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As cloud complexity increases and security concerns mount, organizations need log analytics to discover and investigate issues and gain critical business intelligence. But exploring the breadth of log analytics scenarios with most log vendors often results in unexpectedly high monthly log bills and aggressive year-over-year costs.
This article is the second in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. With ASR, and other new and enhanced technologies we introduce, rigorous analytics and measurement are essential to their success.
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.
As a strategic ISV partner, Dynatrace and Azure are continuously and collaboratively innovating, focusing on a strong build-with motion dedicated to bringing innovative solutions to market to deliver better customer value. Read on to learn more about how Dynatrace and Microsoft leverage AI to transform modern cloud strategies.
At the time when I was building the most innovative observability company, security seemed too distant. Boost your operational resilience: Combining availability and security is now essential. UMELT are kept cost-effectively in a massive parallel processing data lakehouse, enabling contextual analytics at petabyte scale, fast.
Dynatrace, available as an Azure-native service , has a longstanding partnership with Microsoft, deeply rooted in a strong “build with” approach to deliver seamless user experience. This enables Dynatrace customers to achieve faster time-to-value and accelerate innovation.
Dynatrace partners are a cornerstone of our success, driving innovation and enabling customer growth. Were thrilled to announce the winners of this competition, who demonstrated exceptional innovation and creativity in their solutions.
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.
We’re excited to announce several log management innovations, including native support for Syslog messages, seamless integration with AWS Firehose, an agentless approach using Kubernetes Platform Monitoring solution with Fluent Bit, a new out-of-the-box ingest dashboard, and OpenPipeline ingest improvements.
The Clouds app provides a view of all available cloud-native services. Logs in context, along with other details, are instantly available after selecting a resource. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
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. Now, that same full-spectrum value is available at the massive scale of the Dynatrace Grail data lakehouse.
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.
Information related to user experience, transaction parameters, and business process parameters has been an unretrieved treasure, now accessible through new and unique AI-powered contextual analytics in Dynatrace. Executives drive business growth through strategic decisions, relying on data analytics for crucial insights.
When we launched the new Dynatrace experience, we introduced major updates to the platform, including Grail ™, our innovative data lakehouse unifying observability, security, and business data, and Dynatrace Query Language ( DQL ) for accessing and exploring unified data.
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 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. Limited data availability constrains value creation. Even in cases where all data is available, new challenges can arise. Create filters.
Sometimes, introducing new IT solutions is delayed or canceled because a single business unit can’t manage the operating costs alone, and per-department cost insights that could facilitate cost sharing aren’t available. In scenarios like these, automated and precise cost allocation can make a huge difference.
The application consists of several microservices that are available as pod-backed services. This gives us unified analytics views of node resources together with pod-level metrics such as container CPU throttling by node, which makes problem correlation much easier to analyze. The following example drives the point home.
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.
Business analytics is a growing science that’s rising to meet the demands of data-driven decision making within enterprises. But what is business analytics exactly, and how can you feed it with reliable data that ties IT metrics to business outcomes? What is business analytics? Why business analytics matter.
At Dynatrace, we’ve been exploring the many ways of using GPTs to accelerate our innovation on behalf of our customers and the productivity of our teams. ChatGPT and generative AI: A new world of innovation Software development and delivery are key areas where GPT technology such as ChatGPT shows potential.
With extended contextual analytics and AIOps for open observability, Dynatrace now provides you with deep insights into every entity in your IT landscape, enabling you to seamlessly integrate metrics, logs, and traces—the three pillars of observability. Dynatrace extends its unique topology-based analytics and AIOps approach.
The end goal, of course, is to optimize the availability of organizations’ software. Dynatrace is widely recognized for its AI capabilities’ ability to predict and prevent issues, and automatically identify root causes, maximizing availability. Note that the work doesn’t get reduced. Want to learn more about all nine use cases?
In today’s data-driven world, businesses across various industry verticals increasingly leverage the Internet of Things (IoT) to drive efficiency and innovation. This information is essential for later advanced analytics and aircraft tracking. Additionally, how can we determine the distance of the aircraft to the destination airport?
Dynatrace full stack observability for Red Hat OpenShift Dynatrace enhances software quality and operational efficiency, which drives innovation by unifying application, operation, and platform engineering teams on a single platform. Dynatrace observability is available for Red Hat OpenShift on IBM Power.
A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. Security Analytics and automation deal with unknown-unknowns With Security Analytics, analysts can explore the unknown-unknowns, facilitating queries manually in an ad hoc way, or continuously using automation.
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. Clair determined what log data was available to her. To begin, St.
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. Data supports this need for organizations to flex and modernize.
Although most organizations invest in innovative mobile app development, not many allocate enough resources toward delivering and measuring the high-quality user experiences customers expect. Mobile analytics can help organizations optimize their mobile application performance, earning customer accolades and increasing revenue in the process.
It should be open by design to accelerate innovation, enable powerful integration with other tools, and purposefully unify data and analytics. Enter Grail-powered data and analytics. Grail makes converging real-time, historical, and predictive analytics possible on a single platform.
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….
I also have the privilege of being “customer zero” for our platform, which enables me to continually discover where Dynatrace can deliver on more use cases to drive my team’s productivity and innovation. Unlike anything before, contextual analytics in Dynatrace provides answers to any question at any time, instantaneously.
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.
They handle complex infrastructure, maintain service availability, and respond swiftly to incidents. But when these teams work in largely manual ways, they don’t have time for innovation and strategic projects that might deliver greater value. Predictive analytics can anticipate potential failures and security breaches.
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.
This breakout session will discuss the deployment models available for cloud implementations and how Dynatrace helps in iterating faster deployments. In the past, monolith architectures could only be implemented with big bang deployments which result in a slow pace of innovation and significant downtime. Different deployment models.
This year, they’ve been asked to do more with less, innovate faster, and tame the ever-increasing complexities of modern cloud environments. However, AI-powered analytics of the observability data from cloud environments will help organizations tackle expanding emissions and mature their FinOps and sustainability practices.
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. IT teams can resort to playing defense, fighting daily fires rather than focusing on more important tasks, like innovation.
Dynatrace full stack Red Hat OpenShift observability Dynatrace unifies platform engineering and application teams on a single platform, enhancing software quality and operational efficiency to drive innovation. Having all data in context tremendously simplifies analytics and problem detection.
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. Manual troubleshooting is painful, hurts the business, and slows down innovation. Leverage log analytics for additional context.
AWS Lambda is the fastest growing technology for serverless workloads and helps developers innovate faster. All improvements are available with OneAgent version 1.217. Dynatrace news. It removes the burden of managing underlying infrastructure and is broadly adopted for cloud-native application environments.
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 ensures that all logs are properly enriched with metadata to enable context-rich analytics on the platform.
The path to achieving unprecedented productivity and software innovation through ChatGPT and other generative AI – blog Paired with causal AI, organizations can increase the impact and safer use of ChatGPT and other generative AI technologies. So, what is artificial intelligence? What is predictive AI? What is AIOps?
Azure Native Dynatrace Service allows easy access to new Dynatrace platform innovations Dynatrace has long offered deep integration into Azure and Azure Marketplace with its Azure Native Dynatrace Service, developed in collaboration with Microsoft. Notebooks offers advanced Azure observability analytics with DQL.
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