Datadog Observability (Monitoring, Logging, Alerting) in K8s

Install and monitor a Kubernetes cluster with Datadog on EKS, deploy a sample guestbook app using YAML, expose via ingress Engine X, and view logs and metrics in Datadog.

Deploy the Datadog agent to a Kubernetes cluster using a daemonset and helm, configure logs and apm, and enable Kubernetes integration to monitor and visualize cluster health with prebuilt dashboards.

Explain how the Datadog agent gathers data—server CPU, disk load, memory, network IO, uptime events, and docker events—and how these metrics appear in the Kubernetes dashboard.

Explore the Datadog agent architecture in Kubernetes, detailing the collector and forwarder as the core pod processes, with optional APM agent and process agent for traces and live process monitoring.

Assess Datadog agent overhead in Kubernetes by measuring CPU, memory, and network use; reveal negligible CPU and memory under 16 MB, upgrading the agent image tag to fix memory leaks.

Exclude containers and namespaces from Datadog logs and metrics in Kubernetes to curb costs, with a focus on Istio sidecar traffic and common misconfigurations.

Learn how the Datadog cluster agent proxies node Datadog agents in Kubernetes, reducing API server load while enabling cluster-wide metrics and enriched metadata.

Integrate Datadog with aws using a cloudformation template to auto-create the integration, grant permissions, and collect cloudwatch metrics from ec2, load balancers, and other services, all visible in Datadog dashboards.

Explore Datadog dashboards, including the out-of-box options, in the dashboard list. Review the Kubernetes overview, Redis, and MySQL/RDS dashboards to monitor containers and databases.

Explore the 4.2 widget view in Datadog observability for Kubernetes, learning to maximize widgets, reveal hover actions, export to notebook or png/svg, and adjust time frames with previews and tables.

Copy widget queries in Datadog to clipboard for dashboards and alerts, then monitor Kubernetes metrics such as requests per second, CPU and memory usage to set alerts.

Learn to copy metrics queries from a Datadog widget, correlate node and pod CPU usage with logs and traces, and use pattern recognition to diagnose spikes quickly.

Explore infrastructure dashboards that visualize hosts, containers, processes, pods, and serverless functions. Identify outliers and usage patterns while navigating the events, metrics, and apm views.

Navigate the Datadog infrastructure list and host map to view per-host metrics, tags, and billing counts. Explore server, container, and pod views with Kubernetes and Docker metrics, logs, and correlation.

Explore the 5.2 host map to visualize infrastructure topology and cpu utilization across EC2 hosts, with filters by availability zone and id, and drill into live containers and pods.

Navigate the container map in 5.3, selecting containers as a topology inside a worker node, with zoom capabilities to inspect countable containers.

Explore live containers in Datadog's Kubernetes view for real-time visibility into deployments, pods, namespaces, and logs. Correlate YAML, logs, metrics, and topology through cluster maps and log explorer.

Master Datadog log management by collecting, analyzing, and exploring logs from services, containers, and cloud in a single view, and correlate them with metrics and request tracing.

Datadog logs are enabled by default in Kubernetes, with the agent collecting from container stdout and stderr. Enable auto discovery and log integration to add metadata like source and service.

Filter and visualize kubernetes logs in datadog using namespace, service, pod, and port name dimensions; leverage log dashboard facets, include/exclude rules, and live tail with precise query syntax.

Learn how to scrub sensitive information in Kubernetes logs before ingesting them into Datadog by applying built-in log processing rules and metadata annotations in pod or deployment YAML.

Aggregate multi-line logs to reduce log records and save costs in Datadog by treating multiple lines as a single log record, and override processing rules to correctly name the service.

Exclude logs from certain containers and namespaces to lower Datadog cost, using image, namespace, or pod name filters, a best practice covered earlier, so this item is skipped.

Navigate from logs to metrics using the log explorer, dashboards, and metrics tab; jump from dashboards to logs and view related logs, including near the highlighted time frame.

Configure a manual log pipeline in Datadog for Istio service mesh, featuring Json pre-processing, sequential processors including Node.js, and library remappers to map log status for accurate observability.

Configure log index lifecycles in datadog by specifying ingestion, indexing, and archiving with tag-based retention rules. Use exclusion filters to omit logs such as Istio proxy v2 and control costs.

Archive logs in Datadog to cloud storage such as S3 for long-term retention, with default 15 days and a 60-day max, enabling rehydration back into Datadog for audit and compliance.

Use facets to filter logs in Datadog, distinguishing built-in tags from custom facets, and manage them in the facet panel to refine searches and create views.

Identify and monitor notable events that signal changes in code deployments, service health, or configuration, and trigger alerts using Datadog across Kubernetes, Docker, and other integrations.

Explore Datadog event streams to monitor Kubernetes and Docker events, using the 2D table view and histogram previews, filter by status, and diagnose issues across nodes and containers.

Explore how to push custom metrics from applications to Datadog with Dogstatsd, configure client and agent setups in Kubernetes, and use metrics for alerts and dashboards.

Learn to configure Datadog monitors and alerts for Kubernetes using metric-based queries, including disk space alerts when system.disk.in_use exceeds 0.9, with tailored titles and messages.

Define metrics by selecting data from dashboard widgets or the Datadog integration document, then configure alerts using functions like average by host and device, excluding certain namespaces.

Set alert condition by creating a new metric monitor in Datadog, choose a metric such as system load or disk in use, adjust the threshold and period, and configure notifications.

Configure alert messages in datadog observability for k8s by setting the title, message, and destinations (email, channels, or sms) in section 9.3, and test notifications.

Learn how to create service level objectives (SLOs) and monitor them with Datadog in Kubernetes, defining targets, time windows, and alert thresholds for API reliability.

Explore practical Datadog alert queries for Kubernetes, covering worker node cpu and memory usage. Review pod counts, port termination, and Docker versus Kubernetes events to avoid false positives.

Explore how to tag Datadog telemetry with key–value pairs such as host, service, and namespace, enabling search, filtering, and aggregation across logs, metrics, and traces.

Explore the default out-of-the-box tags added by the Datadog agent, such as container ID and port name. Learn to filter logs by Kubernetes namespace using human-readable or cube_namespace syntax.

Learn how to assign Datadog tags in Kubernetes deployments using UI, config, or Helm charts, including service and version labels to persist logs, metrics, and traces.

Enable unified service tagging in Kubernetes by injecting metadata labels and env vars to containers, aligning logs, metrics, and traces across three standard tags, including service and version, across environments.

Learn how to use tags to filter Datadog dashboards across events, monitors, logs, and infra, with service prefixes and unified tagging.