AWS Storage Blog
Tag: Amazon S3 Lifecycle
How Amagi uses Amazon S3 Glacier Instant Retrieval to optimize media storage costs
Amagi is a global leader in SaaS technology providing end-to-end cloud-managed live and on-demand video infrastructure for TV and Over-the-Top (OTT) media services for over 700 playout and 2,000 ad-insertion channels across 40 countries. Amagi enables TV networks and content owners to launch, manage, distribute, and monetize live, linear, and on-demand channels across cable, OTT, […]
Manage Amazon S3 storage costs granularly and at scale using S3 Intelligent-Tiering
Cost-effective data storage is critical when building and scaling data lakes that manage and hold growing datasets. By choosing the right storage architecture, customers are empowered to quickly experiment and migrate to AWS. Amazon S3 Intelligent-Tiering is a storage class that allows customers to optimize storage costs automatically when data access patterns change without performance […]
Optimize storage costs with new Amazon S3 Lifecycle filters and actions
Managing costs is important to the bottom line of many businesses and their ability to innovate on behalf of customers. You may often find that there is some data that you use frequently, and other data that you access less frequently, if ever. Deciding how to manage costs related to storing such unevenly accessed data […]
Reduce storage costs with fewer noncurrent versions using Amazon S3 Lifecycle
Keeping multiple copies and versions of data is a tried-and-true security and data protection protocol. In the event that one version is harmed or corrupted, another is ready as a backup. While increased security with multiple versions and copies is a plus, the added storage costs of (purposefully) redundant data must be carefully considered. It […]
How to move and store your genomics sequencing data with AWS DataSync
Genomics data is expanding at a rate exceeding Moore’s law according to the National Human Genome Research Institute. As more sequencing data is produced and researchers move from genotyping to whole genome sequencing, the amount of data produced is outpacing on-premises capacity. Organizations need cloud solutions that help manage data movement, storage, and analysis. The […]
Optimize storage costs by analyzing API operations on Amazon S3
The demand for data storage has increased with the advent of a fast-paced data environment – creating, sharing, and replicating data at a large scale. Most organizations are looking for the optimal way to store their data cost-effectively, giving them everything they need from their data but without breaking the bank. Cloud storage provides flexible […]
Compressing and archiving logs to the Amazon S3 Glacier storage classes
In distributed architectures, there is often a need to preserve application logs, and for AWS customers preservation is often done via an Amazon S3 bucket. The logs may contain information on runtime transactions, error/failure states, or application metrics and statistics. These logs are later used in business intelligence to provide useful insights and generate dashboards, […]
Modernizing NASCAR’s multi-PB media archive at speed with AWS Storage
The National Association for Stock Car Auto Racing (NASCAR) is the sanctioning body for the No. 1 form of motor sports in the United States, and owns 15 of the nation’s major motorsports entertainment facilities. About 15 years ago NASCAR began to collect all the video, audio, and image assets from over the last 70+ […]
Monitor Amazon S3 activity using S3 server access logs and Pandas in Python
Monitoring and controlling access to data is often essential for security, cost optimization, and compliance. For these reasons, customers want to know what data of theirs is being accessed, when it is being accessed, and who is accessing it. With more data to monitor, large amounts of data can make it more challenging to granularly […]
Automate S3 Lifecycle rules at scale to transition data to S3 Intelligent-Tiering
The vast majority of data customers store on Amazon S3 has unknown or changing access patterns, such as data lakes, analytics, and new applications. With these use cases, a dataset can become infrequently and even rarely accessed at specific points in time. The problem is that customers don’t know how data access patterns will change […]