More renaming stuff

Signed-off-by: keithhc2 <keithhc2@users.noreply.github.com>
This commit is contained in:
keithhc2 2022-02-08 15:05:16 -08:00
parent a5f26f8d67
commit 4f78620d48
20 changed files with 21 additions and 21 deletions

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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.
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.
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.
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
### Trace Analytics pipeline
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.
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.
```yml
entry-pipeline:

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@ -49,7 +49,7 @@ collections:
replication-plugin:
permalink: /:collection/:path/
output: true
observability:
observability-plugin:
permalink: /:collection/:path/
output: true
monitoring-plugins:
@ -91,8 +91,8 @@ just_the_docs:
replication-plugin:
name: Replication plugin
nav_fold: true
observability:
name: Observability
observability-plugin:
name: Observability plugin
nav_fold: true
monitoring-plugins:
name: Monitoring plugins

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# Event analytics
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.
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.
## Get started with event analytics
@ -24,10 +24,10 @@ source = opensearch_dashboards_sample_data_logs | fields host | stats count()
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.
For more information about building PPL queries, see [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability/ppl/index).
For more information about building PPL queries, see [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index).
## Save a visualization
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).
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).
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
nav_order: 1
has_children: false
redirect_from:
- /observability/
- /observability/
- /observability-plugin/
- /observability-plugin/
---
# About Observability
@ -16,13 +16,13 @@ Observability is collection of plugins and applications that let you visualize d
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:
1. Explore data over a certain timeframe using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability/ppl/index).
2. Use [event analytics]({{site.url}}{{site.baseurl}}/observability/event-analytics) to turn data-driven events into visualizations.
1. Explore data over a certain timeframe using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index).
2. Use [event analytics]({{site.url}}{{site.baseurl}}/observability-plugin/event-analytics) to turn data-driven events into visualizations.
![Sample Event Analytics View]({{site.url}}{{site.baseurl}}/images/event-analytics.png)
3. Create [operational panels]({{site.url}}{{site.baseurl}}/observability/operational-panels) and add visualizations to compare data the way you like.
3. Create [operational panels]({{site.url}}{{site.baseurl}}/observability-plugin/operational-panels) and add visualizations to compare data the way you like.
![Sample Operational Panel View]({{site.url}}{{site.baseurl}}/images/operational-panel.png)
4. Use [log analytics]({{site.url}}{{site.baseurl}}/observability/log-analytics) to transform unstructured log data.
5. Use [trace analytics]({{site.url}}{{site.baseurl}}/observability/trace/index) to create traces and dive deep into your data.
4. Use [log analytics]({{site.url}}{{site.baseurl}}/observability-plugin/log-analytics) to transform unstructured log data.
5. Use [trace analytics]({{site.url}}{{site.baseurl}}/observability-plugin/trace/index) to create traces and dive deep into your data.
![Sample Trace Analytics View]({{site.url}}{{site.baseurl}}/images/observability-trace.png)
6. Leverage [notebooks]({{site.url}}{{site.baseurl}}/observability/notebooks) to combine different visualizations and code blocks that you can share with team members.
6. Leverage [notebooks]({{site.url}}{{site.baseurl}}/observability-plugin/notebooks) to combine different visualizations and code blocks that you can share with team members.
![Sample Notebooks View]({{site.url}}{{site.baseurl}}/images/notebooks.png)

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# Operational panels
Operational panels in OpenSearch Dashboards are collections of visualizations generated using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability/ppl/index) (PPL) queries.
Operational panels in OpenSearch Dashboards are collections of visualizations generated using [Piped Processing Language]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/index) (PPL) queries.
## Get started with operational panels
@ -16,7 +16,7 @@ If you want to start using operational panels without adding any data, expand th
To create an operational panel and add visualizations:
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.
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.
1. If you're adding already existing visualizations, choose a visualization from the dropdown menu.
1. Choose **Add**.

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@ -21,7 +21,7 @@ OpenSearch Trace Analytics consists of two components---Data Prepper and the Tra
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.
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.
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.
## Jaeger HotROD
@ -78,4 +78,4 @@ curl -X GET -u 'admin:admin' -k 'https://localhost:9200/otel-v1-apm-span-000001/
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.
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/).
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.
We recommend deleting data from a data stream using an ISM policy.
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.
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.