can no longer scale to meet organizations’ demands, as multicloud architectures continue to expand.
A traditional log management solution uses an often manual and siloed approach, which limits scalability and ultimately hinders organizational innovation. To stay ahead of the curve, organizations should focus on strategic, proactive innovation and optimization. Free IT teams to focus on and support product innovation.
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.
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. A holistic, unified approach to extracting analytics at scale is necessary to keep pace with digital transformation.
Deploying and safeguarding software services has become increasingly complex despite numerous innovations, such as containers, Kubernetes, and platform engineering. Organizations strive to strike a delicate balance between cost, time to market, and innovation. Organizations must balance many factors to stay competitive.
Cloud application security remains challenging because organizations lack end-to-end visibility into cloud architecture. As organizations migrate applications to the cloud, they must balance the agility that microservices architecture brings with the complexity and lack of transparency that can also come with it.
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).
Serverless architecture enables organizations to deliver applications more efficiently without the overhead of on-premises infrastructure, which has revolutionized software development. These tools simply can’t provide the observability needed to keep pace with the growing complexity and dynamism of hybrid and multicloud architecture.
As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. Following the innovation of microservices, serverless computing is the next step in the evolution of how applications are built in the cloud. Simplify error analytics. Dynatrace news.
Unified observability and security When the company’s expanding portfolio and digital-first innovation began transforming how it went to market, the energy leader made the investment to migrate legacy applications to the Amazon Web Services (AWS) cloud.
Lambda serverless functions help developers innovate faster, scale easier, and reduce operational overhead, removing the burden of managing underlying infrastructure when updating and deploying code. The latest Amazon Lambda innovation, Lambda SnapStart, has day one support from Dynatrace. Simplify error analytics.
The increasing complexity of cloud service architectures requires a rock-solid understanding of the activity, health status, and security of cloud services. Dynatrace Log Management and Analytics powered by Grail enables you to get answers from logs with any query at any time.
Serverless functions help developers innovate faster, scale easier and reduce operational overhead, removing the burden of managing underlying infrastructure when updating and deploying code. As you build applications and rely more and more on Lambda architectures you need full observability of all tiers of the supporting infrastructure.
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