Amazon Redshift now provides a serverless option (preview) to run and scale analytics without having to provision and manage data warehouse clusters. With Amazon Redshift Serverless, all users including data analysts, developers, and data scientists can now use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver best-in-class performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications.

Today we announced Amazon MSK Serverless in public preview, a new type of Amazon MSK cluster that makes it easier for developers to run Apache Kafka without having to manage its capacity. MSK Serverless automatically provisions and scales compute and storage resources and offers throughput-based pricing, so you can use Apache Kafka on demand and pay for the data you stream and retain.

AWS Lake Formation is excited to announce the general availability of three new capabilities that simplify building, securing, and managing data lakes. First, Lake Formation Governed Tables, a new type of table on Amazon S3, that simplifies building resilient data pipelines with multi-table transaction support. As data is added or changed, Lake Formation automatically manages conflicts and errors to ensure that all users see a consistent view of the data. This eliminates the need for customers to create custom error handling code or batch their updates. Second, Governed Tables monitor and automatically optimize how data is stored so query times are consistent and fast. Third, in addition to table and columns, Lake Formation now supports row and cell-level permissions, making it more easily to restrict access to sensitive information by granting users access to only the portions of the data they are allowed to see. Governed Tables, row and cell-level permissions are now supported through Amazon Athena, Amazon Redshift Spectrum, AWS Glue, and Amazon QuickSight.

Amazon Kinesis Data Streams is a serverless streaming data service that makes it easy to capture, process, and store streaming data at any scale. Kinesis Data Streams On-Demand is a new capacity mode for Kinesis Data Streams, capable of serving gigabytes of write and read throughput per minute without capacity planning. You can create a new on-demand data stream or convert an existing data stream into the on-demand mode with a single-click and never have to provision and manage servers, storage, or throughput. In the on-demand mode you pay for throughput consumed rather than for provisioned resources, making it easy to balance costs and performance.

Starting today, the new Amazon EC2 C7g instances powered by the latest generation custom-designed AWS Graviton3 processors are available in preview. Amazon EC2 C7g instances will provide the best price performance in Amazon EC2 for compute-intensive workloads such as high performance computing (HPC), gaming, video encoding, and CPU-based machine learning inference. These instances are the first in the cloud to feature the cutting edge DDR5 memory technology, which provides 50% more bandwidth compared to DDR4 memory. C7g instances provide 20% higher networking bandwidth compared to previous generation C6g instances based on AWS Graviton2 processors. They also support Elastic Fabric Adapter (EFA) for applications such as high performance computing that require high levels of inter-node communication.

Amazon SageMaker Canvas is a new capability of Amazon SageMaker that enables business analysts to create accurate machine learning (ML) models and generate predictions using a visual, point-and-click interface, no coding required.

We are happy to announce the preview of Amazon EMR Serverless, a new serverless option in Amazon EMR that makes it easy and cost-effective for data engineers and analysts to run petabyte-scale data analytics in the cloud. Amazon EMR is a cloud big data platform used by customers to run large-scale distributed data processing jobs, interactive SQL queries, and machine learning applications using open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto. With EMR Serverless, customers can run applications built using these frameworks with a few clicks, without having to configure, optimize, or secure clusters. EMR Serverless automatically provisions and scales the compute and memory resources required by the application, and customers only pay for the resources they use.

Today, we are announcing AWS IoT TwinMaker, a new service that makes it faster and easier for developers to create and use digital twins of real-world systems to monitor and optimize operations. Digital twins are virtual representations of physical systems such as buildings, factories, production lines, and equipment that are regularly updated with real-world data to mimic the structure, state, and behavior of the systems they represent. Although digital twin use cases are many and diverse, most customers want to get started by easily using their existing data to get a deeper understanding of their operations.

Today, we are announcing AWS IoT FleetWise, a new service that makes it easier and more cost effective for automakers to collect, transform, and transfer vehicle data to the cloud in near-real time. Once the data is in the cloud, automakers can use it for tasks like remotely diagnosing issues in individual vehicles, analyzing vehicle fleet health to help prevent potential warranty claims and recalls, and collecting rich sensor data for training machine learning models that improve autonomous driving and advanced driver assistance systems (ADAS).

Today, Contact Lens for Amazon Connect announced a new machine learning (ML) capability called call summarization that helps businesses improve the productivity of contact center agents and managers, so they can focus on providing excellent customer experiences.