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Dynatrace Business Flow simplifies business process observability, connecting top-level process KPIs with detailed flow analytics. The app tracks the progress of every process instance, reporting individual and aggregated process performance, throughput, exceptions, and business outcomes.
A good Kubernetes SLO strategy helps teams manage and make containerized workloads more efficient. 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.
I realized that our platforms unique ability to contextualize security events, metrics, logs, traces, and user behavior could revolutionize the security domain by converging observability and security. Carefully planning and integrating new processes and tools is critical to ensuring compliance without disrupting daily operations.
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.
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As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. Here are five strategies executives can pursue to reduce tool sprawl, lower costs, and increase operational efficiency. No delays and overhead of reindexing and rehydration.
Imagine you’re using a lot of OpenTelemetry and Prometheus metrics on a crucial platform. A histogram is a specific type of metric that allows users to understand the distribution of data points over a period of time. You’re gathering a lot of data, but you can’t make sense of it. What are histograms, and why use them?
We kick off with a few topics focused on how were empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the company. DJ acts as a central store where metric definitions can live and evolve. As an example, imagine an analyst wanting to create a Total Streaming Hours metric.
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Second, it enables efficient and effective correlation and comparison of data between various sources. Finally, it empowers automated systems to process and analyze OpenTelemetry data, without requiring adaptations for every framework. At the same time, having aligned telemetry data is crucial for adopting OpenTelemetry at scale.
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Adopting AI to enhance efficiency and boost productivity is critical in a time of exploding data, cloud complexities, and disparate technologies. The Grail™ data lakehouse provides fast, auto-indexed, schema-on-read storage with massively parallel processing (MPP) to deliver immediate, contextualized answers from all data at scale.
My goal was to provide IT teams with insights to optimize customer experience by collaborating with business teams, using both business KPIs and IT metrics. Consolidate real-user monitoring, synthetic monitoring, session replay, observability, and business process analytics tools into a unified platform.
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The emerging concepts of working with DevOps metrics and DevOps KPIs have really come a long way. DevOps metrics to help you meet your DevOps goals. Your next challenge is ensuring your DevOps processes, pipelines, and tooling meet the intended goal. Deployment frequency measures both long-term and short-term efficiency.
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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.
This growth was spurred by mobile ecosystems with Android and iOS operating systems, where ARM has a unique advantage in energy efficiency while offering high performance. Energy efficiency and carbon footprint outshine x86 architectures The first clear benefit of ARM in the enterprise IT landscape is energy efficiency.
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It requires a state-of-the-art system that can track and process these impressions while maintaining a detailed history of each profiles exposure. In this multi-part blog series, we take you behind the scenes of our system that processes billions of impressions daily.
UK Home Office: Metrics meets service The UK Home Office is the lead government department for many essential, large-scale programs. In this episode, Dimitris discusses the many different tools and processes they use. Make sure to stay connected with our social media pages. Tag us with #TechTransforms to be featured on our pages!
A Data Movement and Processing Platform @ Netflix By Bo Lei , Guilherme Pires , James Shao , Kasturi Chatterjee , Sujay Jain , Vlad Sydorenko Background Realtime processing technologies (A.K.A stream processing) is one of the key factors that enable Netflix to maintain its leading position in the competition of entertaining our users.
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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.
This guide will cover how to distribute workloads across multiple nodes, set up efficient clustering, and implement robust load-balancing techniques. Proper setup involves creating a configuration process that accounts for hostname changes, which could prevent nodes from rejoining the cluster.
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.
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RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. RabbitMQ follows a message broker model with advanced routing, while Kafkas event streaming architecture uses partitioned logs for distributed processing. What is RabbitMQ?
Organizations choose data-driven approaches to maximize the value of their data, achieve better business outcomes, and realize cost savings by improving their products, services, and processes. Data is then dynamically routed into pipelines for further processing. Understanding the context.
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Government agencies aim to meet their citizens’ needs as efficiently and effectively as possible to ensure maximum impact from every tax dollar invested. In contrast, observability enables teams to understand a system’s internal state by analyzing the data it generates, including logs, metrics, and traces.
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Fluent Bit is a telemetry agent designed to receive data (logs, traces, and metrics), process or modify it, and export it to a destination. Fluent Bit and Fluentd were created for the same purpose: collecting and processing logs, traces, and metrics. Observability: Elevating Logs, Metrics, and Traces!
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The primary goals of these campaigns are to encourage more people to install and play the games, making incremental installs and engagement crucial metrics for evaluating their effectiveness. Each format has a different production process and different patterns of cash spend, called our Content Forecast.
One issue that often complicates this process is the "noisy neighbor" problem. Continuous instrumentation is critical to catching such matters as they emerge, and eBPF, with its hooks into the Linux scheduler with minimal overhead, enabled us to monitor run queue latency efficiently.
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However, they can also be used to monitor optimization processes effectively. Efficient coordination among resource usage, requests, and allocation is critical. It’s important to choose the right metrics to track based on your objective. It provides insights into how efficiently the blocked resources are being utilized.
These developments open up new use cases, allowing Dynatrace customers to harness even more data for comprehensive AI-driven insights, faster troubleshooting, and improved operational efficiency. Customers have had a positive response to our native syslog implementation, noting its easy setup and efficiency.
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