Change first instances of data prepper to open search data prepper (#9091)

* Change first instances of Data Prepper to OpenSearch Data Prepper

Signed-off-by: natebower <[email protected]>

* Rest of changes

Signed-off-by: natebower <[email protected]>

* Change card

Signed-off-by: Fanit Kolchina <[email protected]>

* Change card

Signed-off-by: Fanit Kolchina <[email protected]>

* One more

Signed-off-by: Fanit Kolchina <[email protected]>

---------

Signed-off-by: natebower <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Co-authored-by: Fanit Kolchina <[email protected]>
Co-authored-by: kolchfa-aws <[email protected]>

Author: natebower

_config.yml

@@ -223,7 +223,7 @@ benchmark_collection:
 data_prepper_collection:
   collections:
     data-prepper: 
-      name: Data Prepper
+      name: OpenSearch Data Prepper
       nav_fold: true
 
 # Defaults
@@ -240,7 +240,7 @@ defaults:
       path: "_data-prepper"
     values:
       section: "data-prepper"
-      section-name: "Data Prepper"
+      section-name: "OpenSearch Data Prepper"
   -
     scope:
       path: "_clients"

_data-prepper/common-use-cases/anomaly-detection.md

@@ -7,7 +7,7 @@ nav_order: 5
 
 # Anomaly detection
 
-You can use Data Prepper to train models and generate anomalies in near real time on time-series aggregated events. You can generate anomalies either on events generated within the pipeline or on events coming directly into the pipeline, like OpenTelemetry metrics. You can feed these tumbling window aggregated time-series events to the [`anomaly_detector` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/anomaly-detector/), which trains a model and generates anomalies with a grade score. Then you can configure your pipeline to write the anomalies to a separate index to create document monitors and trigger fast alerting.
+You can use OpenSearch Data Prepper to train models and generate anomalies in near real time on time-series aggregated events. You can generate anomalies either on events generated within the pipeline or on events coming directly into the pipeline, like OpenTelemetry metrics. You can feed these tumbling window aggregated time-series events to the [`anomaly_detector` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/anomaly-detector/), which trains a model and generates anomalies with a grade score. Then you can configure your pipeline to write the anomalies to a separate index to create document monitors and trigger fast alerting.
 
 ## Metrics from logs 
 

_data-prepper/common-use-cases/codec-processor-combinations.md

@@ -7,7 +7,7 @@ nav_order: 10
 
 # Codec processor combinations
 
-At ingestion time, data received by the [`s3` source]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/sources/s3/) can be parsed by [codecs]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/sources/s3#codec). Codecs compresses and decompresses large data sets in a certain format before ingestion them through a Data Prepper pipeline [processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/processors/).
+At ingestion time, data received by the [`s3` source]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/sources/s3/) can be parsed by [codecs]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/sources/s3#codec). Codecs compresses and decompresses large data sets in a certain format before ingestion them through an OpenSearch Data Prepper pipeline [processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/processors/).
 
 While most codecs can be used with most processors, the following codec processor combinations can make your pipeline more efficient when used with the following input types.
 

_data-prepper/common-use-cases/common-use-cases.md

@@ -9,4 +9,4 @@ redirect_from:
 
 # Common use cases
 
-You can use Data Prepper for several different purposes, including trace analytics, log analytics, Amazon S3 log analytics, and metrics ingestion.
+You can use OpenSearch Data Prepper for several different purposes, including trace analytics, log analytics, Amazon S3 log analytics, and metrics ingestion.

_data-prepper/common-use-cases/event-aggregation.md

@@ -7,7 +7,7 @@ nav_order: 25
 
 # Event aggregation
 
-You can use Data Prepper to aggregate data from different events over a period of time. Aggregating events can help to reduce unnecessary log volume and manage use cases like multiline logs that are received as separate events. The [`aggregate` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/aggregate/) is a stateful processor that groups events based on the values for a set of specified identification keys and performs a configurable action on each group.
+You can use OpenSearch Data Prepper to aggregate data from different events over a period of time. Aggregating events can help to reduce unnecessary log volume and manage use cases like multiline logs that are received as separate events. The [`aggregate` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/aggregate/) is a stateful processor that groups events based on the values for a set of specified identification keys and performs a configurable action on each group.
 
 The `aggregate` processor state is stored in memory. For example, in order to combine four events into one, the processor needs to retain pieces of the first three events. The state of an aggregate group of events is kept for a configurable amount of time. Depending on your logs, the aggregate action being used, and the number of memory options in the processor configuration, the aggregation could take place over a long period of time.
 

_data-prepper/common-use-cases/log-analytics.md

@@ -7,7 +7,7 @@ nav_order: 30
 
 # Log analytics
 
-Data Prepper is an extendable, configurable, and scalable solution for log ingestion into OpenSearch and Amazon OpenSearch Service. Data Prepper supports receiving logs from [Fluent Bit](https://fluentbit.io/) through the [HTTP Source](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/http-source/README.md) and processing those logs with a [Grok Processor](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/grok-processor/README.md) before ingesting them into OpenSearch through the [OpenSearch sink](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/opensearch/README.md).
+OpenSearch Data Prepper is an extendable, configurable, and scalable solution for log ingestion into OpenSearch and Amazon OpenSearch Service. Data Prepper supports receiving logs from [Fluent Bit](https://fluentbit.io/) through the [HTTP Source](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/http-source/README.md) and processing those logs with a [Grok Processor](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/grok-processor/README.md) before ingesting them into OpenSearch through the [OpenSearch sink](https://github.com/opensearch-project/data-prepper/blob/main/data-prepper-plugins/opensearch/README.md).
 
 The following image shows all of the components used for log analytics with Fluent Bit, Data Prepper, and OpenSearch.
 

_data-prepper/common-use-cases/log-enrichment.md

@@ -7,7 +7,7 @@ nav_order: 35
 
 # Log enrichment
 
-You can perform different types of log enrichment with Data Prepper, including:
+You can perform different types of log enrichment with OpenSearch Data Prepper, including:
 
 - Filtering.
 - Extracting key-value pairs from strings.

_data-prepper/common-use-cases/metrics-logs.md

@@ -7,7 +7,7 @@ nav_order: 15
 
 # Deriving metrics from logs
 
-You can use Data Prepper to derive metrics from logs. 
+You can use OpenSearch Data Prepper to derive metrics from logs. 
 
 The following example pipeline receives incoming logs using the [`http` source plugin]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/sources/http-source) and the [`grok` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/grok/). It then uses the [`aggregate` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/aggregate/) to extract the metric bytes aggregated during a 30-second window and derives histograms from the results.
 

_data-prepper/common-use-cases/metrics-traces.md

@@ -7,7 +7,7 @@ nav_order: 20
 
 # Deriving metrics from traces
 
-You can use Data Prepper to derive metrics from OpenTelemetry traces. The following example pipeline receives incoming traces and extracts a metric called `durationInNanos`, aggregated over a tumbling window of 30 seconds. It then derives a histogram from the incoming traces.
+You can use OpenSearch Data Prepper to derive metrics from OpenTelemetry traces. The following example pipeline receives incoming traces and extracts a metric called `durationInNanos`, aggregated over a tumbling window of 30 seconds. It then derives a histogram from the incoming traces.
 
 The pipeline contains the following pipelines:
 

_data-prepper/common-use-cases/s3-logs.md

@@ -7,7 +7,7 @@ nav_order: 40
 
 # S3 logs
 
-Data Prepper allows you to load logs from [Amazon Simple Storage Service](https://aws.amazon.com/s3/) (Amazon S3), including traditional logs, JSON documents, and CSV logs.
+OpenSearch Data Prepper allows you to load logs from [Amazon Simple Storage Service](https://aws.amazon.com/s3/) (Amazon S3), including traditional logs, JSON documents, and CSV logs.
 
 ## Architecture
 

_data-prepper/common-use-cases/sampling.md

@@ -7,7 +7,7 @@ nav_order: 45
 
 # Sampling
 
-Data Prepper provides the following sampling capabilities:
+OpenSearch Data Prepper provides the following sampling capabilities:
 
 - Time sampling
 - Percentage sampling

_data-prepper/common-use-cases/text-processing.md

@@ -7,7 +7,7 @@ nav_order: 55
 
 # Text processing
 
-Data Prepper provides text processing capabilities with the [`grok processor`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/grok/). The `grok` processor is based on the [`java-grok`](https://mvnrepository.com/artifact/io.krakens/java-grok) library and supports all compatible patterns. The `java-grok` library is built using the [`java.util.regex`](https://docs.oracle.com/javase/8/docs/api/java/util/regex/package-summary.html) regular expression library.
+OpenSearch Data Prepper provides text processing capabilities with the [`grok processor`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/grok/). The `grok` processor is based on the [`java-grok`](https://mvnrepository.com/artifact/io.krakens/java-grok) library and supports all compatible patterns. The `java-grok` library is built using the [`java.util.regex`](https://docs.oracle.com/javase/8/docs/api/java/util/regex/package-summary.html) regular expression library.
 
 You can add custom patterns to your pipelines by using the `patterns_definitions` option. When debugging custom patterns, the [Grok Debugger](https://grokdebugger.com/) can be helpful.
 

_data-prepper/common-use-cases/trace-analytics.md

@@ -7,7 +7,7 @@ nav_order: 60
 
 # Trace analytics
 
-Trace analytics allows you to collect trace data and customize a pipeline that ingests and transforms the data for use in OpenSearch. The following provides an overview of the trace analytics workflow in Data Prepper, how to configure it, and how to visualize trace data.
+Trace analytics allows you to collect trace data and customize a pipeline that ingests and transforms the data for use in OpenSearch. The following provides an overview of the trace analytics workflow in OpenSearch Data Prepper, how to configure it, and how to visualize trace data.
 
 ## Introduction
 

_data-prepper/getting-started.md

@@ -1,14 +1,14 @@
 ---
 layout: default
-title: Getting started
+title: Getting started with OpenSearch Data Prepper
 nav_order: 5
 redirect_from:
   - /clients/data-prepper/get-started/
 ---
 
-# Getting started with Data Prepper
+# Getting started with OpenSearch Data Prepper
 
-Data Prepper is an independent component, not an OpenSearch plugin, that converts data for use with OpenSearch. It's not bundled with the all-in-one OpenSearch installation packages.
+OpenSearch Data Prepper is an independent component, not an OpenSearch plugin, that converts data for use with OpenSearch. It's not bundled with the all-in-one OpenSearch installation packages.
 
 If you are migrating from Open Distro Data Prepper, see [Migrating from Open Distro]({{site.url}}{{site.baseurl}}/data-prepper/migrate-open-distro/). 
 {: .note}

_data-prepper/index.md

@@ -1,6 +1,6 @@
 ---
 layout: default
-title: Data Prepper 
+title: OpenSearch Data Prepper 
 nav_order: 1
 has_children: false
 has_toc: false
@@ -12,9 +12,9 @@ redirect_from:
   - /data-prepper/index/
 ---
 
-# Data Prepper
+# OpenSearch Data Prepper
 
-Data Prepper is a server-side data collector capable of filtering, enriching, transforming, normalizing, and aggregating data for downstream analysis and visualization. Data Prepper is the preferred data ingestion tool for OpenSearch. It is recommended for most data ingestion use cases in OpenSearch and for processing large, complex datasets.
+OpenSearch Data Prepper is a server-side data collector capable of filtering, enriching, transforming, normalizing, and aggregating data for downstream analysis and visualization. Data Prepper is the preferred data ingestion tool for OpenSearch. It is recommended for most data ingestion use cases in OpenSearch and for processing large, complex datasets.
 
 With Data Prepper you can build custom pipelines to improve the operational view of applications. Two common use cases for Data Prepper are trace analytics and log analytics. [Trace analytics]({{site.url}}{{site.baseurl}}/data-prepper/common-use-cases/trace-analytics/) can help you visualize event flows and identify performance problems. [Log analytics]({{site.url}}{{site.baseurl}}/data-prepper/common-use-cases/log-analytics/) equips you with tools to enhance your search capabilities, conduct comprehensive analysis, and gain insights into your applications' performance and behavior.
 
@@ -74,6 +74,6 @@ In the given pipeline configuration, the `source` component reads string events
 
 ## Next steps
 
-- [Get started with Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/getting-started/).
+- [Getting started with OpenSearch Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/getting-started/).
 - [Get familiar with Data Prepper pipelines]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/pipelines/).
 - [Explore common use cases]({{site.url}}{{site.baseurl}}/data-prepper/common-use-cases/common-use-cases/). 

_data-prepper/managing-data-prepper/configuring-data-prepper.md

@@ -1,16 +1,16 @@
 ---
 layout: default
-title: Configuring Data Prepper
-parent: Managing Data Prepper
+title: Configuring OpenSearch Data Prepper
+parent: Managing OpenSearch Data Prepper
 nav_order: 5
 redirect_from:
  - /clients/data-prepper/data-prepper-reference/
  - /monitoring-plugins/trace/data-prepper-reference/
 ---
 
-# Configuring Data Prepper
+# Configuring OpenSearch Data Prepper
 
-You can customize your Data Prepper configuration by editing the `data-prepper-config.yaml` file in your Data Prepper installation. The following configuration options are independent from pipeline configuration options. 
+You can customize your OpenSearch Data Prepper configuration by editing the `data-prepper-config.yaml` file in your Data Prepper installation. The following configuration options are independent from pipeline configuration options. 
 
 
 ## Data Prepper configuration

_data-prepper/managing-data-prepper/configuring-log4j.md

@@ -1,13 +1,13 @@
 ---
 layout: default
 title: Configuring Log4j
-parent: Managing Data Prepper
+parent: Managing OpenSearch Data Prepper
 nav_order: 20
 ---
 
 # Configuring Log4j
 
-You can configure logging using Log4j in Data Prepper. 
+You can configure logging using Log4j in OpenSearch Data Prepper. 
 
 ## Logging 
 

_data-prepper/managing-data-prepper/core-apis.md

@@ -1,13 +1,13 @@
 ---
 layout: default
 title: Core APIs
-parent: Managing Data Prepper
+parent: Managing OpenSearch Data Prepper
 nav_order: 15
 ---
 
 # Core APIs
 
-All Data Prepper instances expose a server with some control APIs. By default, this server runs on port 4900. Some plugins, especially source plugins, may expose other servers that run on different ports. Configurations for these plugins are independent of the core API. For example, to shut down Data Prepper, you can run the following curl request:
+All OpenSearch Data Prepper instances expose a server with some control APIs. By default, this server runs on port 4900. Some plugins, especially source plugins, may expose other servers that run on different ports. Configurations for these plugins are independent of the core API. For example, to shut down Data Prepper, you can run the following curl request:
 
 ```
 curl -X POST http://localhost:4900/shutdown

_data-prepper/managing-data-prepper/extensions/extensions.md

@@ -1,14 +1,14 @@
 ---
 layout: default
 title: Extensions
-parent: Managing Data Prepper
+parent: Managing OpenSearch Data Prepper
 has_children: true
 nav_order: 18
 ---
 
 # Extensions
 
-Data Prepper extensions provide Data Prepper functionality outside of core Data Prepper pipeline components.
+OpenSearch Data Prepper extensions provide Data Prepper functionality outside of core Data Prepper pipeline components.
 Many extensions provide configuration options that give Data Prepper administrators greater flexibility over Data Prepper's functionality.
 
 Extension configurations can be configured in the `data-prepper-config.yaml` file under the `extensions:` YAML block.

_data-prepper/managing-data-prepper/extensions/geoip-service.md

@@ -3,12 +3,12 @@ layout: default
 title: geoip_service
 nav_order: 5
 parent: Extensions
-grand_parent: Managing Data Prepper
+grand_parent: Managing OpenSearch Data Prepper
 ---
 
 # geoip_service
 
-The `geoip_service` extension configures all [`geoip`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/geoip) processors in Data Prepper.
+The `geoip_service` extension configures all [`geoip`]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/geoip) processors in OpenSearch Data Prepper.
 
 ## Usage
 

_data-prepper/managing-data-prepper/managing-data-prepper.md

@@ -1,10 +1,10 @@
 ---
 layout: default
-title: Managing Data Prepper
+title: Managing OpenSearch Data Prepper
 has_children: true
 nav_order: 20
 ---
 
-# Managing Data Prepper
+# Managing OpenSearch Data Prepper
 
-You can perform administrator functions for Data Prepper, including system configuration, interacting with core APIs, Log4j configuration, and monitoring. You can set up peer forwarding to coordinate multiple Data Prepper nodes when using stateful aggregation. 
+You can perform administrator functions for OpenSearch Data Prepper, including system configuration, interacting with core APIs, Log4j configuration, and monitoring. You can set up peer forwarding to coordinate multiple Data Prepper nodes when using stateful aggregation. 

_data-prepper/managing-data-prepper/monitoring.md

@@ -1,13 +1,13 @@
 ---
 layout: default
 title: Monitoring
-parent: Managing Data Prepper
+parent: Managing OpenSearch Data Prepper
 nav_order: 25
 ---
 
-# Monitoring Data Prepper with metrics
+# Monitoring OpenSearch Data Prepper with metrics
 
-You can monitor Data Prepper with metrics using [Micrometer](https://micrometer.io/). There are two types of metrics: JVM/system metrics and plugin metrics. [Prometheus](https://prometheus.io/) is used as the default metrics backend.
+You can monitor OpenSearch Data Prepper with metrics using [Micrometer](https://micrometer.io/). There are two types of metrics: JVM/system metrics and plugin metrics. [Prometheus](https://prometheus.io/) is used as the default metrics backend.
 
 ## JVM and system metrics
 

_data-prepper/managing-data-prepper/peer-forwarder.md

@@ -2,12 +2,12 @@
 layout: default
 title: Peer forwarder
 nav_order: 12
-parent: Managing Data Prepper
+parent: Managing OpenSearch Data Prepper
 ---
 
 # Peer forwarder
 
-Peer forwarder is an HTTP service that performs peer forwarding of an `event` between Data Prepper nodes for aggregation. This HTTP service uses a hash-ring approach to aggregate events and determine which Data Prepper node it should handle on a given trace before rerouting it to that node. Currently, peer forwarder is supported by the `aggregate`, `service_map_stateful`, and `otel_traces_raw` [processors]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/processors/).
+Peer forwarder is an HTTP service that performs peer forwarding of an `event` between OpenSearch Data Prepper nodes for aggregation. This HTTP service uses a hash-ring approach to aggregate events and determine which Data Prepper node it should handle on a given trace before rerouting it to that node. Currently, peer forwarder is supported by the `aggregate`, `service_map_stateful`, and `otel_traces_raw` [processors]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/processors/).
 
 Peer Forwarder groups events based on the identification keys provided by the supported processors. For `service_map_stateful` and `otel_traces_raw`, the identification key is `traceId` by default and cannot be configured. The `aggregate` processor is configured using the `identification_keys` configuration option. From here, you can specify which keys to use for Peer Forwarder. See [Aggregate Processor page](https://github.com/opensearch-project/data-prepper/tree/main/data-prepper-plugins/aggregate-processor#identification_keys) for more information about identification keys.