In September 2017, Amazon Web Services announced the new Amazon EC2 X1e instance family with the launch of the x1e.32xlarge instances. This Amazon EC2 instance size offers 3,904 GiB of DRAM available in four AWS regions, enabling customers to run larger in-memory databases such as SAP HANA. Today, five additional sizes (x1e.xlarge, x1e.2xlarge, x1e.4xlarge, x1e.8xlarge, x1e.16xlarge) of the X1e Memory Optimized instance family are being made available. Offering the highest memory per vCPU and one of the lowest price per GiB memory among Amazon EC2 instance types, the new X1e instance sizes are ideally suited for high performance databases, in-memory databases and other memory intensive enterprise applications.

Beginning today, you can use the Amazon Route 53 API to view your current limits on Route 53 resources such as hosted zones and health checks. The same APIs also return how many of each resource you’re currently using. This lets you see how close you are to reaching a service limit at any time.  

Open Neural Network Exchange (ONNX ), is an open source format to encode deep learning models. The ONNX-MXNet open source Python package is now available for developers to build and train models with other frameworks such as PyTorch, CNTK, or Caffe2, and import these models into Apache MXNet to run them for inference using MXNet’s highly optimized engine. 

Amazon Simple Email Service (SES) introduces two new features that can help to protect your sender reputation: email pausing and configuration set reputation metrics. 

Amazon Connect now provides contact flow logs for real-time details about events in your contact flows. Contact flows are used to define the path a customer takes to resolve their issue. You can view the contact flow logs to understand what is happening during the interaction, and quickly identify areas for improvement in your contact center. 

Amazon Connect contact flow import/export (beta) enables you to import contact flows into, and export contact flows from, your Amazon Connect instance. Contact flows are used to define the path a customer takes to resolve their issue. Now you can easily move your contact flows from a test environment to a production environment, copy them from one region to another as you expand your customer service organization, or share contact flows with others. Exported contact flows can be used to create backup copies and used as version control for your contact flows. 

You can now monitor and report on agent activity in your Amazon Connect contact center in real-time, with the data provided by Amazon Connect agent event streams. The data can be used to create dashboards in Amazon Connect that display agent information and activities, integrate the event streams into workforce management (WFM) solutions, and configure alerting tools to notify you about specific agent activity. 

AWS Database Migration Service (DMS) now supports installing a local replication agent on-premises and can now migrate data to the AWS cloud with AWS Snowball , a petabyte-scale data transport solution. Snowball uses secure physical appliances to transfer large amounts of data into and out of the AWS cloud.  

Customers can now query Amazon S3 Inventory with standard SQL language using Amazon Athena, Amazon Redshift Spectrum, and other tools such as Presto, Hive, and Spark. You can easily get started by pointing Amazon Athena to the S3 Inventory report in ORC or CSV format with a few clicks, run ad hoc queries, and get results in seconds. This is available in all AWS Regions where Athena is available. Learn more by visiting our developer guide . 

Starting today, you can use Aurora Auto Scaling to automatically add or remove Aurora Replicas in response to changes in performance metrics specified by you. Aurora Replicas share the same underlying volume as the primary instance and are well suited for read scaling. With Aurora Auto Scaling, you can specify a desired value for predefined metrics of your Aurora Replicas such as average CPU utilization or average active connections. You can also create a custom metric for Aurora Replicas to use it with Aurora Auto Scaling. Aurora Auto Scaling adjusts the number of Aurora Replicas to keep the selected metric closest to the value specified by you. For example, an increase in traffic could cause the average CPU utilization of your Aurora Replicas to go up and beyond your specified value. New Aurora Replicas are automatically added by Aurora Auto Scaling to support this increased traffic. Similarly, when CPU utilization goes below your set value, Aurora Replicas are terminated so that you don’t pay for unused DB instances.