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As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. This integration eliminates the need for separate data collection, transfer, configuration, storage, and analytics, streamlining operations and reducing costs.
Leverage AI for proactive protection: AI and contextual analytics are game changers, automating the detection, prevention, and response to threats in real time. UMELT are kept cost-effectively in a massive parallel processing data lakehouse, enabling contextual analytics at petabyte scale, fast.
By automating OneAgent deployment at the image creation stage, organizations can immediately equip every EC2 instance with real-time monitoring and AI-powered analytics. This is particularly valuable for enterprises deeply invested in VMware infrastructure, as it enables them to fully harness the advantages of cloud computing.
Protect data in multi-tenant architectures To bring you the most value by unifying observability and security in one analytics and automation platform powered by AI, Dynatrace SaaS leverages a multitenancy architecture, enabling efficient and scalable data ingestion, querying, and processing on shared infrastructure.
DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. On average, organizations use 10 different tools to monitor applications, infrastructure, and user experiences across these environments.
As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Fortunately, CISOs can use security analytics to improve visibility of complex environments and enable proactive protection. What is security analytics? Why is security analytics important? Here’s how.
This necessitates a comprehensive platform that empowers enterprises to understand IT and software within the broader context of their business operations, giving them confidence that their software and IT infrastructure are reliable. AI-driven analytics transform data analysis, making it faster and easier to uncover insights and act.
For instance, in a Kubernetes environment, if an application fails, logs in context not only highlight the error alongside corresponding log entries but also provide correlated logs from surrounding services and infrastructure components. Keep in mind that Dynatrace Grail is schema-on-read and indexless, built with scaling in mind.
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.
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.
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.
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?
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.
CSPM solutions continuously monitor and improve the security posture of Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) environments. However, if you only use minimal cloud services, your cloud environment is static, or you rely on on-premises infrastructure, CSPM may not be worth buying yet.
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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.
However, cloud infrastructure has become increasingly complex. Further, the delivery infrastructure that makes this happen has also become 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.
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Sure, cloud infrastructure requires comprehensive performance visibility, as Dynatrace provides , but the services that leverage cloud infrastructures also require close attention. Cloud-based application architectures commonly leverage microservices. Extend infrastructure observability to WSO2 API Manager.
In this blog post, youll learn how Dynatrace OneAgent automatically identifies Journald and ingests structured logs into Dynatrace while enriching them with topology and infrastructure context. For forensic log analytics use cases, the Security Investigator app benefits from the scalability and analytics power of Dynatrace Grail.
For organizations running their own on-premises infrastructure, these costs can be prohibitive. Cloud service providers, such as Amazon Web Services (AWS) , can offer infrastructure with five-nines availability by deploying in multiple availability zones and replicating data between regions. What is always-on infrastructure?
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.
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. Open source solutions are also making tracing harder.
In serverless and microservices architectures, messaging systems are often used to build asynchronous service-to-service communication. We’ve introduced brand-new analytics capabilities by building on top of existing features for messaging systems. Finally, you can configure and activate them there. New to Dynatrace?
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.
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?
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.
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.
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. This can include internal services within an organizations infrastructure or external systems.
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.
For IT infrastructure managers and site reliability engineers, or SREs , logs provide a treasure trove of data. These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues.
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. These metrics are visualized using Lumen , a self-service dashboarding infrastructure. What is BPF?
Central engineering teams enable this operational model by reducing the cognitive burden on innovation teams through solutions related to securing, scaling and strengthening (resilience) the infrastructure. All these micro-services are currently operated in AWS cloud infrastructure.
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.
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.
To take full advantage of the scalability, flexibility, and resilience of cloud platforms, organizations need to build or rearchitect applications around a cloud-native architecture. So, what is cloud-native architecture, exactly? What is cloud-native architecture? Immutable infrastructure. Default to managed services.
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.
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.
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Increased adoption of Infrastructure as code (IaC). IaC, or software intelligence as code , codifies and manages IT infrastructure in software, rather than in hardware. Infrastructure as code is also known as software-defined infrastructure, or software intelligence as code.
Also, these modern, cloud-native architectures produce an immense volume, velocity, and variety of data. Some companies are still using different tools for application performance monitoring, infrastructure monitoring, and log monitoring. AI-powered answers and additional context for apps and infrastructure, at scale.
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).
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