Add selective download feature to Data Prepper sources section

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

@@ -9,7 +9,6 @@ nav_order: 40
 
 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 can read objects from S3 buckets using an [Amazon Simple Queue Service (SQS)](https://aws.amazon.com/sqs/) (Amazon SQS) queue and [Amazon S3 Event Notifications](https://docs.aws.amazon.com/AmazonS3/latest/userguide/NotificationHowTo.html).
@@ -20,15 +19,14 @@ The following diagram shows the overall architecture of the components involved.
 
 <img src="{{site.url}}{{site.baseurl}}/images/data-prepper/s3-source/s3-architecture.jpg" alt="S3 source architecture">{: .img-fluid}
 
-The flow of data is as follows.
+The component data flow is as follows:
 
 1. A system produces logs into the S3 bucket.
 2. S3 creates an S3 event notification in the SQS queue.
 3. Data Prepper polls Amazon SQS for messages and then receives a message.
 4. Data Prepper downloads the content from the S3 object.
 5. Data Prepper sends a document to OpenSearch for the content in the S3 object.
 
-
 ## Pipeline overview
 
 Data Prepper supports reading data from S3 using the [`s3` source]({{site.url}}{{site.baseurl}}/data-prepper/pipelines/configuration/sources/s3/).
@@ -44,7 +42,6 @@ Before Data Prepper can read log data from S3, you need the following prerequisi
 - An S3 bucket.
 - A log producer that writes logs to S3. The exact log producer will vary depending on your specific use case, but could include writing logs to S3 or a service such as Amazon CloudWatch.
 
-
 ## Getting started
 
 Use the following steps to begin loading logs from S3 with Data Prepper.
@@ -57,8 +54,7 @@ Use the following steps to begin loading logs from S3 with Data Prepper.
 
 ### Setting permissions for Data Prepper
 
-To view S3 logs, Data Prepper needs access to Amazon SQS and S3.
-Use the following example to set up permissions:
+To view S3 logs, Data Prepper needs access to Amazon SQS and S3. Use the following example to set up permissions:
 
 ```json
 {
@@ -88,12 +84,13 @@ Use the following example to set up permissions:
     ]
 }
 ```
+{% include copy-curl.html %}
 
 If your S3 objects or SQS queues do not use KMS, you can remove the `kms:Decrypt` permission.
 
 ### SQS dead-letter queue
 
-The are two options for how to handle errors resulting from processing S3 objects.
+The following two options can be used to handle S3 object processing errors:
 
 - Use an SQS dead-letter queue (DLQ) to track the failure. This is the recommended approach.
 - Delete the message from SQS. You must manually find the S3 object and correct the error.
@@ -104,8 +101,8 @@ The following diagram shows the system architecture when using SQS with DLQ.
 
 To use an SQS dead-letter queue, perform the following steps:
 
-1. Create a new SQS standard queue to act as your DLQ.
-2. Configure your SQS's redrive policy [to use your DLQ](https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-configure-dead-letter-queue.html). Consider using a low value such as 2 or 3 for the "Maximum Receives" setting.
+1. Create a new SQS standard queue to act as the DLQ.
+2. Configure your SQS re-drive policy [to use DLQ](https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-configure-dead-letter-queue.html). Consider using a low value such as 2 or 3 for the **Maximum Receives** setting.
 3. Configure the Data Prepper `s3` source to use `retain_messages` for `on_error`. This is the default behavior.
 
 ## Pipeline design
@@ -125,6 +122,7 @@ s3-log-pipeline:
          queue_url: "arn:aws:sqs:<YOUR-REGION>:<123456789012>:<YOUR-SQS-QUEUE>"
          visibility_timeout: "2m"
 ```
+{% include copy-curl.html %}
 
 Configure the following options according to your use case:
 
@@ -164,17 +162,67 @@ s3-log-pipeline:
          password: "admin"
          index: s3_logs
 ```
+{% include copy-curl.html %}
 
 ## Multiple Data Prepper pipelines
 
-We recommend that you have one SQS queue per Data Prepper pipeline. In addition, you can have multiple nodes in the same cluster reading from the same SQS queue, which doesn't require additional configuration with Data Prepper.
+It is recommended that you have one SQS queue per Data Prepper pipeline. In addition, you can have multiple nodes in the same cluster reading from the same SQS queue, which doesn't require additional Data Prepper configuration.
 
 If you have multiple pipelines, you must create multiple SQS queues for each pipeline, even if both pipelines use the same S3 bucket.
 
 ## Amazon SNS fanout pattern
 
 To meet the scale of logs produced by S3, some users require multiple SQS queues for their logs. You can use [Amazon Simple Notification Service](https://docs.aws.amazon.com/sns/latest/dg/welcome.html) (Amazon SNS) to route event notifications from S3 to an SQS [fanout pattern](https://docs.aws.amazon.com/sns/latest/dg/sns-common-scenarios.html). Using SNS, all S3 event notifications are sent directly to a single SNS topic, where you can subscribe to multiple SQS queues.
 
-To make sure that Data Prepper can directly parse the event from the SNS topic, configure [raw message delivery](https://docs.aws.amazon.com/sns/latest/dg/sns-large-payload-raw-message-delivery.html) on the SNS to SQS subscription. Setting this option will not affect other SQS queues that are subscribed to that SNS topic.
+To make sure that Data Prepper can directly parse the event from the SNS topic, configure [raw message delivery](https://docs.aws.amazon.com/sns/latest/dg/sns-large-payload-raw-message-delivery.html) on the SNS-to-SQS subscription. Applying this option does not affect other SQS queues subscribed to the SNS topic.
+
+## Filtering and retrieving data using Amazon S3 Select
+
+If a pipeline uses an S3 source, you can use SQL expressions to perform filtering and computations on the contents of S3 objects before ingesting them into the pipeline.
 
+The `s3_select` option supports objects in the [Parquet File Format](https://parquet.apache.org/docs/). It also works with objects that are compressed with GZIP or BZIP2 (for CSV and JSON objects only) and supports columnar compression for the Parquet File Format using GZIP and Snappy.
 
+Refer to [Filtering and retrieving data using Amazon S3 Select](https://docs.aws.amazon.com/AmazonS3/latest/userguide/selecting-content-from-objects.html) and [SQL reference for Amazon S3 Select](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-select-sql-reference.html) for comprehensive information about using Amazon S3 Select.
+{: .note}
+
+The following example pipeline retrieves all data S3 objects encoded in the Parquet File Format:
+
+```json
+pipeline:
+  source:
+    s3:
+      s3_select:
+        expression: "select * from s3object s"  
+        input_serialization: parquet
+      notification_type: "sqs"
+...
+```
+{% include copy-curl.html %}
+
+The following example pipeline retrieves only the first 10,000 records in the objects:
+
+```json
+pipeline:
+  source:
+    s3:
+      s3_select:
+        expression: "select * from s3object s LIMIT 10000"
+        input_serialization: parquet
+      notification_type: "sqs"
+...
+```
+{% include copy-curl.html %}
+
+The following example pipeline retrieves records from S3 objects that have a `data_value` in the given range of 200--500:
+
+```json
+pipeline:
+  source:
+    s3:
+      s3_select:
+        expression: "select s.* from s3object s where s.data_value > 200 and s.data_value < 500 "
+        input_serialization: parquet
+      notification_type: "sqs"
+...
+```
+{% include copy-curl.html %}