Dynatrace Masterclass - The Complete Guide for Beginners

Analyze a ten-line sample app log in a Linux terminal to count requests, identify errors, and spot slow responses. See why observability platforms like Dynatrace exist.

Explore Dynatrace clouds for end-to-end cloud observability, tracking resources with metadata, metrics, logs, and events across AWS, Azure, and GCP. Filter by environment, region, and tags to drill into resources.

Discover the databases observability app, a unified view of monitored databases powered by Dynatrace SQL extensions, with auto fetch of execution plans and performance metrics like query latency.

Explore the infrastructure and operations app to monitor data centers, hosts, and network devices, detect performance issues early with Davis AI, and drill into metrics, logs, and events.

Utilize Dynatrace Kubernetes observability to view clusters, nodes, and pods through a command center, with metrics, logs, events, and traces for quick troubleshooting.

Explore Kubernetes classic as the original monitoring view for clusters, nodes, pods, and workloads, and learn why the modern Kubernetes app offers richer, scalable insights.

Compare the old AWS classic with cloud services in Dynatrace, connect your AWS accounts, and customize which services and metrics to monitor for deeper insights.

Discover how Dynatrace migrated Azure monitoring from Azure Classic to the clouds app, overcoming limited coverage and configuration to monitor newer Azure services.

Explore Cloud Foundry, a platform that lets developers push apps without server management, while Dynatrace Cloud Foundry monitors platform health, response times, errors, and resource usage.

Learn how Dynatrace monitors containers with a single cluster agent, automatically discovering containers, tracking health, CPU and memory usage, restarts, errors, and slow responses, and alerting you.

Explore extensions to extend dynatrace capabilities for data acquisition and domain expertise, view and manage installed extensions, check configuration and health, and deploy new extensions from the Dynatrace hub.

Clarify why two apps exist for extensions in Dynatrace by contrasting the previous version with the new extension experience, and guide beginners to use the new one.

Explore the Dynatrace technology overview to see all discovered software technologies and their running processes, drill into specific technologies like php, and assess process health, deployment and potential issues.

Navigate both classic and new Dynatrace interfaces, review front end monitoring of mobile apps, application services, and databases, and explore distributed traces, multidimensional analysis, profiling, and optimization using live demos.

Explore how distributed tracing in Dynatrace tracks requests across microservices, combines topology and code-level visibility, and provides an overview with charts, traces, and deep-dive trace analytics.

Practice lab invites you to analyze many traces and locate bottlenecks via trace IDs in a complete user transaction, highlighting distributed tracing and comparing slow, failed, and healthy requests.

Learn how Dynatrace's new distributed tracing app scales for large trace data, integrates with OpenTelemetry, and supports microservices and container-based architectures, with powerful filtering and quick troubleshooting.

Discover multidimensional analysis to inspect user actions in your application using Dynatrace's built-in top web requests, top database statements, and exception analysis.

Explore multidimensional analysis of top web requests in Dynatrace, learning how to configure metrics, split modes, and filters to uncover top URLs and error patterns.

Explore top database statements in multidimensional analysis by viewing the last seven days, filtering by database service, and exporting data for exception analysis and distributed traces.

Use exception analysis to identify top exceptions in your environment, view by service, drill into details, and uncover root causes for issues like failed to charge card.

Create a multidimensional analysis from scratch to compare web request services by average response time. Split by service, view the last seven days, identify cart checkout as slowest, and save.

Learn to edit and refine multidimensional analyses in dynatrace, switch metrics such as response time and failure rate, and save as new views or templates for quick reuse.

Learn how to build multidimensional analyses from scratch, save and customize views from prebuilt queries, and use metrics like CPU time, processing time, and failure rate to quickly derive insights.

Explore application observability with Dynatrace by profiling and optimization, using automated problem detection and manual tools to identify CPU usage, memory dumps, analysis, and crashes.

Explore cpu profiling in dynatrace by examining process groups, cpu consumption, and gc time, then drill down with hotspots and top APIs to identify primary cpu hotspots.

Analyze process crashes across your environment with a 30-day overview, crash details, host and process information, and downloadable artifacts for deeper analysis.

Discover how the Dynatrace services app reveals how each service behaves, shows performance, failure rates, response time, and resource usage, and enables fast troubleshooting and collaborative monitoring.

Use the Dynatrace message queues app to visualize queues and Kafka topics, reveal producers and consumers, and monitor incoming versus outgoing throughput to spot backlog.

Learn about the new AI observability app in Dynatrace, gaining visibility into model behavior, usage, and costs, while tracking versions and spotting issues early for reliable AI deployments.

Explore the ai observability interface in dynatrace, connect with openurl metrics, generate the access token, and send ai telemetry data. Understand the overview, service health previews, dashboards, and cost estimates.

AI observability overview: metrics for services, models, agents, model requests, token usage, latency, and cost, with governance visibility and capacity planning.

Explore the errors tab to monitor model reliability with success and failure rate, zero problems, and invocation error counts, and learn how time frame affects error metrics.

Explore traffic and latency in AI model invocations, comparing response times across OpenAI models, tracking average percentile and throughput to inform model selection and capacity planning.

Explore cost metrics like token count, cost per request, and cache hit ratio to forecast spending, optimize prompt design, compare models, and plan scalable AI.

Guardrails act as the AI safety layer that monitors outputs, prevents sharing personal data or restricted topics, and tracks activations, PII leaks, and blocked prompts over time.

Explore AI dashboards in Dynatrace, featuring Amazon Bedrock integration for building AI apps, with health, performance, cost analysis, and Davis AI forecast to predict costs and issues.

Explore how azure openai enables running gpt models on microsoft azure with enterprise security, identity controls, scalability, and regional hosting, plus dashboards for secure data handling and enterprise integration.

Explore Google Gemini, Google's newest family of AI models designed to understand and generate information across text, images, audio, and code, with a dashboard overview of its multimodal capabilities.

Explore Nvidia dashboard to verify inference health, monitor latency, and optimize GPU usage with metrics like time to first token, throughput per second, concurrency, and cash utilization.

Observe OpenAI usage in your app with Dynatrace, treating it as an external service with latency, availability, and cost while tracking token usage and end-to-end tracing.

Explore Kong AI as an AI gateway that governs, routes, and protects traffic to AI models. Use Dynatrace dashboards to monitor gateway health, latency, token usage, costs, and route performance.

The prompt audit trail records every AI prompt interaction as an audit event, capturing the prompt text, model, timestamp, and provider to enable governance, incident analysis, defensibility, and accountability.

Explore AI model versioning and comparison with a side-by-side dashboard that compares two models across speed, token usage, and cost, using real prompts and tracing to prevent regressions.

Discover how the Dynatrace application security overview monitors global vulnerabilities, highlighting critical risks, third-party and code-level issues, 30-day trends, host coverage, and affected process groups.

Discover third-party vulnerabilities in Dynatrace, learn to view open and muted issues, filter by critical risk, and use the Davos Security Advisor and CVSS-based scoring to prioritize fixes.

Dynatrace monitors code level vulnerabilities in libraries and first-party code, identifies how user inputs can exploit flaws, and analyzes related process groups and public internet exposure to aid quick fixes.

Navigate Dynatrace's attack analytics, identifying malicious requests, tags, and attack sources across timeframes; examine SQL and command injections, block statuses, and country-specific drill-downs to resolve vulnerabilities.

Dynatrace's vulnerabilities app ranks issues by the security score within a time window, covering code level, third party, and new vulnerabilities. The security advisor suggests fixes and upgrades.

Learn how Dynatrace's security posture management continuously checks configurations against compliance standards to identify misconfigurations, reveal failures, and guide remediation through assessments, dashboards, and rule details.

Explore how the Threats and Exploits app detects real-time attacks, correlates them with code-level vulnerabilities, and reveals attacker details, vectors, and incident responses for beginners.

Explore how the security investigator in Dynatrace acts as a detective notebook to investigate suspicious activity, analyze logs, collect evidence, and document conclusions using real runtime data.

Explore session segmentation in the Dynatrace digital experience dashboard, analyzing user sessions (visits, journeys, click paths) with live data across performance, errors, conversions, and geographic and device insights.

Explore Dynatrace filtering to segment sessions by time frame, country, operating system, and user actions, then drill down to compare usability and insights.

Dive into individual sessions to troubleshoot user experience, review filters, and analyze session details, including duration, actions, conversions, and errors, for anonymous and known users.

Explore the user sessions query tool to create custom advanced queries and generate bar and pie chart visualizations. Filter by country to analyze session data and build dashboards.

Record and replay user sessions to analyze interactions, identify errors, and reveal confusing interfaces or slow performance. Replay full sessions or segments to review frustrations and plan improvements.

Explore synthetic monitoring in digital experience section to simulate user interactions, verify availability and performance from any location, using single browser monitors, browser click path, and http monitors.

Create a browser-based synthetic monitor in a Dynatrace environment, configuring a Google test from Europe with an iPhone 12 Pro, Madrid, Spain, every ten minutes, and apply tags.

Duplicate an existing synthetic monitor to quickly create multiple similar tests, then edit the device profile (e.g., iPhone with 2G/3G), enable and save to deploy tests in seconds.

Learn to create a click path synthetic monitor in Dynatrace by recording with the Dynatrace synthetic recorder or manually, including location checks from Mumbai to Tokyo.

Explore synthetic monitor settings in the Dynatrace masterclass, including status, type, device profile, location, and tags, edit monitors, configure performance metrics, thresholds, frequency, and click paths for reliable monitoring.

Experience vitals tracks front-end health from real user data, displaying healthy, warning, or unhealthy statuses. It provides overview, explorer, and performance views with ready dashboards for user-centric observability.

Dynatrace's error inspector surfaces front end errors in web and mobile apps by user impact, guiding prioritization and fixes across types, operating systems, URL, and location.

Explore the new app synthetic interface for synthetic monitoring, showing monitors with proactive monitoring, availability, and health. Select a monitor to view details, errors, traces, and executions.

Explore the opportunity insights app, which uses ai and real user data to rank improvement opportunities and translate performance gains into measurable business outcomes such as revenue and sessions.

Open notebooks to explore the observe and explore section, learn the Dynatrace query language (DQL), and query, analyze, and visualize observability data in collaborative, data-driven documents, and fetch external data.

Create your first notebook and rename it to get started. Learn to build sections, especially query sections, using the Dynatrace query language to fetch logs and explore basic visualization options.

Learn to use the Dynatrace query language to fetch, filter, and limit data in Dynatrace, apply a trace sampled equals true filter, and sort by timestamp.

Open notebooks to practice Dynatrace query language and fetch logs with filters like log level equals error, then compare results across last two hours to a five minute time frame.

Learn how to use Dynatrace DQL to filter logs by level, code 400, and platform conditions, add comments, and refine results with contains, endswith, and brackets.

Learn to use or and filter out with conditions and countif for log levels. Explore time series by log level with intervals and summaries for errors, info, and warnings.

Discover how to use Dynatrace query language (DQL) to count log levels in the last hour and visualize results with pie and categorical charts.

Learn to boost collaboration in notebooks with markdown sections that add context, text, images, and links. Master formatting options like italic, code, tables, and insert links to the Dynatrace docs.

Duplicate notebook sections, edit and run queries, filter alerts, and quickly recreate results, then move sections to a dashboard or open with automation using Dynatrace query language.

Use sampling to analyze large datasets and manage performance and cost, by adjusting the sampling ratio, counting errors, and approximating totals, revealing near-accurate insights without scanning all data.

Bin data by timestamp to analyze when errors occur, using one-hour bins and sampling to reveal time-of-day trends in the last seven days for dql analysis.

Apply best practices for structuring Dynatrace query language queries by fetching logs, filtering early, applying filter out, selecting essential fields, and sequencing summarize before sort to optimize performance.

Master the Dynatrace query language with practical references to the official Dynatrace documentation, learning to fetch, filter, and summarize data using DQL functions and time series concepts.

Explore the data explorer to query metrics, visualize disk space usage, and publish to classic dashboards or notebooks, with templates, timeframes, thresholds, and transformation options.

Explore data explorer to create and customize multiple metrics, switch visualizations, and pin them to dashboards, using built-in metrics and understanding persistence across edits.

Create a Kubernetes metric from scratch in dynatrace, exploring pre-built metrics, data availability, and flexible display options like time frames, splitting by cluster or node, and line or column charts.

Explore the problems app, enhanced with Davis AI explanations, that group symptoms into incidents, surface root causes like memory saturation on Kubernetes nodes, and suggest remediation.