Elastic Stack Observability
Elastic Stack Observability is a monitoring platform built on Elasticsearch and Kibana that pulls logs, metrics, and application traces from your systems into one searchable store, so you can find the cause of a slowdown or outage from a single place instead of checking separate tools.
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Intro
Elastic Stack Observability
Elastic Stack Observability puts logs, metrics, application traces, and user-experience data into one Elasticsearch cluster, viewed and queried through Kibana. Elastic Observability combines these signals into a single, integrated platform for cross-referenced analysis, so a team that already runs Elasticsearch for search does not need a second data platform to answer "why is this service slow, erroring, or down."
The mental model:
system and application activity
↓
Elastic Agent, Beats, or an OpenTelemetry SDK
↓
ingestion (Fleet-managed integrations, Logstash, or the EDOT Collector)
↓
Elasticsearch index — logs, metrics, traces, normalized to common field names
↓
Kibana — search, dashboards, SLOs, alerts, AI-assisted investigation
Every signal lands in the same search engine, tagged with the same field names. That is what lets you pivot from a spike in a metric to the logs and traces recorded at the same moment, without switching tools or re-mapping field names by hand.
Why it exists
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Sources
- https://www.elastic.co/elastic-stack/
Supports
- The Elastic Stack comprises Elasticsearch, Kibana, Beats, Logstash, and additional integrations.
- Elasticsearch is a distributed JSON-based search and analytics engine, while Kibana is its extensible user interface.
- https://www.elastic.co/observability
Supports
- Elastic Observability brings together log analytics, infrastructure monitoring, APM and distributed tracing, digital experience monitoring, OpenTelemetry, and metrics monitoring.
- The product page lists more than 450 integrations across cloud services, CI/CD systems, databases, and other sources.
- https://www.elastic.co/docs/solutions/observability
Supports
- Elastic Observability stores and analyzes logs, metrics, traces, and related operational data in Elasticsearch through Kibana.
- Elastic supports OpenTelemetry as a collection path for observability data.
- https://www.elastic.co/docs/reference/fleet/install-elastic-agents
Supports
- Elastic Agent is installed on hosts to collect telemetry, and Fleet centrally manages policies, integrations, and upgrades.
- Fleet-managed agents are the recommended deployment model when centralized management is needed.
- https://www.elastic.co/docs/reference/ecs
Supports
- Elastic Common Schema is an open specification for normalizing event data with common field names and types.
- ECS supports consistent analysis, visualization, and correlation across Elastic, third-party, and custom data sources.
- https://www.elastic.co/docs/solutions/observability/apm
Supports
- Elastic APM provides application tracing, errors, metrics, and performance analysis.
- Elastic documents the classic APM-agent instrumentation path alongside OpenTelemetry-based instrumentation.
- https://www.elastic.co/docs/reference/opentelemetry/edot-sdks
Supports
- Elastic Distributions of OpenTelemetry provide Elastic-supported OpenTelemetry SDKs for application telemetry.
- Elastic recommends EDOT for new application instrumentation and does not support running an EDOT SDK with a classic Elastic APM agent in the same process.
- EDOT SDK telemetry is sent through the EDOT Collector or Elastic Cloud managed OTLP endpoint rather than directly to APM Server.
- https://www.elastic.co/observability/log-monitoring
Supports
- Elastic log analytics includes ingest, search, investigation, and machine-learning-assisted analysis of log data.
- LogsDB is an Elasticsearch index mode optimized for log data with sorting and compression.
- https://www.elastic.co/observability/synthetic-monitoring
Supports
- Synthetic monitoring runs scripted user journeys from managed or private testing locations.
- Synthetic monitoring complements real user monitoring, which records actual end-user interactions.
- https://www.elastic.co/docs/solutions/observability/incident-management/service-level-objectives-slos
Supports
- Elastic SLOs define service-level indicators, objectives, error budgets, and burn rates from observability data.
- Burn-rate analysis evaluates error-budget consumption across multiple time windows.
- https://github.com/dzharii/awesome-elasticsearch
Supports
- The curated list includes the official elasticsearch-py client, Cerebro, ElastAlert 2, and elasticdump as Elasticsearch ecosystem resources.