# Welcome
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## Amazon S3
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## Amazon S3 Control
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 AWS S3 Control provides access to Amazon S3 control plane actions. 

## Amazon S3 Files
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S3 Files makes S3 buckets accessible as high-performance file systems powered by EFS. This service enables file system interface access to S3 data with sub-millisecond latencies through mount targets, supporting AI/ML workloads, media processing, and hybrid storage workflows that require both file system and object storage access to the same data.

## Amazon S3 on Outposts
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Amazon S3 on Outposts provides access to S3 on Outposts operations.

## Amazon S3 Tables
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An Amazon S3 table represents a structured dataset consisting of tabular data in [Apache Parquet](https://parquet.apache.org/docs/) format and related metadata. This data is stored inside an S3 table as a subresource. All tables in a table bucket are stored in the [Apache Iceberg](https://iceberg.apache.org/docs/latest/) table format. Through integration with the [AWS Glue Data Catalog](https://docs.aws.amazon.com/https:/docs.aws.amazon.com/glue/latest/dg/catalog-and-crawler.html) you can interact with your tables using AWS analytics services, such as [Amazon Athena](https://docs.aws.amazon.com/https:/docs.aws.amazon.com/athena/) and [Amazon Redshift](https://docs.aws.amazon.com/https:/docs.aws.amazon.com/redshift/). Amazon S3 manages maintenance of your tables through automatic file compaction and snapshot management. For more information, see [Amazon S3 table buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-tables-buckets.html).

## Amazon S3 Vectors
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Amazon S3 vector buckets are a bucket type to store and search vectors with sub-second search times. They are designed to provide dedicated API operations for you to interact with vectors to do similarity search. Within a vector bucket, you use a vector index to organize and logically group your vector data. When you make a write or read request, you direct it to a single vector index. You store your vector data as vectors. A vector contains a key (a name that you assign), a multi-dimensional vector, and, optionally, metadata that describes a vector. The key uniquely identifies the vector in a vector index.