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CyberSecurity Breakthrough is a leading independent market intelligence organization that highlights the top companies, technologies, and products in the global information security market. It also breaks down silos across the technology stack, allowing for rapid, scalable analysis and automation to prevent issues before they impact users.
Adopting AI to enhance efficiency and boost productivity is critical in a time of exploding data, cloud complexities, and disparate technologies. Dynatrace delivers AI-powered, data-driven insights and intelligent automation for cloud-native technologies including Azure.
The rapid evolution of cloud technology continues to shape how businesses operate and compete. AWS’ recent recognition of Dynatrace as the 2024 AWS EMEA Technology Partner of the Year highlights the joint commitment to accelerate customer cloud transformation.
that offers security, scalability, and simplicity of use. already address SNMP, WMI, SQL databases, and Prometheus technologies, serving the monitoring needs of hundreds of Dynatrace customers. are technologically very different, Python and JMX extensions designed for Extension Framework 1.0 Extensions 2.0 Extensions 2.0
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.
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.
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….
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.
Messaging systems can significantly improve the reliability, performance, and scalability of the communication processes between applications and services. We’ve introduced brand-new analytics capabilities by building on top of existing features for messaging systems. Easily troubleshoot anomalies with technology-specific views.
The complexity of such deployments has accelerated with the adoption of emerging, open-source technologies that generate telemetry data, which is exploding in terms of volume, speed, and cardinality. How can we optimize for performance and scalability? Dynatrace extends its unique topology-based analytics and AIOps approach.
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. Current analytics tools are fragmented and lack context for meaningful analysis. Effective analytics with the Dynatrace Query Language.
To solve this problem , Dynatrace offers a fully automated approach to infrastructure and application observability including Kubernetes control plane, deployments, pods, nodes, and a wide array of cloud-native technologies. Embracing cloud native best practices to increase automation. The technicalities are simple to understand.
Today’s organizations flock to multicloud environments for myriad reasons, including increased scalability, agility, and performance. In fact, according to recent Dynatrace research, 85% of technology leaders say the number of tools, platforms, dashboards, and applications they use adds to the complexity of managing a multicloud environment.
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.
A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. Unraveling these hidden threats requires a proactive and adaptive approach, leveraging advanced technologies and threat intelligence to uncover vulnerabilities and mitigate potential risks.
The exponential growth of data volume—including observability, security, software lifecycle, and business data—forces organizations to deal with cost increases while providing flexible, robust, and scalable ingest. This “data in context” feeds Davis® AI, the Dynatrace hypermodal AI , and enables schema-less and index-free analytics.
This massive migration is critical to organizations’ digital transformation , placing cloud technology front and center and elevating the need for greater visibility, efficiency, and scalability delivered by a unified observability and security platform. Optimization. Thoughtful reinvestment.
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.
The annual Google Cloud Next conference explores the latest innovations for cloud technology and Google Cloud. As organizations continue to expand within cloud-native environments using Google Cloud, ensuring scalability becomes a top priority. This year, Google’s event will take place from April 9 to 11 in Las Vegas.
has been a key to success in many technological endeavors and to lead in many scientific domains. Waqas Dhillon : The goal of in-database machine learning is to bring popular machine learning algorithms and advanced analytical functions directly to the data, where it most commonly resides – either in a data warehouse or a data lake.
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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. That’s especially true of the DevOps teams who must drive digital-fueled sustainable growth.
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. Dynatrace supports scalable data ingestion, ensuring your observability infrastructure grows with your cloud environment.
Werner Vogels weblog on building scalable and robust distributed systems. The Amazon.com 2010 Shareholder Letter Focusses on Technology. In the 2010 Shareholder Letter Jeff Bezos writes about the unique technologies developed at Amazon.com over the years. All Things Distributed. By Werner Vogels on 27 April 2011 12:51 AM.
Werner Vogels weblog on building scalable and robust distributed systems. a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications. s Dynamo technology , which was one of the first non-relational databases developed at Amazon. This was not our technology vendorsâ?? All Things Distributed.
These technologies are poorly suited to address the needs of modern enterprises—getting real value from data beyond isolated metrics. Grail needs to support security data as well as business analytics data and use cases. From the beginning, Grail was built to be fast and scalable to manage massive volumes of data.
A traditional log management solution uses an often manual and siloed approach, which limits scalability and ultimately hinders organizational innovation. They need to automate manual tasks, streamline processes, and invest in new technologies. Cloud-based log management technologies reduce total cost of ownership.
As end-to-end observability has become critical, we believe this placement reflects our commitment to delivering innovation that helps our customers solve their most complex business challenges with AI-powered observability, analytics, and automation. As a result, a unified observability and security platform has become mandatory.
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. And although technology has become more central to their business strategies, they are juggling many priorities in digital transformation.
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. Netflix software infrastructure is a large distributed ecosystem that consists of specialized functional tiers that are operated on the AWS and Netflix owned services.
Organizations across industries are embracing generative AI, a technology that promises faster development and increased productivity. 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.
If cloud-native technologies and containers are on your radar, you’ve likely encountered Docker and Kubernetes and might be wondering how they relate to each other. In a nutshell, they are complementary and, in part, overlapping technologies to create, manage, and operate containers. Dynatrace news. But first, some background.
Google Cloud Ready – AlloyDB is a new designation for the solutions of Google Cloud’s technology partners that integrate with AlloyDB. Recognizing Dynatrace as Google Cloud Ready – AlloyDB validates their support for and integrations with AlloyDB” said Ritika Suri, Director, Technology Partnerships at Google Cloud.
While technologies have enabled new productivity and efficiencies, customer expectations have grown exponentially, cyberthreat risks continue to mount, and the pace of business has sped up. It’s being recognized around the world as a transformative technology for delivering productivity gains. What is artificial intelligence?
Cloud technology complexity with billions of dependencies has outgrown human comprehension and requires AI to analyze and conclude. As responsibilities shift left due to the increased use of cloud-native technologies, development teams take more control over production deployments.
Open-source metric sources automatically map to our Smartscape model for AI analytics. With this announcement, Dynatrace brings the value of its AI engine, the scale, security, and automation of Dynatrace OneAgent and the scale of our platform (which can handle 50,000 hosts) to open source technologies so that you get the best of both worlds.
The Dynatrace platform automatically integrates OpenTelemetry data, thereby providing the highest possible scalability, enterprise manageability, seamless processing of data, and, most importantly the best analytics through Davis (our AI-driven analytics engine), and automation support available. What Dynatrace will contribute.
Although the adoption of serverless functions brings many benefits, including scalability, quick deployments, and updates, it also introduces visibility and monitoring challenges to CloudOps and DevOps. From here you can use Dynatrace analytics capabilities to understand the response time, or failures, or jump to individual PurePaths.
They contribute to efficiency, scalability , and improved decision-making, making them indispensable in modern software development. They also provide customization options, allowing developers to tailor software solutions to specific business requirements.
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.
After a decade of helping companies manage container orchestration, Kubernetes, the open source container platform, has established itself as a mature enterprise technology. The company receives tens of thousands of requests per second on its edge layer and sees hundreds of millions of events per hour on its analytics layer.
Many organizations attempt to combine tools, products, and do-it-yourself solutions with custom code to fulfill custom use cases that are specific to their unique business requirements and technology stacks. Not only are these approaches difficult and costly to maintain, they also lack proper security and scalability.
To cope with the risk of cyberattacks, companies should implement robust security measures combining proactive preventive measures such as runtime vulnerability analytics , with comprehensive application and perimeter protection through firewalls, intrusion detection systems, and regular security audits.
The goal of observability is to understand what’s happening across all these environments and among the technologies, so you can detect and resolve issues to keep your systems efficient and reliable and your customers happy. Making observability actionable and scalable for IT teams.
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