Change first instances of data prepper to open search data prepper (opensearch-project#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