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Collect metrics on energy consumption or derive them from existing signals. For example, reporting jobs can process monthly data without running exactly at the end of the month. Platform engineers can set defaults for development teams, such as the number of replicas a service should have or whether it scales automatically.
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. Each format has a different production process and different patterns of cash spend, called our Content Forecast. Need to catch up?
For busy site reliability engineers, ensuring system reliability, scalability, and overall health is an imperative that’s getting harder to achieve in ever-expanding, cloud-native, container-based environments. To get a more granular look into telemetry data, many analysts rely on custom metrics using Prometheus. What is Prometheus?
Back during Perform 2019, we introduced the next generation of the Dynatrace AI causation engine , also known as Davis. becomes the default causation engine and will replace the previous version as the default for all new environments. as the default AI engine. AI causation engine. All existing Davis 1.0
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.
This approach enhances key DORA metrics and enables early detection of failures in the release process, allowing SREs more time for innovation. This blog post explores the Reliability metric , which measures modern operational practices. It forms the cornerstone of chaos engineering experiments. Why reliability?
This lets you build your SLOs around the indicators that matter to you and your customers—critical metrics related to availability, failure rates, request response times, or select logs and business events. Hence, having a dedicated dashboard tile visualizing the key parameters of each SLO simplifies the process of evaluating them.
Dynatrace OpenPipeline is a new stream processing technology that ingests and contextualizes data from any source. For years, logs have been the dominant approach many observability vendors have taken to report business metrics on dashboards. Business process monitoring and optimization.
In response to this shift, platform engineering is growing in popularity. The practice of platform engineering has evolved alongside the increasing complexity of cloud environments. Platform engineers design and implement these platforms, as well as ensure their security, scalability, and reliability.
I spoke with Martin Spier, PicPay’s VP of Engineering, about the challenges PicPay experienced and the Kubernetes platform engineering strategy his team adopted in response. In addition, their logs-heavy approach to analysis made scaling processes complex and costly. “And these layers tend to be similar. .
To get a better idea of OpenTelemetry trends in 2025 and how to get the most out of it in your observability strategy, some of our Dynatrace open-source engineers and advocates picked out the innovations they find most interesting. Because its constantly evolving, staying up to date with the latest in OpenTelemetry is no small feat.
In this blog, I will be going through a step-by-step guide on how to automate SRE-driven performance engineering. This will enable deep monitoring of those Java,NET, Node, processes as well as your web servers. Dynatrace news. Keptn uses SLO definitions to automatically configure Dynatrace or Prometheus alerting rules.
Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data.
While increasing both the precision and the recall of our secrets detection engine, we felt the need to keep a close eye on speed. So it wasn’t a surprise to find that our engine had the same problem: more power, less speed. In a gearbox, if you want to increase torque, you need to decrease speed.
Ensuring smooth operations is no small feat, whether you’re in charge of application performance, IT infrastructure, or business processes. Chances are, youre a seasoned expert who visualizes meticulously identified key metrics across several sophisticated charts.
Platform engineering is on the rise. According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026.
Dynatrace has recently extended its Kubernetes operator by adding a new feature, the Prometheus OpenMetrics Ingest , which enables you to import Prometheus metrics in Dynatrace and build SLO and anomaly detection dashboards with Prometheus data. Here we’ll explore how to collect Prometheus metrics and what you can achieve with them.
But because of the complexity involved in executing and analyzing test results of dynamic systems, performance engineering is difficult to scale — especially with lean staff or resources. How can organizations address this process bottleneck and run more tests in less time? Current challenges with performance testing.
DevOps and platform engineering are essential disciplines that provide immense value in the realm of cloud-native technology and software delivery. Observability of applications and infrastructure serves as a critical foundation for DevOps and platform engineering, offering a comprehensive view into system performance and behavior.
As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. The pair showed how to track factors including developer velocity, platform adoption, DevOps research and assessment metrics, security, and operational costs.
We are proud to s hare Dynatrace has been named the winner in the “ Best Overall AI-based Analytics Company ” category, recognized for our innovation and the business-driving impact of our AI engine, Davis. . The post Dynatrace wins AI Breakthrough Award for Davis AI engine appeared first on Dynatrace blog.
There’s no lack of metrics, logs, traces, or events when monitoring your Kubernetes (K8s) workloads. I was pulled into that troubleshooting call and started taking notes and screenshots so I can share how easy it is to troubleshoot the Kubernetes workload with our engineers and you – our readers – on this blog post. Dynatrace news.
Five of the most common include cluster instability, resource and cost management, security, observability, and stress on engineering teams. Engineering teams are overwhelmed with stuff to do.” ” First, Akamas collects metrics, then recommends configuration improvements and applies these recommendations.
This is achieved, in part, by establishing actionable statistical accuracy —not necessarily precise accuracy —through practical levels of metric sampling, aggregation, and extrapolation. Introducing metric extraction from business events Beginning with Dynatrace SaaS version 1.257, you can extract metrics from ingested business events.
Micrometer is used for instrumenting both out-of-the-box and custom metrics from Spring Boot applications. Davis topology-aware anomaly detection and alerting for your Micrometer metrics. Topology-related custom metrics for seamless reports and alerts. Micrometer uses a registry to export metrics to monitoring systems.
Unrealized optimization potential of business processes due to monitoring gaps Imagine a retail company facing gaps in its business process monitoring due to disparate data sources. Due to separated systems that handle different parts of the process, the view of the process is fragmented.
A Dynatrace API token with the following permissions: Ingest OpenTelemetry traces ( openTelemetryTrace.ingest ) Ingest metrics ( metrics.ingest ) Ingest logs ( logs.ingest ) To set up the token, see Dynatrace API – Tokens and authentication in Dynatrace documentation. If you don’t have one, you can use a trial account.
By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. Let’s dive in! What is late-arriving data? Some techniques we used were: 1.
When it comes to platform engineering, not only does observability play a vital role in the success of organizations’ transformation journeys—it’s key to successful platform engineering initiatives. The various presenters in this session aligned platform engineering use cases with the software development lifecycle.
OpenTelemetry metrics are useful for augmenting the fully automatic observability that can be achieved with Dynatrace OneAgent. OpenTelemetry metrics add domain specific data such as business KPIs and license relevant consumption details. Enterprise-grade observability for custom OpenTelemetry metrics from AWS. Dynatrace news.
However, due to the fact that they boil down selected indicators to single values and track error budget levels, they also offer a suitable way to monitor optimization processes while aligning on single values to meet overall goals. By recognizing the insights provided, you can optimize processes and improve overall efficiency.
Site reliability engineering (SRE) has become increasingly important to organizations looking to keep up with the rapid pace of digital transformation. Effective site reliability engineering requires enterprise-wide transformation Without a unified understanding of SRE practices, organizational silos can quickly form between departments.
Fluentd is an open-source data collector that unifies log collection, processing, and consumption. It collects, processes, and outputs log files to and from a wide variety of technologies. Processing plugins parse (normalize), filter, enrich (tagging), format, and buffer log streams. Adding the Dynatrace plug-in is easy.
This process involves: Identifying Stakeholders: Determine who is impacted by the issue and whose input is crucial for a successful resolution. In this context, were focused on developing systems that ensure successful title launches, build trust between content creators and our brand, and reduce engineering operational overhead.
Every image you hover over isnt just a visual placeholder; its a critical data point that fuels our sophisticated personalization engine. It requires a state-of-the-art system that can track and process these impressions while maintaining a detailed history of each profiles exposure.
by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.
The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? Option 1: Log Processing Log processing offers a straightforward solution for monitoring and analyzing title launches.
Imagine a ML practitioner on the Netflix Content ML team, sourcing features from hundreds of columns in our data warehouse, and creating a multitude of models against a growing suite of metrics. Subsequent versions of the model will result from experimenting with hyper parameters, tweaking feature engineering, or conducting feature diets.
This process, known as auto-adaptive thresholding, eliminates the need to define a static threshold upfront. A key element in platform engineering is the establishment of fast feedback cycles regarding the quality and security measures of new software releases. However, other data formats, like logs, can also be employed.
The configuration also includes an optional span metrics connector, which generates Request, Error, and Duration (R.E.D.) metrics from span data. The configuration also includes an optional span metrics connector, which generates Request, Error, and Duration (R.E.D.) metrics from span data.
Open-source metric sources automatically map to our Smartscape model for AI analytics. With this announcement, Dynatrace brings the value of its AI engine, the scale, security, and automation of Dynatrace OneAgent and the scale of our platform (which can handle 50,000 hosts) to open source technologies so that you get the best of both worlds.
Proper setup involves creating a configuration process that accounts for hostname changes, which could prevent nodes from rejoining the cluster. Message load balancing guarantees that messages are processed evenly across different queues and nodes within the RabbitMQ system. Erlang is the backbone of RabbitMQ clustering.
By leveraging Dynatrace observability on Red Hat OpenShift running on Linux, you can accelerate modernization to hybrid cloud and increase operational efficiencies with greater visibility across the full stack from hardware through application processes. Learn more about the new Kubernetes Experience for Platform Engineering.
While other methods typically rely on mere correlation and historical data analysis, weve further enhanced our capabilities by implementing causational analysis, which leverages contextual information automatically gathered during data ingestion and processing in addition to historical data analysis.
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