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[8.x](backport #4615) Add the OTel quickstart (#4758)
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docs/en/observability/index.asciidoc

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[[quickstart-monitor-hosts-with-otel]]
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= Quickstart: Monitor hosts with OpenTelemetry
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preview::[]
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In this quickstart guide, you'll learn how to monitor your hosts using the Elastic Distribution of OpenTelemetry (EDOT) Collector.
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You'll also learn how to use {observability} features to gain deeper insight into your observability data after collecting it.
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[discrete]
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== Prerequisites
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* An {es} cluster for storing and searching your data, and {kib} for visualizing and managing your data. This quickstart is available for all Elastic deployment models. The quickest way to get started with this quickstart is using a trial project on {serverless-docs}/quickstart-monitor-hosts-with-otel.html[Elastic serverless].
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* This quickstart is only available for Linux and MacOS systems.
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* A user with the **Admin** role or higher—required to onboard system logs and metrics. To learn more, refer to {cloud}/ec-user-privileges.html[User roles and privileges].
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* Root privileges on the host—required to run the OpenTelemetry collector because of these components:
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** `hostmetrics` receiver to read all system metrics (all processes, memory, etc.).
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** `filelog` to allow the collector to read any user or application log files.
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[discrete]
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== Limitations
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Refer to https://github.com/elastic/opentelemetry/blob/main/docs/collector-limitations.md[Elastic OpenTelemetry Collector limitations] for known limitations when using the EDOT Collector.
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[discrete]
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== Collect your data
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Follow these steps to collect logs and metrics using the EDOT Collector:
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. In {kib}, go to the **Observability** UI and click **Add Data**.
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. Under **What do you want to monitor?** select **Host**, and then select **OpenTelemetry: Logs & Metrics**.
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[role="screenshot"]
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image::images/quickstart-monitor-hosts-otel-entry-point.png[Host monitoring entry point]
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. Select the appropriate platform.
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. Copy the command under step 1, open a terminal on your host, and run the command.
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This command downloads the {agent} package, extracts it in a EDOT directory. For example, `elastic-distro-8.16.0-linux-x86_64`.
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It also adds a sample `otel.yml` configuration file to the directory and updates the storage directory, Elastic endpoint, and API key in the file.
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The default log path is `/var/log/*.log`. To update the path, modify the `otel.yml` in the EDOT directory.
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Find additional sample `otel.yml` configuration files in the EDOT directory in the `otel_samples` folder.
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. Copy the command under Step 2 and run it in your terminal to start the EDOT Collector.
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NOTE: Logs are collected from setup onward, so you won't see logs that occurred before starting the EDOT Collector.
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Under **Visualize your data**, you'll see links to **Logs Explorer** to view your logs and **Hosts** to view your host metrics.
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[discrete]
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== Gain deeper insight into your host data
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After using the Hosts page and Discover to confirm you've ingested all the host logs and metrics you want to monitor,
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use Elastic {observability} to gain deeper insight into your host data with the following capabilities and features:
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* In the <<monitor-infrastructure-and-hosts,Infrastructure UI>>, analyze and compare data collected from your hosts.
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You can also:
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** <<inspect-metric-anomalies,Detect anomalies>> for memory usage and network traffic on hosts.
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** <<create-alerts,Create alerts>> that notify you when an anomaly is detected or a metric exceeds a given value.
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* In the <<explore-logs,Logs Explorer>>, search and filter your log data,
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get information about the structure of log fields, and display your findings in a visualization.
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You can also:
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** <<monitor-datasets,Monitor log data set quality>> to find degraded documents.
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** {kibana-ref}/xpack-ml-aiops.html#log-pattern-analysis[Run a pattern analysis] to find patterns in unstructured log messages.
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** <<create-alerts,Create alerts>> that notify you when an Observability data type reaches or exceeds a given value.
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* Use {kibana-ref}/xpack-ml.html[machine learning] to apply predictive analytics to your data:
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** {kibana-ref}/xpack-ml-anomalies.html[Detect anomalies] by comparing real-time and historical data from different sources to look for unusual, problematic patterns.
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** {kibana-ref}/xpack-ml-aiops.html#log-rate-analysis[Analyze log spikes and drops].
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** {kibana-ref}/xpack-ml-aiops.html#change-point-detection[Detect change points] in your time series data.
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Refer to the <<observability-introduction>> for a description of other useful features.

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