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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.
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.
However, data overload and skills shortages present challenges that companies need to address to maximize the benefits of cloud and AI technologies. With Dynatrace, customers can utilize the full set of Azure capabilities, including metrics and data from the Azure platform, and automatically identify workflow optimization opportunities.
Exploratory analytics now cover more bespoke scenarios, allowing you to access any element of test results stored in the Dynatrace Grail data lakehouse. Select any execution you’re interested in to display its details, for example, the content response body, its headers, and related metrics. Wed like to use it for our dashboards.
Chances are, youre a seasoned expert who visualizes meticulously identified key metrics across several sophisticated charts. The market is saturated with tools for building eye-catching dashboards, but ultimately, it comes down to interpreting the presented information.
Good visualizations are not just static, unintelligent data presentations; they enable interaction and ideally serve as a starting point for subsequent analysis. While histograms look much like time-series bar charts, they’re different in that each bar represents a count (often termed frequency) of metric values.
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.
Dynatrace collects a huge number of metrics for each OneAgent-monitored host in your environment. Depending on the types of technologies you’re running on individual hosts, the average number of metrics is about 500 per computational node. Running metric queries on a subset of entities for live monitoring and system overviews.
Metrics matter. But without complex analytics to make sense of them in context, metrics are often too raw to be useful on their own. To achieve relevant insights, raw metrics typically need to be processed through filtering, aggregation, or arithmetic operations. Examples of metric calculations. Dynatrace news.
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.
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. The hypermodal AI engine shows what’s happening in a system down to the data coming in, while presenting the information in context. “It’s
Analytical Insights Additionally, impression history offers insightful information for addressing a number of platform-related analytics queries. We accomplish this by gathering detailed column-level metrics that offer insights into the state and quality of each impression.
As the application owner of an e-commerce application, for example, you can enrich the source code of your application with domain-specific knowledge by adding actionable semantics to collected performance or business metrics. New OpenTelemetry metrics exporters provide the broadest language support on the market.
We’re proud to announce that Ally Financial has presented Dynatrace with its Ally Technology Velocity with Quality award. This is the second time Ally Financial has presented its Ally Technology Partner Awards. Earlier this year, Dynatrace presented Ally Financial with its own award as our first Digital Breakout Performer.
Across both his day one and day two mainstage presentations, Steve Tack, SVP of Product Management, described some of the investments we’re making to continue to differentiate the Dynatrace Software Intelligence Platform. Dynatrace news. As a result, we announced the extended support for Kubernetes for Dynatrace customers.
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 ingests AWS CloudWatch metrics for multiple preselected services. Amazon Kinesis Data Analytics. Select Add metric to save your settings.
But on their own, logs present just another data silo as IT professionals attempt to troubleshoot and remediate problems. These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues.
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.
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. Observability on edge devices presents unique challenges compared to traditional data-center or cloud-based environments.
From a cost perspective, internal customers waste valuable time sending tickets to operations teams asking for metrics, logs, and traces to be enabled. A team looking for metrics, traces, and logs no longer needs to file a ticket to get their app monitored in their own environments. This approach is costly and error prone.
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 ingests AWS CloudWatch metrics for multiple preselected services. Amazon Kinesis Data Analytics. Select Add metric to save your settings.
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…?
Realizing that executives from other organizations are in a similar situation to my own, I want to outline three key objectives that Dynatrace’s powerful analytics can help you deliver, featuring nine use cases that you might not have thought possible. With the latest advances from Dynatrace, this process is instantaneous.
Similar to the observability desired for a request being processed by your digital services, it’s necessary to comprehend the metrics, traces, logs, and events associated with a code change from development through to production. Lastly, we’re working on a ready-made dashboard for the DORA metrics based on GitHub and ArgoCD metadata.
A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. Further, it presents data in intuitive, user-friendly ways to enable data gathering, analysis, and collaboration among far-flung teams. Here are some examples: IT infrastructure and operations.
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.
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. Cloud environments present IT complexity challenges that don’t exist in on-premises data centers. Improve business decisions with precision analytics.
They’re unleashing the power of cloud-based analytics on large data sets to unlock the insights they and the business need to make smarter decisions. From a technical perspective, however, cloud-based analytics can be challenging. That’s especially true of the DevOps teams who must drive digital-fueled sustainable growth.
Building on its advanced analytics capabilities for Prometheus data , Dynatrace now enables you to create extensions based on Prometheus metrics. Many technologies expose their metrics in the Prometheus data format. Easily gain actionable insights with the Dynatrace Extension for Prometheus metrics. Dynatrace news.
Automated Metric Anomaly Detection. Thanks to the automated dependency information (=Dynatrace Smartscape), Dynatrace’s AI Davis automatically analyzes every single metric along the dependency tree. From here we also get access to all other pod & process relevant metrics, e.g. memory, threads, … or accessing the container logs.
Monitoring SAP products can present challenges Monitoring SAP systems can be challenging due to the inherent complexity of using different technologies—such as ABAP, Java, and cloud offerings—and the sheer amount of generated data. Visibility into SAP CPI messages, down to every single attribute.
Part of our series on who works in Analytics at Netflix?—?and I’m a Senior Analytics Engineer on the Content and Marketing Analytics Research team. My team focuses on innovating and maintaining the metrics Netflix uses to understand performance of our shows and films on the service. But what do I actually do?
framework , the SNMP extensions are a bundle of everything that’s needed (DataSource configuration, a dashboard template, a unified analysis page template, topology definition, entity extraction rules, relevant metric definitions and more) to get going with monitoring. Simplified data analysis presented in topological context.
A full list of metrics can be found here and include dimensions such as the following: Packets. When it comes to logs and metrics, the Dynatrace platform provides direct access to the log content of all mission-critical processes. A feature that enables you to present log data in a filterable table that is easy to work with.
With the latest release, we drive this further by improving the automatic connection of relevant log and trace data for further drill down, presenting the full context of an issue in a single view. Traditional forecasting engines typically depend on historical data, stored in metrics.
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.
” But, he continues, ” Today’s environments present a completely different picture. Traditional log management solution challenges Survey data suggests that teams need a modern approach to log management and analytics, which requires a unified log management solution. during 2021–2026.
automating ingestion of logs, metrics, and traces and continuous dependency mapping with precise context across hybrid and multicloud environments. Log Viewer enables users to present log data in a filterable, easy-to-use table and to browse log data within a certain time frame using detected aspects of the log content.
But organizations must also be aware of the pitfalls of AI: security and compliance risks, biases, misinformation, and lack of insight into critical metrics (including availability, code development, infrastructure, databases, and more). But contextual analytics don’t stop here. “AI
Organizations can now accelerate innovation and reduce the risk of failed software releases by incorporating on-demand synthetic monitoring as a metrics provider for automatic, continuous release-validation processes. This metric indicates how quickly software can be released to production. Dynatrace news.
Because of everything that can go wrong, it’s imperative for organizations to constantly track metrics that indicate user satisfaction and have a robust complaint resolution model in place. Without agreeing on the single source of truth, you’ll end up in meetings arguing about metrics instead of helping your users.
Observability is typically achieved by collecting three types of data from a system, metrics, logs and traces. Some platforms provide built-in metrics, logs and traces for serverless functions, while others require additional configuration or integration with external services or agents.
Although Dynatrace can’t help with the manual remediation process itself , end-to-end observability, AI-driven analytics, and key Dynatrace features proved crucial for many of our customers’ remediation efforts. Time is of the essence in any crisis—so is having the right tools and capabilities.
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