Amazon EFS Archive is a new storage class that is cost-optimized for long-lived file data that is accessed a few times a year or less. With EFS Archive, you now have a cost-effective way to retain even your coldest data so that it’s always available to power new business insights. In addition, as part of this launch, the storage price for the existing Amazon EFS Infrequent Access (IA) storage class is being reduced by 36% to make EFS an even better solution for sharing a wide range of data.

We are excited to announce Amazon CloudWatch Logs Infrequent Access (Logs IA), a new log class for cost-effectively consolidating all your logs natively on AWS, helping to improve visibility into your overall application health. CloudWatch Logs IA offers a subset of CloudWatch Logs’ capabilities including managed ingestion, cross-account log analytics, and encryption with a lower per GB ingestion price making Logs IA ideal for ad-hoc querying and after-the-fact forensic analysis on infrequently accessed logs.

Today we are announcing customizable EC2 rightsizing recommendations within AWS Compute Optimizer. With this launch you can now adjust both CPU headroom and thresholds, configure a new 32-day lookback period option, and set instance family preferences. These settings can be configured at the organization, account, or regional level.

AWS Analytics services, including Amazon QuickSight, Amazon Redshift, AWS Lake Formation, and Amazon S3 via S3 Access Grants, now use trusted identity propagation with AWS IAM Identity Center to manage and audit access to data and resources based on user identity. This new capability passes identity information between connected business intelligence and data analytics applications. Administrators define access to their service based on a common set of users and groups in the customer’s chosen identity provider. Auditors can track users’ access across services. Analytics users benefit from an improved single sign-on experience when accessing data.

Amazon Redshift now supports multi-data warehouse writes through data sharing in public preview. You can start writing to Redshift databases from multiple Redshift data warehouses in just a few clicks. The written data is available to all warehouses as soon as it is committed. This allows teams to flexibly scale compute by adding warehouses of different types and sizes based on their write workloads’ price-performance needs, isolate compute to more easily meet SLAs, and easily and securely collaborate with other teams.

Amazon Monitron is an end-to-end system that uses machine learning to detect abnormal conditions in industrial equipment to enable predictive maintenance. Amazon Monitron includes wireless sensors to capture vibration and temperature data; gateways to automatically transfer data to the AWS Cloud; the Amazon Monitron service that analyzes the data for abnormal machine patterns using ML, and a companion mobile app to set up the devices and receive reports on operating behavior and alerts to potential failures in machinery.

Amazon Redshift enhances Redshift ML to support large language models (LLM). Amazon Redshift ML enables customers to create, train, and deploy machine learning models using familiar SQL commands. Now, you can leverage pretrained publicly available LLMs in Amazon SageMaker JumpStart aspart of Redshift ML, allowing you to bring the power of LLMs to analytics. For example, you can make inferences on your product feedback data in Amazon Redshift, use LLMs to summarize feedback, perform entity extraction, sentiment analysis and product feedback classification.

AWS CloudFormation now supports Git sync, enabling customers to synchronize their stacks from a CloudFormation template stored in a remote Git repository. A CloudFormation template describes your desired resources and their dependencies so you can launch and configure them together as a stack. 

Amazon CloudWatch now supports data querying from multiple sources, enabling you to gain visibility across your hybrid and multicloud metrics in a single view. With this feature, you can consolidate and visualize metrics from sources such as Amazon OpenSearch Service, Amazon Managed Service for Prometheus, Azure Monitor, your own custom data sources, and query those metrics in real time, increasing visibility into your application health and helping you resolve critical events faster.

Amazon Personalize is excited to announce Content Generator, a new generative AI powered capability that makes recommendations more compelling by identifying thematic connections between recommended items. Content Generator will assist customers in crafting taglines to create a more engaging browsing experience.