You can now use AWS Systems Manager Session Manager to specify a single command or a sequence of commands that execute in an interactive shell experience on an Amazon EC2 or on-premises instance. This enables you to easily limit user interaction to a specific command or command sequence, which helps you manage the interactive actions a user can take. 

Beginning today, you can now use Amazon CloudWatch metrics to see the number of DNS queries served for each of your Route 53 public hosted zones. With these metrics, you can see at a glance the activity level of each hosted zone to monitor changes in traffic. Using the metric functions that Amazon CloudWatch provides, you can view the number of queries per minute, or any longer time range that Amazon CloudWatch supports.

You can now use AWS Resource Access Manager (RAM) in the AWS GovCloud (US-East) Region, an AWS Region designed to host sensitive data and regulated workloads in the cloud for customers who have U.S. federal, state, and local government compliance requirements.

With EMR release 5.26.0, Spark users benefit from all the new Spark performance optimizations introduced in EMR release 5.24.0 and 5.25.0 without the need to make any configuration or code changes. The following optimizations are enabled by default in the 5.26.0 release:

• Dynamic partition pruning – Allows the Spark engine to infer relevant partitions at runtime, saving time and compute resources both by reading less data from storage and by reducing the number of records that need to be processed.
• DISTINCT before INTERSECT – Eliminates duplicate values in each input collection prior to computing the intersection, which improves performance by reducing the amount of data shuffled between hosts.
• Flattening scalar subqueries – Helps in situations where multiple different conditions need to be applied to rows from a specific table, preventing the table from being read multiple times for each condition.
• Optimized join reorder – Dynamically reorders joins to execute smaller joins with filters first, reducing the processing required for larger subsequent joins.
• Bloom filter join – Filters table joins dynamically to include only relevant rows, reducing the amount of data processed by Spark and improving query runtime performance.

Please visit Optimizing Spark Performance documentation and the EMR 5.26.0 release notes for details on these optimizations.

Also included in EMR 5.26.0, is a Beta integration with AWS Lake Formation and new versions of Apache HBase 1.4.10, and Apache Phoenix 4.14.2. Please see Integrating Amazon EMR with AWS Lake Formation (Beta) for more details on the integration.

Amazon EMR release 5.26.0 is now available in all supported regions for Amazon EMR.

The integration between AWS Lake Formation and Amazon EMR is in Beta, and is available in the US East (N. Virginia), and US West (Oregon) regions.

You can stay up to date on EMR releases by subscribing to the feed for EMR release notes. Use the icon at the top of the EMR Release Guide to link the feed URL directly to your favorite feed reader.

Amazon ElastiCache now allows you to name your clusters up to 50 characters for cacheClusters and upto 40 characters for replicationGroups. By lifting the previous limit of 20 characters, this increase lets you use longer names that are unique to your naming standards. The cluster name must contain alphanumeric characters or hyphens, should start with a letter, and cannot end with a hyphen or contain two consecutive hyphens.

This feature is available in all regions. To get started, log in to the AWS Management Console .

AWS Single Sign-On (AWS SSO) is now available in Canada (Central) region. For a full list of the regions where AWS SSO is available, see AWS Region Table .

Starting today, inter-region Amazon Virtual Private Cloud peering (VPC peering) can be setup between the AWS Asia Pacific (Hong Kong) Region and other AWS public regions, except the AWS Govcloud (US) and China Regions.

Today, AWS Systems Manager Parameter Store launched intelligent-tiering to enable automatic parameter tier selection. If you have unknown or changing patterns of parameter count, value size or parameter policies, you can use intelligent-tiering setting to allow Parameter Store to select the standard or advanced tier for you. This selection is based on the create or update request and eliminates the need for you to change your application code.  

Amazon SageMaker now supports Amazon Elastic File System (Amazon EFS) and Amazon FSx for Lustre file systems as data sources for training machine learning models on SageMaker. Amazon FSx for Lustre is a high performance file system optimized for workloads, such as machine learning, analytics and high performance computing. Amazon EFS provides a simple, scalable, elastic file system for Linux-based workloads for use with AWS Cloud services and on-premises resources. Support for these file systems accelerates and simplifies using Amazon SageMaker to train models with data sets. The file system data source reduces the start-up time by eliminating the data download step of the training process and leveraging the various performance and throughput benefits of the file system to execute the training job faster.

Amazon SageMaker now supports a new fully managed option called Managed Spot Training for training machine learning models using Amazon EC2 Spot instances. Spot Instances let you take advantage of unused compute capacity in the AWS cloud, and as a result, you can optimize the cost of training machine learning models by up to 90% compared to on-demand instances.