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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.
Break data silos and add context for faster, more strategic decisions : Unifying metrics, logs, traces, and user behavior within a single platform enables real-time decisions rooted in full context, not guesswork. Traditional network-based security approaches are evolving.
You can now: Kickstart your creation journey using ready-made dashboards Accelerate your data exploration with seamless integration between apps Start from scratch with the new Explore interface Search for known metrics from anywhere Let’s look at each of these paths through an end-to-end use case focused on Kubernetes monitoring.
To continue down the carbon reduction path, IT leaders must drive carbon optimization initiatives into the hands of IT operations teams, arming them with the tools needed to support analytics and optimization. We implemented a wasted energy metric in the app to enhance practitioner actionability. Public network traffic uses 1.0
By Alok Tiagi , Hariharan Ananthakrishnan , Ivan Porto Carrero and Keerti Lakshminarayan Netflix has developed a network observability sidecar called Flow Exporter that uses eBPF tracepoints to capture TCP flows at near real time. Without having network visibility, it’s difficult to improve our reliability, security and capacity posture.
Exploratory analytics now cover more bespoke scenarios, allowing you to access any element of test results stored in the Dynatrace Grail data lakehouse. It now fully supports not only Network Availability Monitors but also HTTP synthetic monitors. The new Dynatrace Synthetic app allows you to analyze these results.
Chances are, youre a seasoned expert who visualizes meticulously identified key metrics across several sophisticated charts. This is where Davis AI for exploratory analytics can make all the difference. Your trained eye can interpret them at a glance, a skill that sets you apart.
We introduced Digital Business Analytics in part one as a way for our customers to tie business metrics to application performance and user experience, delivering unified insights into how these metrics influence business milestones and KPIs. A sample Digital Business Analytics dashboard. Dynatrace news.
As businesses increasingly embrace these technologies, integrating IoT metrics with advanced observability solutions like Dynatrace becomes essential to gaining additional business value through end-to-end observability. Both methods allow you to ingest and process raw data and metrics.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. A log is a detailed, timestamped record of an event generated by an operating system, computing environment, application, server, or network device.
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. Start by asking yourself what’s there, whether it’s logs, metrics, or traces.
Quick and easy network infrastructure monitoring. Begin network monitoring by simply deploying an extension with just a few clicks. The topology model for network devices covers simple to complex use cases from visualizing the interfaces of a router to mapping an F5 Big-IP LTM load balancer. Start monitoring in minutes.
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.
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. The number and variety of applications, network devices, serverless functions, and ephemeral containers grows continuously.
The result is that IT teams must often contend with metrics, logs, and traces that aren’t relevant to organizational business objectives—their challenge is to translate such unstructured data into actionable business insights. Dynatrace extends its unique topology-based analytics and AIOps approach.
Even if infrastructure metrics aren’t your thing, you’re welcome to join us on this creative journey simply swap out the suggested metrics for ones that interest you. For our example dashboard, we’ll only focus on some selected key infrastructure metrics. Click on Select metric. Change it now to sum.
Clearly, continuing to depend on siloed systems, disjointed monitoring tools, and manual analytics is no longer sustainable. It also helps to have access to OpenTelemetry, a collection of tools for examining applications that export metrics, logs, and traces for analysis.
Mobile applications (apps) are an increasingly important channel for reaching customers, but the distributed nature of mobile app platforms and delivery networks can cause performance problems that leave users frustrated, or worse, turning to competitors. Some of the most important KPIs are listed below. Performance optimization.
In this blog post, we’ll use Dynatrace Security Analytics to go threat hunting, bringing together logs, traces, metrics, and, crucially, threat alerts. Attack tactics describe why an attacker performs an action, for example, to get that first foothold into your network.
Native support for Syslog messages Syslog messages are generated by default in Linux and Unix operating systems, security devices, network devices, and applications such as web servers and databases. Native support for syslog messages extends our infrastructure log support to all Linux/Unix systems and network devices.
That is, relying on metrics, logs, and traces to understand what software is doing and where it’s running into snags. In addition to tracing, observability also defines two other key concepts, metrics and logs. When software runs in a monolithic stack on on-site servers, observability is manageable enough. What is OpenTelemetry?
Metrics, logs , and traces make up three vital prongs of modern observability. Together with metrics, three sources of data help IT pros identify the presence and causes of performance problems, user experience issues, and potential security threats. Comparing log monitoring, log analytics, and log management.
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. And because Dynatrace can consume CloudWatch metrics, almost all your AWS usage information is available to you within Dynatrace. Conclusion.
As an example, many retailers already leverage containerized workloads in-store to enhance customer experiences using video analytics or streamline inventory management using RFID tracking for improved security. Moreover, edge environments can be highly dynamic, with devices frequently joining and leaving the network.
By contextualizing data, OpenPipeline enhances the Dynatrace platform’s ability to offer AI-driven insights, analytics, and automation across observability, security, software lifecycle, and business domains. Seamless integration with AWS Data Firehose: address high-impact issues quickly through real-time, high-frequency log analytics.
AWS offers a broad set of global, cloud-based services including computing, storage, networking, Internet of Things (IoT), and many others. To reduce your CloudWatch costs and throttling, you can now select from additional services and metrics to monitor. Get up to 300 new AWS metrics out of the box. Dynatrace news.
Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. Its architecture supports stream transformations, joins, and filtering, making it a powerful tool for real-time analytics. Apache Kafka, designed for distributed event streaming, maintains low latency at scale.
Real-time streaming needs real-time analytics As enterprises move their workloads to cloud service providers like Amazon Web Services, the complexity of observing their workloads increases. Log data—the most verbose form of observability data, complementing other standardized signals like metrics and traces—is especially critical.
I never thought I’d write an article in defence of DOMContentLoaded , but here it is… For many, many years now, performance engineers have been making a concerted effort to move away from technical metrics such as Load , and toward more user-facing, UX metrics such as Speed Index or Largest Contentful Paint. Or are they…?
AWS offers a broad set of global, cloud-based services including computing, storage, networking, Internet of Things (IoT), and many others. To reduce your CloudWatch costs and throttling, you can now select from additional services and metrics to monitor. Get up to 300 new AWS metrics out of the box. Dynatrace news.
This new service enhances the user visibility of network details with direct delivery of Flow Logs for Transit Gateway to your desired endpoint via Amazon Simple Storage Service (S3) bucket or Amazon CloudWatch Logs. AWS Transit Gateway is a service offering from Amazon Web Services that connects network resources via a centralized hub.
Fast, consistent application delivery creates a positive user experience that can ultimately drive customer loyalty and improve business metrics like conversion rate and user retention. With DEM solutions, organizations can operate over on-premise network infrastructure or private or public cloud SaaS or IaaS offerings.
Open Connect Open Connect is Netflix’s content delivery network (CDN). video streaming) takes place in the Open Connect network. The network devices that underlie a large portion of the CDN are mostly managed by Python applications. CORE The CORE team uses Python in our alerting and statistical analytical work.
Dynatrace’s ability to ingest metrics from all 95 AWS services will be available within the next 60 days. The latest batch of services cover databases, networks, machine learning and computing. AWS IoT Analytics. AWS SDK Metrics for Enterprise Support. Achieve full observability of all AWS services. AWS Elastic Beanstalk.
Many companies rely on Citrix as a critical component of their infrastructure that demands thorough observability and integrated analytics across the entire application landscape. Automated AI-powered analytics are necessary to match the scale of monitoring these enterprises require.
For Carbon Impact, these business events come from an automation workflow that translates host utilization metrics into energy consumption in watt hours (Wh) and into greenhouse gas emissions in carbon dioxide equivalent (CO2e). Some use cases benefit from dashboards or ad-hoc analytics, complementing the insights from Carbon Impact.
They collect data from multiple sources through real user monitoring , synthetic monitoring, network monitoring, and application performance monitoring systems. Align business and development teams’ input on what user experience metrics to measure to understand users’ most critical digital experience aspects.
A full-stack observability solution uses telemetry data such as logs, metrics, and traces to give IT teams insight into application, infrastructure, and UX performance. Not just infrastructure connections, but the relationships and dependencies between containers, microservices , and code at all network layers.
Is it the WSO2-AM gateway itself, a networking issue, a sudden increase in demand, or something else entirely? Looking at the key metrics of the deployment does not reveal anything out of the ordinary. This short journey through collected metrics prevented a serious issue and a long bug hunt. But where does the fault lie?
The F5 BIG-IP Local Traffic Manager (LTM) is an application delivery controller (ADC) that ensures the availability, security, and optimal performance of network traffic flows. By analyzing metric anomalies between hosts and load balancers, Davis AI can quickly resolve problems, thus expediting the process.
As organizations adopt more cloud-native technologies, observability data—telemetry from applications and infrastructure, including logs, metrics, and traces—and security data are converging. But with a platform approach to log analytics based on observability at a cloud-native scale, organizations can accomplish much more. Incomplete.
To overcome these complex issues, teams must quickly find root causes among numerous alerts and metrics. Based on the topology model, detected dependencies, and thousands of events and metrics, Davis AI can pinpoint the origin of an issue. For the most granular metrics and network insights, OneAgent is the optimal choice.
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases.
Networking. Large-scale, multicloud deployments can introduce challenges related to network visibility and interoperability. Traditional ways of operating networks using static IPs and ports simply don’t work in dynamic Kubernetes environments. AI-powered analytics. Acceleration of innovation.
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