More renaming stuff
Signed-off-by: keithhc2 <keithhc2@users.noreply.github.com>
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@ -10,6 +10,6 @@ has_toc: false
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Data Prepper is a server side data collector capable of filtering, enriching, transforming, normalizing and aggregating data for downstream analytics and visualization.
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Data Prepper lets users build custom pipelines to improve the operational view of applications. Two common uses for Data Prepper are trace and log analytics. [Trace analytics]({{site.url}}{{site.baseurl}}/observability/trace/index/) can help you visualize the flow of events and identify performance problems, and [log analytics]({{site.url}}{{site.baseurl}}/observability/log-analytics/) can improve searching, analyzing and provide insights into your application.
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Data Prepper lets users build custom pipelines to improve the operational view of applications. Two common uses for Data Prepper are trace and log analytics. [Trace analytics]({{site.url}}{{site.baseurl}}/observability-plugin/trace/index/) can help you visualize the flow of events and identify performance problems, and [log analytics]({{site.url}}{{site.baseurl}}/observability-plugin/log-analytics/) can improve searching, analyzing and provide insights into your application.
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To get started building your own custom pipelines with Data Prepper, see the [Get Started]({{site.url}}{{site.baseurl}}/clients/data-prepper/get-started/) guide.
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@ -73,7 +73,7 @@ This example uses weak security. We strongly recommend securing all plugins whic
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### Trace Analytics pipeline
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The following example demonstrates how to build a pipeline that supports the [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/clients/trace/ta-dashboards/). This pipeline takes data from the OpenTelemetry Collector and uses two other pipelines as sinks. These two separate pipelines index trace and the service map documents for the dashboard plugin.
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The following example demonstrates how to build a pipeline that supports the [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability-plugin/trace/ta-dashboards/). This pipeline takes data from the OpenTelemetry Collector and uses two other pipelines as sinks. These two separate pipelines index trace and the service map documents for the dashboard plugin.
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```yml
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entry-pipeline:
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@ -49,7 +49,7 @@ collections:
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replication-plugin:
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permalink: /:collection/:path/
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output: true
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observability:
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observability-plugin:
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permalink: /:collection/:path/
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output: true
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monitoring-plugins:
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@ -91,8 +91,8 @@ just_the_docs:
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replication-plugin:
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name: Replication plugin
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nav_fold: true
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observability:
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name: Observability
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observability-plugin:
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name: Observability plugin
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nav_fold: true
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monitoring-plugins:
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name: Monitoring plugins
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@ -6,7 +6,7 @@ nav_order: 10
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# Event analytics
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Event analytics in observability is where you can use [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability/ppl/index) (PPL) queries to build and view different visualizations of your data.
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Event analytics in observability is where you can use [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index) (PPL) queries to build and view different visualizations of your data.
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## Get started with event analytics
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@ -24,10 +24,10 @@ source = opensearch_dashboards_sample_data_logs | fields host | stats count()
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By default, Dashboards shows results from the last 15 minutes of your data. To see data from a different timeframe, use the date and time selector.
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For more information about building PPL queries, see [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability/ppl/index).
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For more information about building PPL queries, see [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index).
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## Save a visualization
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After Dashboards generates a visualization, you must save it if you want to return to it at a later time or if you want to add it to an [operational panel]({{site.url}}{{site.baseurl}}/observability/operational-panels).
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After Dashboards generates a visualization, you must save it if you want to return to it at a later time or if you want to add it to an [operational panel]({{site.url}}{{site.baseurl}}/observability-plugin/operational-panels).
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To save a visualization, expand the save dropdown menu next to **Run**, enter a name for your visualization, then choose **Save**. You can reopen any saved visualizations on the event analytics page.
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@ -4,8 +4,8 @@ title: About Observability
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nav_order: 1
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has_children: false
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redirect_from:
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- /observability/
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- /observability/
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- /observability-plugin/
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- /observability-plugin/
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---
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# About Observability
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Your experience of exploring data might differ, but if you're new to exploring data to create visualizations, we recommend trying a workflow like the following:
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1. Explore data over a certain timeframe using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability/ppl/index).
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2. Use [event analytics]({{site.url}}{{site.baseurl}}/observability/event-analytics) to turn data-driven events into visualizations.
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1. Explore data over a certain timeframe using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index).
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2. Use [event analytics]({{site.url}}{{site.baseurl}}/observability-plugin/event-analytics) to turn data-driven events into visualizations.
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![Sample Event Analytics View]({{site.url}}{{site.baseurl}}/images/event-analytics.png)
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3. Create [operational panels]({{site.url}}{{site.baseurl}}/observability/operational-panels) and add visualizations to compare data the way you like.
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3. Create [operational panels]({{site.url}}{{site.baseurl}}/observability-plugin/operational-panels) and add visualizations to compare data the way you like.
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![Sample Operational Panel View]({{site.url}}{{site.baseurl}}/images/operational-panel.png)
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4. Use [log analytics]({{site.url}}{{site.baseurl}}/observability/log-analytics) to transform unstructured log data.
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5. Use [trace analytics]({{site.url}}{{site.baseurl}}/observability/trace/index) to create traces and dive deep into your data.
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4. Use [log analytics]({{site.url}}{{site.baseurl}}/observability-plugin/log-analytics) to transform unstructured log data.
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5. Use [trace analytics]({{site.url}}{{site.baseurl}}/observability-plugin/trace/index) to create traces and dive deep into your data.
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![Sample Trace Analytics View]({{site.url}}{{site.baseurl}}/images/observability-trace.png)
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6. Leverage [notebooks]({{site.url}}{{site.baseurl}}/observability/notebooks) to combine different visualizations and code blocks that you can share with team members.
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6. Leverage [notebooks]({{site.url}}{{site.baseurl}}/observability-plugin/notebooks) to combine different visualizations and code blocks that you can share with team members.
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![Sample Notebooks View]({{site.url}}{{site.baseurl}}/images/notebooks.png)
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@ -6,7 +6,7 @@ nav_order: 30
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# Operational panels
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Operational panels in OpenSearch Dashboards are collections of visualizations generated using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability/ppl/index) (PPL) queries.
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Operational panels in OpenSearch Dashboards are collections of visualizations generated using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index) (PPL) queries.
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## Get started with operational panels
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To create an operational panel and add visualizations:
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1. From the **Add Visualization** dropdown menu, choose **Select Existing Visualization** or **Create New Visualization**, which takes you to the [event analytics]({{site.url}}{{site.baseurl}}/observability/event-analytics) explorer, where you can use PPL to create visualizations.
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1. From the **Add Visualization** dropdown menu, choose **Select Existing Visualization** or **Create New Visualization**, which takes you to the [event analytics]({{site.url}}{{site.baseurl}}/observability-plugin/event-analytics) explorer, where you can use PPL to create visualizations.
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1. If you're adding already existing visualizations, choose a visualization from the dropdown menu.
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1. Choose **Add**.
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@ -21,7 +21,7 @@ OpenSearch Trace Analytics consists of two components---Data Prepper and the Tra
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1. [Data Prepper]({{site.url}}{{site.baseurl}}/clients/data-prepper/index/) processes the OpenTelemetry data, transforms it for use in OpenSearch, and indexes it on an OpenSearch cluster.
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1. The [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability/trace/ta-dashboards/) displays the data in near real-time as a series of charts and tables, with an emphasis on service architecture, latency, error rate, and throughput.
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1. The [Trace Analytics OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability-plugin/trace/ta-dashboards/) displays the data in near real-time as a series of charts and tables, with an emphasis on service architecture, latency, error rate, and throughput.
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## Jaeger HotROD
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Navigate to `http://localhost:5601` in a web browser and choose **Trace Analytics**. You can see the results of your single click in the Jaeger HotROD web interface: the number of traces per API and HTTP method, latency trends, a color-coded map of the service architecture, and a list of trace IDs that you can use to drill down on individual operations.
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If you don't see your trace, adjust the timeframe in OpenSearch Dashboards. For more information on using the plugin, see [OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability/trace/ta-dashboards/).
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If you don't see your trace, adjust the timeframe in OpenSearch Dashboards. For more information on using the plugin, see [OpenSearch Dashboards plugin]({{site.url}}{{site.baseurl}}/observability-plugin/trace/ta-dashboards/).
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@ -262,4 +262,4 @@ You can use wildcards to delete more than one data stream.
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We recommend deleting data from a data stream using an ISM policy.
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You can also use [asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/index/) and [SQL]({{site.url}}{{site.baseurl}}/search-plugins/sql/index/) and [PPL]({{site.url}}{{site.baseurl}}/observability/ppl/index/) to query your data stream directly. You can also use the security plugin to define granular permissions on the data stream name.
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You can also use [asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/index/) and [SQL]({{site.url}}{{site.baseurl}}/search-plugins/sql/index/) and [PPL]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index/) to query your data stream directly. You can also use the security plugin to define granular permissions on the data stream name.
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