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. 

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. 

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 . 

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.  

You can now resolve comments, organize comments with filters, and disable email notifications for files. This makes it easier to keep track of your feedback, disable the notifications not relevant to you, and focus on the feedback that matters.

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. 

Spot Fleet now supports a new type of scaling policy called target tracking scaling policies that you can use to set up dynamic scaling for your application in just a few simple steps. Adding Auto Scaling to your Spot Fleet is one way to maximize the benefits of AWS. Auto Scaling helps you build systems that respond to changes in demand by automatically launching or terminating Amazon EC2 instances based on conditions that you define. This dynamic scaling helps to improve application availability and reduce costs. For example, you can use Auto Scaling to automatically launch EC2 instances for your Spot Fleet when demand increases to help maintain performance, and terminate instances when demand drops to save money.

You can now record configuration changes to Classic Load Balancers (CLB) using AWS Config and track changes to load balancer configuration attributes, such as subnets, security groups, stickiness policies, listeners, and health checks. 

Today, AWS Identity and Access Management (IAM) made it easier for you to create and modify your IAM policies by using a point-and-click visual editor. You can now use the new visual editor to create and modify your AWS IAM policies in the IAM console. The visual editor guides you through granting permissions using IAM policies without requiring you to author policies in JSON (although you can still author and edit policies in JSON, if you prefer). This update to the IAM console makes it easier to grant least privilege by listing all the supported resource types and request conditions for the AWS service actions you select. Policy summaries identify unrecognized services and actions and permissions errors when you import existing policies, and now you can use the visual editor to correct them. To start using the point-and-click visual editor, navigate to the IAM console . 

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