Amazon Redshift Data Sharing, a secure and easy way to share live data across Redshift clusters, is now generally available. Data Sharing enables instant, granular, and high-performance data access across Amazon Redshift clusters within an AWS account, without the need to copy or move data. Data Sharing provides live access to the data so that your users always see most up-to-date and consistent information as it is updated in the data warehouse. Data Sharing can be used on your Amazon Redshift RA3 clusters at no additional cost.

An Amazon Aurora Global Database is a single database that spans multiple AWS Regions, enabling disaster recovery from region-wide outages and low latency global reads. With today’s launch, the feature is available in the AWS Americas (São Paulo) and AWS Europe (Stockholm) Regions.  

Amazon Kinesis Video Streams (KVS) updates the service quota model for its media playback APIs to throttle based on the number of fragments requested rather than the number of playback sessions. This gives developers the flexibility to enable up to 10x more simultaneous playback sessions for applications that use Kinesis Video Streams for live or on-demand playback.

Amazon Redshift Cross-database queries provide ability to query across databases in a Redshift cluster. This feature is now generally available in all regions where Amazon Redshift RA3 node types are available. With Cross-database queries, you can seamlessly query data from any database in the cluster, regardless of which database you are connected to. Cross-database queries can eliminate data copies and simplify your data organization to support multiple business groups on the same cluster. Cross-database queries can be used on your RA3 clusters at no additional cost.

Starting today Amazon RDS for PostgreSQL supports Cross-Region Automated Backups. This feature extends the existing Amazon RDS backup functionality, giving you the ability to setup automatic replication of system snapshots and transaction logs from a primary AWS Region to a secondary AWS Region.  

Amazon WorkSpaces is now available in the Asia Pacific (Mumbai) region, increasing the number of AWS Regions in which WorkSpaces is available to 14. This expansion into a new AWS Region allows you to provision WorkSpaces closer to your users and data, providing a more responsive experience. Additionally, you can better meet data sovereignty requirements without the cost and complexity of building on-premises Virtual Desktop Infrastructure (VDI). For companies that provide services such as customer support, development, or back office services like accounting or IT support, using Amazon WorkSpaces in Asia Pacific (Mumbai) can enable a more reliable way of giving users a persistent desktop while helping you to reduce the risk of data leakage, with the closest possible AWS Region.

AWS Graviton2-based database instances for Amazon Relational Database Service (RDS) for PostgreSQL are now supported in: US West (N. California), Canada (Central), South America (São Paulo), and Europe (London). Graviton2 instances provide up to 35% performance improvement and up to 52% price/performance improvement over comparable current generation x86-based instances for RDS open source databases depending on database engine, version, and workload. You can launch these database instances when using Amazon RDS for PostgreSQL.

AWS Lambda now supports four new Trusted Advisor checks to help customers optimize the cost, security, function runtime version and fault tolerance of their Lambda functions.  

Amazon Forecast uses machine learning (ML) to generate more accurate demand forecasts, without requiring any prior ML experience. Forecast brings the same technology used at Amazon.com to developers as a fully managed service, removing the need to manage resources or rebuild your systems.

When you generate data quality profiles on your datasets, DataBrew now publishes a visual dashboard on the AWS Glue DataBrew console with 40+ statistics and visualizations listed in a tabular format for easy comparison. Understanding data quality is key to the success of your analytics and machine learning projects. With this new capability in DataBrew, it’s easy to spot anomalies in data distributions, detect outliers, understand skews, and more for datasets varying from a few thousand rows to tens of millions of rows and varying file formats.