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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 first 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. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft.
Organizations are increasingly embracing cloud- and AI-native strategies, requiring a more automated and intelligent approach to their observability and development practices. Thats why Dynatrace will make its AI-powered, unified observability platform generally available on Google Cloud for all customers later this year.
Leverage AI for proactive protection: AI and contextual analytics are game changers, automating the detection, prevention, and response to threats in real time. In dynamic and distributed cloud environments, the process of identifying incidents and understanding the material impact is beyond human ability to manage efficiently.
In this blog post, we will see how Dynatrace harnesses the power of observability and analytics to tailor a new experience to easily extend to the left, allowing developers to solve issues faster, build more efficient software, and ultimately improve developer experience!
As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Two-thirds say vulnerability management is becoming harder because of complex supply chain and cloud ecosystems. What is security analytics? Why is security analytics important? Here’s how.
Enterprises are turning to Dynatrace for its unified observability approach for cloud-native, on-premises, and hybrid resources. The Clouds app provides a view of all available cloud-native services. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
Cloud-native observability is a prerequisite for companies that need to meet these expectations. With PurePath ® distributed tracing and analysis technology at the code level, Dynatrace already provides the deepest possible insights into every transaction. Dynatrace news. How to get started.
DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. Moreover, teams are constantly dealing with continuously evolving cyberthreats to data both on premises and in the cloud.
Cloud environments have become ever more complex, with an increasingly interconnected set of services. To tame this complexity and deliver differentiated digital experiences, IT, development, security, and business teams need automated workflows throughout these cloud ecosystems.
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.
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. Organizations need a more proactive approach to log management to tame this proliferation of cloud 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. Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes.
This is where observability analytics can help. What is observability analytics? Observability analytics enables users to gain new insights into traditional telemetry data such as logs, metrics, and traces by allowing users to dynamically query any data captured and to deliver actionable insights. Put simply, context is king.
Exploratory analytics with collaborative analytics capabilities can be a lifeline for CloudOps, ITOps, site reliability engineering, and other teams struggling to access, analyze, and conquer the never-ending deluge of big data. These analytics can help teams understand the stories hidden within the data and share valuable insights.
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.
New cloud-native technologies make observability more important than ever…. Technical complexity has shifted from the actual code to the interdependencies between services. Dynatrace PurePath 4 extends automatic distributed tracing to OpenTelemetry and the latest cloud-native technologies.
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.
Much of the software developed today is cloud native. However, cloud infrastructure has become increasingly 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. Real-time anomaly detection.
Grail – the foundation of exploratory analytics Grail can already store and process log and business events. Introducing Metrics on Grail Despite their many advantages, modern cloud-native architectures can result in scalability and fragmentation challenges. You no longer need to split, distribute, or pre-aggregate your data.
However, today’s highly dynamic cloud-native environments with containers, microservices, and platforms like Kubernetes, make it more challenging to ensure that applications are working as expected and that customers are adopting new features and generating conversions. The easiest way to lose mobile app users is through crashes.
To stay competitive in an increasingly digital landscape, organizations seek easier access to business analytics data from IT to make better business decisions faster. Five constraints that limit insights from business analytics data. Digital businesses rely on real-time business analytics data to make agile decisions.
In a digital-first world, site reliability engineers and IT data analysts face numerous challenges with data quality and reliability in their quest for cloud control. Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices. Discovery using global search.
A traditional log-based SIEM approach to security analytics may have served organizations well in simpler on-premises environments. With the rising complexity of cloud-native environments, manual investigation and response are too slow and inaccurate. What can you do with Dynatrace Security Analytics?
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?
In cloud-native environments, there can also be dozens of additional services and functions all generating data from user-driven events. This is critical to ensure high performance, security, and a positive user experience for cloud-native applications and services. Comparing log monitoring, log analytics, and log management.
The methodology and algorithms were designed by Dynatrace with guidance from the Sustainable Digital Infrastructure Alliance (SDIA), expanding on formulas from the open source project Cloud Carbon Footprint. Some use cases benefit from dashboards or ad-hoc analytics, complementing the insights from Carbon Impact.
Leveraging cloud-native technologies like Kubernetes or Red Hat OpenShift in multicloud ecosystems across Amazon Web Services (AWS) , Microsoft Azure, and Google Cloud Platform (GCP) for faster digital transformation introduces a whole host of challenges. Dynatrace news. Connecting data siloes requires daunting integration endeavors.
Cloud-native technologies, including Kubernetes and OpenShift, help organizations accelerate innovation. Open source has also become a fundamental building block of the entire cloud-native stack. Why cloud-native applications, Kubernetes, and open source require a radically different approach to application security.
Grail data lakehouse delivers massively parallel processing for answers at scale Modern cloud-native computing is constantly upping the ante on data volume, variety, and velocity. Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. Kubernetes makes spans longer,” Ortner explains.
With unified observability and security, organizations can protect their data and avoid tool sprawl with a single platform that delivers AI-driven analytics and intelligent automation. For performance, for security analytics, you have to have the data in context. An overview of the Dynatrace unified observability and security platform.
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.
Cloud application security is becoming more of a critical issue as cloud-based applications gain popularity. The cloud allows a modular approach to building applications, enabling development and operations teams to create and deploy feature-rich apps very quickly. What is cloud application security?
Today’s organizations face increasing pressure to keep their cloud-based applications performing and secure. Cloud application security remains challenging because organizations lack end-to-end visibility into cloud architecture. In many cases, organizations don’t discover vulnerabilities until after they have been exploited.
The Dynatrace platform now enables comprehensive data exploration and interactive analytics across data sets (trace, logs, events, and metrics)empowering you to solve complex use cases, handle any observability scenario, and gain unprecedented visibility into your systems.
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. SSRF can lead to unauthorized access to sensitive data, such as cloud metadata, internal databases, and other protected resources.
As more organizations invest in a multicloud strategy, improving cloud operations and observability for increased resilience becomes critical to keep up with the accelerating pace of digital transformation. Additionally, PurePath provides distributed tracing with code-level detail at scale with contextual data. ski explains.
I am excited to announce that ISG analysts positioned Dynatrace highest for portfolio attractiveness and competitive strength in the Cloud-Native Observability Solutions Quadrant. . Dynatrace’s deep understanding of enterprises’ cloud-native needs stand s out .
With segments, you can isolate particular OpenPipeline log sources, resource entities, cloud regions, or even certain buckets your developers use. Dont worry; with the power of the DQL commands like concat you can easily address this for your users with a single line of code.
How can you reduce the carbon footprint of your hybrid cloud? Evaluating these on three levels—data center, host, and application architecture (plus code)—is helpful. energy-efficient data centers—cloud providers—achieve values closer to 1.2. Is the solution to just move all workloads to the cloud? A PUE of 1.0
Most organizations have hundreds of business processes across these four categories, supported by IT systems through a mix of on-premises, cloud, and SaaS solutions. But even the best BPM solutions lack the IT context to support actionable process analytics; this is the opportunity for observability platforms.
For example, I’ll get asked “We are using Adobe Analytics/Omniture SiteCatalyst , would we retire this when we use Dynatrace?” The only exception to this would be if you have Adobe Analytics for the sole purpose of understanding geographically where your users are coming from. Cool parts of integrating Adobe Analytics and Dynatrace.
Energy efficiency is a key reason why organizations are migrating workloads from energy-intensive on-premises environments to more efficient cloud platforms. But while moving workloads to the cloud brings overall carbon emissions down, the cloud computing carbon footprint itself is growing. Certainly, this is true for us.
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