Amazon Simple Storage Service (S3) is the largest and most performant object storage service for structured and unstructured data and the storage service of choice to build a data lake. With Amazon S3, you can cost-effectively build and scale a data lake of any size in a secure environment where data is protected by 99.999999999% (11 9s) of durability.

With a data lake built on Amazon S3, you can use native AWS services to run big data analytics, artificial intelligence (AI), machine learning (ML), high-performance computing (HPC) and media data processing applications to gain insights from your unstructured data sets. Using Amazon FSx for Lustre, you can launch file systems for HPC and ML applications, and process large media workloads directly from your data lake. You also have the flexibility to use your preferred analytics, AI, ML, and HPC applications from the Amazon Partner Network (APN). Because Amazon S3 supports a wide range of features, IT managers, storage administrators, and data scientists are empowered to enforce access policies, manage objects at scale and audit activities across their S3 data lakes.

Amazon S3 hosts more than 10,000 data lakes for household brands such as Netflix, Airbnb, Sysco, Expedia, GE, and FINRA, who are using them to securely scale with their needs and to discover business insights every minute.

Amazon S3 lets you manage data with object-level granularity, as well as at the bucket and account levels. You can append metadata tags to an object and use them to organize data in ways that work for your business. You can also organize objects by prefixes and buckets. With these capabilities, quickly point to one or a group of objects to replicate across regions, restrict access, transfer to cheaper storage classes, among other tasks.

With S3 Batch Operations, you can take action across billions of objects with a single API request or a few clicks in the S3 Management Console, and audit the progress of your requests. Modify object properties and metadata, copy objects between buckets, replace tag sets, configure access controls, restore archives from S3 Glacier, and invoke AWS Lambda functions – in minutes instead of months.

Use bucket policies, object tags, and access control lists (ACLs) to restrict access to specific buckets and objects. You can also use AWS Identity and Access Management to define user access within an AWS account. Organizations that need to block all access requests to their data can configure S3 Block Public Access to enforce a “no public access” policy for a specific bucket of objects or an entire AWS account.

All S3 customers can store data across 6 distinct storage classes that are designed to accommodate different access requirements at corresponding costs. Use S3 Storage Class Analysis to learn the access patterns to your data. Then, configure lifecycle policies to transfer less frequently accessed objects to cheaper classes or archive them in S3 Glacier or S3 Glacier Deep Archive for maximum savings.

With S3 reporting tools, quickly discover who is requesting access to what data and from where, audit object metadata (such as storage class, retention date, business unit, and encryption status), monitor usage and costs, learn access patterns, among other activities related to your S3 resources. With these insights, make changes to optimize your data lake and the applications that rely on it, and reduce costs.