Dynatrace Bootcamp: Observability, AI, AppSec & Automation

Explore Dynatrace high-level architecture as a unified analytics and automation platform, detailing observability and security principles, core components, and multi-layer topology from bottom to top.

Explore the one agent, pure path, data lakehouse, and automation engine behind Dynatrace's full stack observability with Davis AI, including automatic instrumentation and real user monitoring.

Explore PurePath, Dynatrace's distributed tracing technology that captures full traces for every request across microservices, containers, Kubernetes, and databases, with code visibility and correlation of logs, traces, metrics, and events.

a distributed trace is a tree of spans sharing a trace id, with context propagation linking root to each service. Oneagent auto-injects and extracts trace context for end-to-end visibility.

Grail, the unified data lakehouse in the Dynatrace platform, enables full stack observability, security analytics, and business telemetry by preserving full context and enabling fast, scalable queries.

Leverage the Gradle-powered automation engine to transform observability, security, and business data into real-time, event-driven and AI-driven workflows that automatically respond to anomalies, logs, metrics, traces, and KPIs.

Explore how an agent collects data viewer path, traces requests, and correlates logs, metrics, and events; grail analyzes data at scale with preserved context, enabling automated actions via Davis AI.

Uncover four key strengths of the Dynatrace platform: automatic full stack instrumentation via one agent and context-rich data, and Grail, the unified data lake for observability, security, and analytics. Leverage Davis AI's causal, predictive, and generative insights alongside a powerful automation engine and extensible app platform for end-to-end automation across DevOps, SecOps, and business operations.

Automatically instrument technology stack with one agent to discover, monitor, and collect metrics, logs, traces, and security data with dependency and topology context across hosts, Kubernetes, microservices, and serverless platforms.

Grail data unifies logs, metrics, traces, events, topology, security findings, and business data in Lake House Grail, enabling massively parallel query language and preserving full context for analytics.

Utilize Davis AI, a causal, predictive, and generative AI engine that identifies root causes from service dependencies and topology gathered by one agent, forecasts issues, and delivers AI assisted insights.

Leverage the Dynatrace automation engine and app platform to build event-driven and AI-driven workflows that auto-remediate anomalies, orchestrate DevSecOps, using a visual no-code workflow builder and ITSM, CI/CD, cloud integrations.

Explore digital experience monitoring on the Dynatrace platform, including real user monitoring, session replay, synthetic monitoring, mobile rum, and analysis with Grail data lakehouse, Dql, and Davis AI.

Explore Dynatrace functionalities in a sandbox playground to monitor user sessions and investigate app performance. Deploy a full stack web app to the cloud with AWS, tracing API requests.

Navigate to a sandbox Dynatrace environment to explore digital experience monitoring capabilities. Inspect the Hipster Shop Logs app and walk through the analysis section to understand its value.

Perform a health check and explore the topology to verify ROM status and site reliability metrics, while understanding how the Dynatrace one-agent injects JavaScript telemetry.

Explore performance analysis and user behavior insights across desktop browsers, real and synthetic users, and regions, with detailed load times, page breakdowns (home, cart, checkout), and conversion events.

Examine Apdex scores and Dynatrace insights to understand user satisfaction, compare timeframes from last seven days to previous periods, and pinpoint checkout and homepage performance bottlenecks.

Analyze apdex drivers and landing page load times with a waterfall analysis to identify slow resources, CDN assets, and caching opportunities for data distribution.

Explore user sessions in Dynatrace, filter by application, view live and historical sessions, and analyze metrics—from duration to user experience scores—to replay and diagnose issues.

Learn to query user sessions with SQL and DQL in Grail data lakehouse, generate stats for seven days, and visualize browser load times and errors with line and bar charts.

Analyze errors by type (requests, JavaScript, custom) and user type (real, synthetic, robots). Trace issues from user actions to code and monitor HTTP 500 cart checkout with Apdex and CLS.

Discover how Dynatrace problems with Pure Path, Smart Topology, and Davis AI diagnose JavaScript and request errors, visualize root cause with the visual resolution path, and pinpoint issues in Kubernetes.

Explore how Dynatrace analyzes errors across services classic and new apps in Kubernetes, with active problems and time-filtered views, and note that distributed tracing and session analytics are covered later.

Learn to interact with the Dynatrace API programmatically by sending, getting, and posting requests, using tokens, the API explorer, and logs endpoints to automate workflows.

Learn how to create a new web application in Dynatrace via a post request, using Postman to send an API payload and authenticate with an API token.

Demonstrates creating applications programmatically in Dynatrace using access tokens, Postman, and the configuration API, then validating the new web app in the platform with minimal overhead.

Push business metrics into Dynatrace using the metrics ingest API via Swagger, including creating a metrics push token and sending commercial.promos.discount with a 0.05 value; verify results in notebooks.

Learn to set up a VPN server on AWS LightSail and monitor it with Dynatrace OneAgent. Track CPU usage, load, and metrics to ensure privacy and connectivity.

Set up a free 15-day Dynatrace trial by creating an account, choosing EU West Ireland, and launching Dynatrace to explore monitoring of your VPN and infrastructure.

Set up outline, an open source vpn tech backed by Google, using outline manager and outline client to host servers and route traffic, including a new Amazon Lightsail deployment.

Spin up a London-based VPN server on Amazon LightSail using Ubuntu, install Outline via Docker, configure firewall and SSH keys, and verify your IP geolocation with get my IP.

Deploy the Dynatrace one agent to a linux x86 server, connect via ssh, and monitor host and software metrics (cpu, memory, disk, docker) using hosts classic and infrastructure and operations.

Shut down and delete the Amazon LightSail instance, remove the server from Outline client and Outline Manager, and ensure no further cloud charges.

Explore the Dynatrace UI and apps—hosts classic, infrastructure and operations, notebooks, smartscape topology, session replay, and releases—for monitoring infrastructure, networks, databases, and services across AWS and GCP, with SLOs.

Explore the hosts classic in Dynatrace to view CPU, memory, traffic, and container metrics for a VPN server on Linux and AWS; rename the host and explore naming rules.

Rename the host to VPN server London and explore infrastructure and operations metrics, including vulnerabilities, CVEs, full stack monitoring, and Node.js deep monitoring within Dynatrace.

Analyze container resource consumption over 72 hours for two containers, Watchtower and Shadow Box Stable, and drill into CPU usage, memory usage, and metrics for Main.js and Prometheus.

Learn how to define and monitor service level objectives (SLOs) and availability targets with Dynatrace, including setting thresholds, metrics, time windows, and error budgets.

Learn to monitor hosts and virtual machines with one Dynatrace agent, drill into CPU, memory, and logs, and use SmartScape, vulnerabilities, and service level objectives for VPN server observability.

Explore Dynatrace notebooks as interactive, step-by-step data analysis tools and compare them to dashboards for reporting. Use notebooks for exploratory data analysis and dashboards for multi-factor insights, blend them.

Start from what users observe, verify the change, form a hypothesis, and narrow down—repeat until you name the root cause—using DQL as the core tool in Dynatrace observability.

Explore Dynatrace notebooks app to run DQL queries, fetch logs, and create visualizations—tables, time-series graphs, and CPU usage predictions with Davis AI.

Explore Dynatrace visualisations by aggregating log level messages with a summarize query and count function, then visualize results with pie, donut, or categorical charts.

Harness markdown basics for notebooks to annotate log messages, headers, lists, bold text, and hyperlinks for clear cpu load analysis.

Explore how Dynatrace notebooks integrate Davis AI to forecast time series like the average CPU load, generating predictions with confidence intervals and anomaly detection options for budgeting and infrastructure planning.

Explore Dynatrace notebooks' advanced features, hide and show input to focus on visualizations, and build and summarize log queries with DQL, aggregations, and categorical visualizations for DevOps insights.

Enable internet access in Dynatrace notebooks to fetch external data with a JavaScript get request to an open API, and log results to the console after whitelisting the domain.

Share your research results using Dynatrace notebooks by creating share links and setting view or edit permissions. Collaborate with colleagues by sharing notebooks via emails and applying access rules.

Learn to monitor databases with Dynatrace using a practical PostgreSQL case on AWS EC2, including extension setup, active gate deployment, and dashboard-driven performance insights.

Set up and monitor a PostgreSQL database on an AWS EC2 Ubuntu instance using Dynatrace one agent and ActiveGate, with the PostgreSQL extension for deep monitoring and dashboards.

Log into the aws console and launch an ubuntu 24.04 ec2; select t2.medium with at least 2 vcpus and 4 gb ram, and configure ssh and postgresql rules.

Set up PostgreSQL on a remote server via SSH, install and configure it, create the dynatrace database and an employees table, and prepare to load absenteeism data from CSV.

Learn data ingestion by uploading absenteeism.csv to a remote server, moving it to /tmp, and copying it into the Postgres employees table, then configure SQL access for Dynatrace.

Configure PostgreSQL to accept external connections for Dynatrace by setting listen_addresses to all and updating pg_hba to allow all hosts with md5, then restart and verify port 5432.

Enable Dynatrace to interact with the cloud SQL by creating a Dynatrace user, granting pg_monitor rights, and connecting to the Dynatrace database via terminal and DBeaver to test connectivity.

connect to a cloud-based postgresql database from your local machine using a command line interface and dbeaver, testing the connection and previewing data to prepare for dynatrace integration.

Deploy the Dynatrace OneAgent to an EC2 Linux host to monitor PostgreSQL, start data transmission, verify deployment, and plan to use the PostgreSQL extension and Dynatrace infrastructure features.

Explore basic monitoring in Dynatrace by running SQL queries and database load, then analyze PostgreSQL metrics such as CPU, memory, IO, connections, and events in hosts classic.

Set up a Dynatrace active gate to secure communication with AWS EC2, then configure the PostgreSQL extension in Dynatrace hub to monitor your database using that active gate.

Explore deep monitoring of a Postgres database with Dynatrace dashboards, reviewing instances, databases, locks, connections, and transactions, and learn to create custom dashboards for in-depth performance analytics.

Shut down AWS resources, disable Dynatrace host monitoring, and summarize the finished setup: PostgreSQL on EC2 with Dynatrace agent and hub extension connected to Active Gate, with metrics.