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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.
Metadata enrichment improves collaboration and increases analytic value. The Dynatrace® platform continues to increase the value of your data — broadening and simplifying real-time access, enriching context, and delivering insightful, AI-augmented analytics. Our Business Analytics solution is a prominent beneficiary of this commitment.
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.
Key benefits of Runtime Vulnerability Analytics Managing application vulnerabilities is no small feat. Unified vulnerabilities view The new Dynatrace platform consolidates third-party and code-level vulnerabilities into a single, intuitive view. By focusing on actionable intelligence, you can reduce noise and focus on whats important.
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. Advanced analytics are not limited to use-case-specific apps.
Code changes are often required to refine observability data. This results in site reliability engineers nudging development teams to add resource attributes, endpoints, and tokens to their source code. The missed SLO can be analytically explored and improved using Davis insights on an out-of-the-box Kubernetes workload overview.
But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise. And operations teams need to forecast cloud infrastructure and compute resource requirements, then automatically provision resources to optimize digital customer experiences.
By following key log analytics and log management best practices, teams can get more business value from their data. Challenges driving the need for log analytics and log management best practices As organizations undergo digital transformation and adopt more cloud computing techniques, data volume is proliferating.
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.
On average, organizations use 10 different tools to monitor applications, infrastructure, and user experiences across these environments. Clearly, continuing to depend on siloed systems, disjointed monitoring tools, and manual analytics is no longer sustainable.
Take your monitoring, data exploration, and storytelling to the next level with outstanding data visualization All your applications and underlying infrastructure produce vast volumes of data that you need to monitor or analyze for insights. Infrastructure health: A honeycomb chart is often used to visualize infrastructure health.
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.
What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.
What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.
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.
Exploding volumes of business data promise great potential; real-time business insights and exploratory analytics can support agile investment decisions and automation driven by a shared view of measurable business goals. Traditional observability solutions don’t capture or analyze application payloads. What’s next?
Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices. The next challenge is harnessing additional AI techniques to make exploratory data analytics even easier. Notebooks] is purposely built to focus on data analytics,” Zahrer said. “We
In this blog post, we’ll use Dynatrace Security Analytics to go threat hunting, bringing together logs, traces, metrics, and, crucially, threat alerts. Instead, we want to focus on detecting and stopping attacks before they happen: In your applications, in context, at the exact line of code that is vulnerable and in use.
Despite the deep IT observability you may have deployed, you still cant infer process health from system status; problems occureven when the underlying infrastructure is healthy. But even the best BPM solutions lack the IT context to support actionable process analytics; this is the opportunity for observability platforms.
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.
More recently, teams have begun to apply DevOps best practices to infrastructure automation, giving developers a more active role with GitOps as an operational framework. Key components of GitOps are declarative infrastructure as code, orchestration, and observability. Dynatrace enables software intelligence as code.
To make this possible, the application code should be instrumented with telemetry data for deep insights, including: Metrics to find out how the behavior of a system has changed over time. Dynatrace AWS monitoring gives you an overview of the resources that are used in your AWS infrastructure along with their historical usage.
As an application owner, product manager, or marketer, however, you might use analytics tools like Adobe Analytics to understand user behavior, user segmentation, and strategic business metrics such as revenue, orders, and conversion goals. The reporting of values must happen in the source code of your mobile app via the SDK API.
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.
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.
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. Operations.
From business operations to personal communication, the reliance on software and cloud infrastructure is only increasing. Software bugs Software bugs and bad code releases are common culprits behind tech outages. These issues can arise from errors in the code, insufficient testing, or unforeseen interactions among software components.
Indeed, according to one survey, DevOps practices have led to 60% of developers releasing code twice as quickly. But increased speed creates a tradeoff: According to another study, nearly half of organizations consciously deploy vulnerable code because of time pressure. Increased adoption of Infrastructure as code (IaC).
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. None of this complexity is exposed to application and infrastructure teams.
Endpoints include on-premises servers, Kubernetes infrastructure, cloud-hosted infrastructure and services, and open-source technologies. Observability across the full technology stack gives teams comprehensive, real-time insight into the behavior, performance, and health of applications and their underlying infrastructure.
Dynatrace with Red Hat OpenShift monitoring stands out for the following reasons: With infrastructure health monitoring and optimization, you can assess the status of your infrastructure at a glance to understand resource consumption and thus optimize resource allocation for cost efficiency.
In these modern environments, every hardware, software, and cloud infrastructure component and every container, open-source tool, and microservice generates records of every activity. Metrics can originate from a variety of sources, including infrastructure, hosts, services, cloud platforms, and external sources.
Optimize the IT infrastructure supporting risk management processes and controls for maximum performance and resilience. The IT infrastructure, services, and applications that enable processes for risk management must perform optimally. Once teams solidify infrastructure and application performance, security is the subsequent priority.
The development of internal platform teams has taken off in the last three years, primarily in response to the challenges inherent in scaling modern, containerized IT infrastructures. The ability to effectively manage multi-cluster infrastructure is critical to consistent and scalable service delivery.
In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. From data lakehouse to an analytics platform Traditionally, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs.
A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. Here are some examples: IT infrastructure and operations. To tame this complexity and optimize cloud operations, teams across the organization need to manage and explore their data effectively.
A central element of platform engineering teams is a robust Internal Developer Platform (IDP), which encompasses a set of tools, services, and infrastructure that enables developers to build, test, and deploy software applications. Code : The branch for the new feature in a GitHub repository is merged into the main branch.
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. High-performance analytics—no indexing required.
The result is a framework that offers a single source of truth and enables companies to make the most of advanced analytics capabilities simultaneously. The performance of these queries needs to be at a level where they can support ad-hoc analytics use cases. Support diverse analytics workloads. Massively parallel processing.
Putting logs into context with metrics, traces, and the broader application topology enables and improves how companies manage their cloud architectures, platforms and infrastructure, optimizing applications and remediate incidents in a highly efficient way. AI-powered answers and additional context for apps and infrastructure, at scale.
Use buckets for any use case in a secure way When using Log Management and Analytics or Business Analytics with Grail, you can create custom buckets with specified data-retention periods. Infrastructure teams may need to work with host logs from recent months or quarters.
An easy, though imprecise, way of thinking about Netflix infrastructure is that everything that happens before you press Play on your remote control (e.g., Various software systems are needed to design, build, and operate this CDN infrastructure, and a significant number of them are written in Python. are you logged in?
Dynatrace has been building automated application instrumentation—without the need to modify source code—for over 15 years already. Driving the implementation of higher-level APIs—also called “typed spans”—to simplify the implementation of semantically strong tracing code. What Dynatrace will contribute.
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