Title

AWS re:Invent 2023 - Maximize the value of cold data with Amazon S3 Glacier (STG201)

Summary

Importance of a cold data management strategy is emphasized due to the increasing volume of data that becomes cold quickly.
Amazon S3 Glacier offers cost-effective storage solutions for cold data, with different storage classes catering to various access and retrieval needs.
Use cases for cold data include preservation of raw data, backups, compliance, and future machine learning applications.
Amazon S3 Glacier storage classes discussed are Instant Retrieval, Flexible Retrieval, and Deep Archive, each with different retrieval times and costs.
Factors to consider when choosing a storage class include retrieval speed, storage cost, and data retention period.
Amazon S3 Intelligent Tiering and Lifecycle policies help automate the movement of data to colder storage classes based on access patterns and object age.
Object size should be considered when designing lifecycle policies due to transition costs.
Customer example: Snapchat saved tens of millions by moving 2 exabytes of data to Amazon S3 Glacier Instant Retrieval.
Recent enhancements to Glacier restore processes include increased performance of standard restores from Glacier Flexible Retrieval and support for Amazon Athena to query restored data directly.
The session concludes with a call to preserve data for future AI/ML use cases, choose the right Glacier storage class, use bulk retrieval to lower costs, and optimize restores with S3 batch operations.

The increasing volume of cold data (60-80% of the world's data) highlights the need for efficient and cost-effective storage strategies.
Amazon S3 Glacier's tiered storage classes allow for fine-tuning storage costs based on data access patterns and retrieval needs.
The introduction of Amazon S3 Glacier Instant Retrieval addresses the need for immediate access to cold data, which is critical for industries like healthcare and media.
Amazon S3 Intelligent Tiering and Lifecycle policies are essential tools for managing data lifecycle without manual intervention, saving time and reducing the risk of human error.
The significant cost savings realized by Snapchat demonstrate the potential financial impact of using Amazon S3 Glacier for large-scale data storage.
The recent enhancements to the Glacier restore process, particularly the increased performance of standard restores, can significantly reduce the time required to access and process archived data.
The integration of Amazon Athena with Glacier storage classes enables direct querying of restored data, which can be transformative for analytical workloads and log analysis.
The session underscores the importance of not hastily deleting data, as cold data can become a valuable asset for future AI and ML projects.