Merge pull request #400 from opensearch-project/data-prepper-move

Moving data prepper to clients and tools

_clients/agents-and-ingestion-tools/index.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Agents and ingestion tools
-nav_order: 100
+nav_order: 140
 has_children: false
 has_toc: false
 redirect_from:

_clients/data-prepper/data-prepper-reference.md

@@ -7,7 +7,7 @@ nav_order: 3
 
 # Data Prepper configuration reference
 
-This page lists all supported Data Prepper server, sources, buffers, preppers, and sinks, along with their associated options. For example configuration files, see [Data Prepper]({{site.url}}{{site.baseurl}}/observability/data-prepper/pipelines/).
+This page lists all supported Data Prepper server, sources, buffers, preppers, and sinks, along with their associated options. For example configuration files, see [Data Prepper]({{site.url}}{{site.baseurl}}/clients/data-prepper/pipelines/).
 
 ## Data Prepper server options
 
@@ -49,7 +49,7 @@ max_connection_count | No | Integer | The maximum allowed number of open connect
 ssl | No | Boolea | Enables connections to the OTel source port over TLS/SSL. Defaults to `true`.
 sslKeyCertChainFile | Conditionally | String | File-system path or AWS S3 path to the security certificate (e.g. `"config/demo-data-prepper.crt"` or `"s3://my-secrets-bucket/demo-data-prepper.crt"`). Required if ssl is set to `true`.
 sslKeyFile | Conditionally | String | File-system path or AWS S3 path to the security key (e.g. `"config/demo-data-prepper.key"` or `"s3://my-secrets-bucket/demo-data-prepper.key"`). Required if ssl is set to `true`.
-useAcmCertForSSL | No | Boolean, enables TLS/SSL using certificate and private key from AWS Certificate Manager (ACM). Default is `false`.
+useAcmCertForSSL | No | Boolean | Whether to enable TLS/SSL using certificate and private key from AWS Certificate Manager (ACM). Default is `false`.
 acmCertificateArn | Conditionally | String | Represents the ACM certificate ARN. ACM certificate take preference over S3 or local file system certificate. Required if `useAcmCertForSSL` is set to `true`.
 awsRegion | Conditionally | String | Represents the AWS region to use ACM or S3. Required if `useAcmCertForSSL` is set to `true` or `sslKeyCertChainFile` and `sslKeyFile` are AWS S3 paths.
 authentication | No | Object| An authentication configuration. By default, this runs an unauthenticated server. This uses pluggable authentication for HTTPS. To use basic authentication, define the `http_basic` plugin with a `username` and `password`. To provide customer authentication use or create a plugin which implements: [GrpcAuthenticationProvider](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/armeria-common/src/main/java/com/amazon/dataprepper/armeria/authentication/GrpcAuthenticationProvider.java).

_clients/data-prepper/get-started.md

@@ -41,7 +41,7 @@ docker run --name data-prepper \
     opensearchproject/opensearch-data-prepper:latest
 ```
 
-This sample pipeline configuration above demonstrates a simple pipeline with a source (`random`) sending data to a sink (`stdout`). For more examples and details on more advanced pipeline configurations, see [Pipelines]({{site.url}}{{site.baseurl}}/observability/data-prepper/pipelines).
+This sample pipeline configuration above demonstrates a simple pipeline with a source (`random`) sending data to a sink (`stdout`). For more examples and details on more advanced pipeline configurations, see [Pipelines]({{site.url}}{{site.baseurl}}/clients/data-prepper/pipelines).
 
 After starting Data Prepper, you should see log output and some UUIDs after a few seconds:
 

_clients/data-prepper/index.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: Data Prepper
-nav_order: 80
+nav_order: 120
 has_children: true
 has_toc: false
 ---
@@ -10,6 +10,6 @@ has_toc: false
 
 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}}/observability/data-prepper/get-started/) guide.
+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.

_clients/data-prepper/pipelines.md

@@ -42,7 +42,7 @@ simple-sample-pipeline:
 
 ## Examples
 
-This section provides some pipeline examples that you can use to start creating your own pipelines. For more information, see [Data Prepper configuration reference]({{site.url}}{{site.baseurl}}/observability/data-prepper/data-prepper-reference/) guide.
+This section provides some pipeline examples that you can use to start creating your own pipelines. For more information, see [Data Prepper configuration reference]({{site.url}}{{site.baseurl}}/clients/data-prepper/data-prepper-reference/) guide.
 
 The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.
 
@@ -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}}/observability/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:

_clients/javascript.md

@@ -1,7 +1,7 @@
 ---
 layout: default
 title: JavaScript client
-nav_order: 90
+nav_order: 100
 ---
 
 # JavaScript client

_config.yml

@@ -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

_observability-plugin/event-analytics.md

@@ -6,7 +6,7 @@ nav_order: 10
 
 # 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.

_observability-plugin/index.md

@@ -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)

_observability-plugin/log-analytics.md

@@ -10,7 +10,7 @@ Log ingestion provides a way to transform unstructured log data into structured
 
 ## Get started with log ingestion
 
-OpenSearch Log Ingestion consists of three components---[Data Prepper]({{site.url}}{{site.baseurl}}/observability/data-prepper/index/), [OpenSearch]({{site.url}}{{site.baseurl}}/) and [OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/)---that fit into the OpenSearch ecosystem. The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.
+OpenSearch Log Ingestion consists of three components---[Data Prepper]({{site.url}}{{site.baseurl}}/clients/data-prepper/index/), [OpenSearch]({{site.url}}{{site.baseurl}}/) and [OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/)---that fit into the OpenSearch ecosystem. The Data Prepper repository has several [sample applications](https://github.com/opensearch-project/data-prepper/tree/main/examples) to help you get started.
 
 ### Basic flow of data
 
@@ -20,7 +20,7 @@ OpenSearch Log Ingestion consists of three components---[Data Prepper]({{site.ur
 
    (In the [example](#example) below, [FluentBit](https://docs.fluentbit.io/manual/) is used as a log collector that collects log data from a file and sends the log data to Data Prepper).
 
-2. [Data Prepper]({{site.url}}{{site.baseurl}}/observability/data-prepper/index/) receives the log data, transforms the data into a structure format, and indexes it on an OpenSearch cluster.
+2. [Data Prepper]({{site.url}}{{site.baseurl}}/clients/data-prepper/index/) receives the log data, transforms the data into a structure format, and indexes it on an OpenSearch cluster.
 
 3. The data can then be explored through OpenSearch search queries or the **Discover** page in OpenSearch Dashboards.
 

_observability-plugin/operational-panels.md

@@ -6,7 +6,7 @@ nav_order: 30
 
 # 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**.
 

_observability-plugin/trace/get-started.md

@@ -19,9 +19,9 @@ OpenSearch Trace Analytics consists of two components---Data Prepper and the Tra
 
 1. The [OpenTelemetry Collector](https://opentelemetry.io/docs/collector/getting-started/) receives data from the application and formats it into OpenTelemetry data.
 
-1. [Data Prepper]({{site.url}}{{site.baseurl}}/observability/data-prepper/index/) processes the OpenTelemetry data, transforms it for use in OpenSearch, and indexes it on an OpenSearch cluster.
+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/).

_opensearch/data-streams.md

@@ -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.