Add website documentation for using S3 Tables with Spark Operator

website/docs/blueprints/data-analytics/spark-operator-s3tables.md

@@ -20,21 +20,17 @@ This document provides an overview of Amazon S3 Tables, its integration with Apa
 
 Amazon S3 Tables allow you to store and query data directly on Amazon S3 in tabular formats such as Parquet, ORC, or CSV. They are commonly used in data lake architectures and are often paired with query engines like Apache Spark, Hive, or Presto.
 
-## Key Benefits:
+## Key Features
 
-- Purpose-built storage for tables
+- 🛠️ Automatic Compaction: S3 Tables implements automatic compaction, which intelligently optimizes data storage in the background by combining smaller files into larger, more efficient ones. This process reduces storage costs, improves query speed, and operates continuously without manual intervention.
 
-  S3 table buckets are specifically designed for tables. Table buckets provide higher transactions per second (TPS) and better query throughput compared to self-managed tables in S3 general purpose buckets. Table buckets deliver the same durability, availability, and scalability as other Amazon S3 bucket types.
+- 🔄 Table Maintenance: It offers critical maintenance tasks like snapshot management and unreferenced file removal. This continuous optimization ensures that tables remain performant and cost-effective without manual intervention, reducing operational overhead and allowing teams to focus on data insights.
 
-- Built-in support for Apache Iceberg
-
-  Tables in Amazon S3 table buckets are stored in Apache Iceberg format. You can query these tables using standard SQL in query engines that support Iceberg. Iceberg has a variety of features to optimize query performance, including schema evolution and partition evolution.
-
-  With Iceberg, you can change how your data is organized so that it can evolve over time without requiring you to rewrite your queries or rebuild your data structures. Iceberg is designed to help ensure data consistency and reliability through its support for transactions. To help you correct issues or perform time travel queries, you can track how data changes over time and roll back to historical versions.
-
-- Automated table optimization
+- ❄️ Apache Iceberg Support: Offers built-in support for Apache Iceberg, which simplifies managing data lakes at scale while improving query performance and reducing costs. Consider using S3 Tables for your data lake if you want to experience the following results.
 
-  To optimize your tables for querying, S3 continuously performs automatic maintenance operations, such as compaction, snapshot management, and unreferenced file removal. These operations increase table performance by compacting smaller objects into fewer, larger files. Maintenance operations also reduce your storage costs by cleaning up unused objects. This automated maintenance streamlines the operation of data lakes at scale by reducing the need for manual table maintenance. For each table and table bucket, you can customize maintenance configurations.
+- 🔒 Simplified Security: S3 Tables treat your tables as AWS resources, enabling fine-grained AWS Identity and Access Management (IAM) permissions at the table level. This simplifies data governance, enhances security, and makes access control more intuitive and manageable with your familiar AWS services.
+
+- ⚡ Enhanced Performance: Amazon S3 Tables introduce a new type of bucket, purpose-built for storing Apache Iceberg tables. Table buckets deliver up to 3x faster query performance and up to 10x higher transactions per second compared to storing Iceberg tables in general-purpose S3 buckets. This performance enhancement supports high-frequency updates, real-time ingestion, and more demanding workloads, ensuring scalability and responsiveness as data volumes grow.
 
 <CollapsibleContent header={<h2><span>Deploying the Solution</span></h2>}>