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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.
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. Key insights for executives: Optimize customer experiences through end-to-end contextual analytics from observability, user behavior, and business data. Google or Adobe Analytics).
Dynatrace Business Flow simplifies business process observability, connecting top-level process KPIs with detailed flow analytics. Each business unit relies on a collection of processes, and each process has metrics and KPIs that can be affected by delays, exceptions, or failures. How does this change over time? Average duration.
Davis AI contextually aligns all relevant data points—such as logs, traces, and metrics—enabling teams to act quickly and accurately while still providing power users with the flexibility and depth they desire and need. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
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.
Dynatrace unified analytics capabilities for observability are top-of-the-class ( Gartner Magic Quadrant 2024 ), enabling you to query and analyze all your observability data across your enterprise. Go to our documentation to learn more about implementing honeycomb visualizations on your dashboards or notebooks.
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.
We introduced Dynatrace’s 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. Only with Dynatrace Digital Busines Analytics.
Grail – the foundation of exploratory analytics Grail can already store and process log and business events. Now we’re adding Smartscape to DQL and two new data sources to Grail: Metrics on Grail and Traces on Grail. With Dynatrace and Smartscape for DQL, metrics are a completely different game.
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. Current analytics tools are fragmented and lack context for meaningful analysis. Effective analytics with the Dynatrace Query Language.
Manual and configuration-heavy approaches to putting telemetry data into context and connecting metrics, traces, and logs simply don’t scale. By unifying log analytics with PurePath tracing, Dynatrace is now able to automatically connect monitored logs with PurePath distributed traces. New to Dynatrace? Start your free trial!
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.
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.
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.
When using Dynatrace, in addition to automatic log collection, you gain full infrastructure context and access to powerful, advanced log analytics tools such as the Logs, Notebooks, and Dashboards apps. For forensic log analytics use cases, the Security Investigator app benefits from the scalability and analytics power of Dynatrace Grail.
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.
Amazon Bedrock , equipped with Dynatrace Davis AI and LLM observability , gives you end-to-end insight into the Generative AI stack, from code-level visibility and performance metrics to GenAI-specific guardrails. Send unified data to Dynatrace for analysis alongside your logs, metrics, and traces.
We added monitoring and analytics for log streams from Kubernetes and multicloud platforms like AWS, GCP, and Azure, as well as the most widely used open-source log data frameworks. Whatever your use case, when log data reflects changes in your infrastructure or business metrics, you need to extract the metrics and monitor them.
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.
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 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.
Well also demonstrate how you can drill down on such problems further to identify bottlenecks within the application’s ingested distributed traces using the analytics power of Grail. To set up the token, see Dynatrace APITokens and authentication in Dynatrace documentation. If you dont have one, you can use a trial account.
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 ElastiCache (see AWS documentation for Memcached and Redis ). Amazon Aurora.
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.
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. Dynatrace Documentation maintains a list of events, which will grow as we unlock new use cases.
Application logs and metrics are vital for any application development or maintenance process. However, managing and analyzing logs and metrics can be a daunting task, especially if the application generates a large volume of data. It stores data in a document-oriented index, offering fast search and analytics capabilities.
Now, developers can build software libraries and use OpenTelemetry to add tracing and telemetry directly into them so an observability analytics backend, such as Dynatrace, can consume the data immediately. With deep analytics of traces from Dynatrace, developers have data in full context, which helps them easily debug instrumentation issues.
Incomplete view of the ordering process due to older systems Get business process observability across data silos with Dynatrace OpenPipeline Traditional observability platforms often focus on technical metrics and logs, which are essential for troubleshooting, but they don’t take business value into consideration.
Telemetry data, such as traces and metrics, allow you to analyze the end-to-end performance of your deployed applications. Dynatrace Operator consumes DynaKubes with cloud-native full-stack configuration and deploys the following resources: Dynatrace OneAgent, deployed as a DaemonSet, collects host metrics from Kubernetes nodes.
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 ElastiCache (see AWS documentation for Memcached and Redis ). Amazon Aurora.
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.
Spring also introduced Micrometer, a vendor-agnostic metric API with rich instrumentation options. Soon after, Dynatrace built a registry for exporting Micrometer metrics. Our data APIs, which ingest millions of metrics, traces, and logs per second, are reconciled using Micrometer-based metrics.
Dynatrace provides tooling and documentation to help you migrate your Extensions 1.0 address these limitations and brings new monitoring and analytical capabilities that weren’t available to Extensions 1.0: Comprehensive metrics support Extensions 2.0 Reporting and analytics assets out-of-the-box Bundles offered by Extensions 2.0
OpenTelemetry has become a standard for collecting traces, metrics, and logs. Utilizing an additional OpenTelemetry SDK layer, this data seamlessly flows into the Dynatrace environment, offering advanced analytics and a holistic view of the AI deployment stack. Maintained under the Apache 2.0
Optimize cost and availability while staying compliant Observability data like logs and metrics provide automated answers, root cause detection, and security issues. Every API call is saved in audit logs to document the complete picture of activities in your environments.
One-click activation of log collection and Azure Monitor metric collection in the Microsoft Azure Portal allows instant ingest of Azure Monitor logs and metrics into the Dynatrace platform. Notebooks offers advanced Azure observability analytics with DQL. There’s no need for configuration or setup of any infrastructure.
If you can collect the relevant data (and that’s a big if), the problem shifts to analytics. Connecting data from different systems, stitching process steps together, calculating delays between steps, alerting on business exceptions and technical issues, and tracking SLOs are just some of the requirements for an effective analytics solution.
In addition to APM , th is platform offers our customers infrastructure monitoring spanning logs and metrics, digital business analytics, digital experience monitoring, and AIOps capabilities. as part of a larger research document and should be evaluated in the context of the entire document.
The forecast operation is selected within the Davis action, and a DQL query is used to specify the set of disks and the capacity indicator metric that should be predicted. Tip: Download the TypeScript template from our documentation. In this example, two parallel actions are defined. Create an alarm event for predicted shortages.
As web applications commonly use load balancers, such as F5 BIG-IP, Dynatrace customers often seek to enhance their monitoring capabilities by integrating it with Dynatrace comprehensive monitoring and AI-powered analytics. You can customize the metrics shown to suit your individual needs. Example F5 overview dashboard. What’s next?
Table name Default bucket logs default_logs events default_events metrics default_metrics bizevents default_bizevents dt.system.events dt_system_events entities spans (in the future) The default buckets let you ingest data immediately, but you can also create additional custom buckets to make the most of Grail.
The comprehensive functionality is highly customizable, facilitating a seamless presentation of any SAP metric in the context of SAP systems, business architecture, and all SAP-integrated systems. Notebooks and dashboards enable users, including developers, to create data-driven documents for custom analytics.
The Local self-monitoring environment collects and aggregates all the self-monitoring metrics that are captured from the other environments on the cluster. When the capture rate for metrics is at or near 100%, data capture is in progress and all incoming data is covered. The Code Modules metric show the deployment status of the?OneAgent
Logs complement metrics and enable automation Cloud practitioners agree that observability, security, and automation go hand in hand. Logs complement out-of-the-box metrics and enable automated actions for responding to availability, security, and other service events.
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