diff --git a/_data-prepper/index.md b/_data-prepper/index.md
index 0c7a228b27..a7f51a8514 100644
--- a/_data-prepper/index.md
+++ b/_data-prepper/index.md
@@ -14,9 +14,9 @@ redirect_from:
 
 # Data Prepper
 
-Data Prepper is a server-side data collector capable of filtering, enriching, transforming, normalizing, and aggregating data for downstream analytics and visualization.
+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.
 
-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.
+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}}/observability-plugin/trace/index/) can help you visualize event flows and identify performance problems. [Log analytics]({{site.url}}{{site.baseurl}}/observability-plugin/log-analytics/) equips you with tools to enhance your search capabilities, conduct comprehensive analysis, and gain insights into your applications' performance and behavior.
 
 ## Concepts
 
diff --git a/_ingest-pipelines/index.md b/_ingest-pipelines/index.md
index 8d1ed8d7bb..f0b52ea152 100644
--- a/_ingest-pipelines/index.md
+++ b/_ingest-pipelines/index.md
@@ -16,7 +16,15 @@ An _ingest pipeline_ is a sequence of _processors_ that are applied to documents
 
 Processors are customizable tasks that run in a sequential order as they appear in the request body. This order is important, as each processor depends on the output of the previous processor. The modified documents appear in your index after the processors are applied.
 
-Ingest pipelines can only be managed using [ingest API operations]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/).
+## OpenSearch ingest pipelines compared to Data Prepper
+
+OpenSeach ingest pipelines run within the OpenSearch cluster, whereas [Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/) is an external component that runs on the OpenSearch cluster. 
+
+OpenSearch ingest pipelines perform actions on indexes and are preferred for use cases involving pre-processing simple datasets, [machine learning (ML) processors]({{site.url}}{{site.baseurl}}/ingest-pipelines/processors/sparse-encoding/), and [vector embedding processors]({{site.url}}{{site.baseurl}}/ingest-pipelines/processors/text-image-embedding/). OpenSearch ingest pipelines are recommended for simple data pre-processing and small datasets. 
+
+Data Prepper is recommended for any data processing tasks it supports, particularly when dealing with large datasets and complex data pre-processing requirements. It streamlines the process of transferring and fetching large datasets while providing robust capabilities for intricate data preparation and transformation operations. Refer to the [Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/) documentation for more information.      
+
+OpenSearch ingest pipelines can only be managed using [Ingest API operations]({{site.url}}{{site.baseurl}}/api-reference/ingest-apis/index/).
 {: .note}
 
 ## Prerequisites 
diff --git a/_ingest-pipelines/processors/grok.md b/_ingest-pipelines/processors/grok.md
index 981ec34bbd..c47865a8dc 100644
--- a/_ingest-pipelines/processors/grok.md
+++ b/_ingest-pipelines/processors/grok.md
@@ -10,6 +10,9 @@ nav_order: 140
 
 The `grok` processor is used to parse and structure unstructured data using pattern matching. You can use the `grok` processor to extract fields from log messages, web server access logs, application logs, and other log data that follows a consistent format.
 
+This documentation describes using the `grok` processor in OpenSearch ingest pipelines. Consider using the [Data Prepper `grok` processor]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/processors/grok/), which runs on the OpenSearch cluster, if your use case involves large or complex datasets.
+{: .note}
+
 ## Grok basics
 
 The `grok` processor uses a set of predefined patterns to match parts of the input text. Each pattern consists of a name and a regular expression. For example, the pattern `%{IP:ip_address}` matches an IP address and assigns it to the field `ip_address`. You can combine multiple patterns to create more complex expressions. For example, the pattern `%{IP:client} %{WORD:method} %{URIPATHPARM:request} %{NUMBER:bytes %NUMBER:duration}` matches a line from a web server access log and extracts the client IP address, the HTTP method, the request URI, the number of bytes sent, and the duration of the request.