Amazon Connect now supports Outbound Campaign calling to Poland in the Europe (Frankfurt) and Europe (London) regions, making it easier to proactively communicate across voice, SMS, and email for use cases such as delivery notifications, marketing promotions, appointment reminders, or debt collection, etc. Outbound Campaigns offers real-time audience segmentation using unified customer data from Customer Profiles, along with an intuitive UI for campaign management, targeting, and analytics. It eliminates the need for complex integrations or direct AWS Console access. Outbound Campaigns can be enabled within the AWS Connect Console.

With Outbound Campaigns, Amazon Connect becomes the only CCaaS platform offering native, seamless support for both inbound and outbound engagement across voice and digital channels in a single, business-friendly application. To learn more, visit our webpage.
 

 

​Amazon Connect now supports Outbound Campaign calling to Poland in the Europe (Frankfurt) and Europe (London) regions, making it easier to proactively communicate across voice, SMS, and email for use cases such as delivery notifications, marketing promotions, appointment reminders, or debt collection, etc. Outbound Campaigns offers real-time audience segmentation using unified customer data from Customer Profiles, along with an intuitive UI for campaign management, targeting, and analytics. It eliminates the need for complex integrations or direct AWS Console access. Outbound Campaigns can be enabled within the AWS Connect Console. With Outbound Campaigns, Amazon Connect becomes the only CCaaS platform offering native, seamless support for both inbound and outbound engagement across voice and digital channels in a single, business-friendly application. To learn more, visit our webpage.    

AWS WAF is expanding the availability of its enhanced rate-based rules feature to customers in the following AWS Regions: Asia Pacific (Hyderabad), Australia (Melbourne), Israel (Tel Aviv), and Asia Pacific (Malaysia). This feature supports additional request parameters for rate-based rules, including cookies and other HTTP headers. Additionally, customers can now create composite keys based on up to 5 request parameters, providing more granular options for managing and securing web application traffic.

Customers could already use WAF rate-based rules to automatically block requests from IP addresses that make large numbers of requests within a short period of time until the rate of requests falls below a customer-defined threshold. Now, WAF customers can aggregate requests by combining IP addresses with other request parameters (“keys”). Supported keys include cookies and other request headers, query strings or query arguments, cookies, label namespaces, and HTTP methods. By combining multiple request parameters into a single composite key, customers can detect and mitigate potential threats with higher accuracy.

There is no additional cost for using this feature, however standard AWS WAF charges still apply. For more information about pricing, visit the AWS WAF Pricing page. This feature is now available in all AWS regions where WAF is supported, except the China (Beijing) and China (Ningxia) Regions. To learn more, see the AWS WAF developer guide. For more information about the service, visit the AWS WAF page.

 

​AWS WAF is expanding the availability of its enhanced rate-based rules feature to customers in the following AWS Regions: Asia Pacific (Hyderabad), Australia (Melbourne), Israel (Tel Aviv), and Asia Pacific (Malaysia). This feature supports additional request parameters for rate-based rules, including cookies and other HTTP headers. Additionally, customers can now create composite keys based on up to 5 request parameters, providing more granular options for managing and securing web application traffic. Customers could already use WAF rate-based rules to automatically block requests from IP addresses that make large numbers of requests within a short period of time until the rate of requests falls below a customer-defined threshold. Now, WAF customers can aggregate requests by combining IP addresses with other request parameters (“keys”). Supported keys include cookies and other request headers, query strings or query arguments, cookies, label namespaces, and HTTP methods. By combining multiple request parameters into a single composite key, customers can detect and mitigate potential threats with higher accuracy. There is no additional cost for using this feature, however standard AWS WAF charges still apply. For more information about pricing, visit the AWS WAF Pricing page. This feature is now available in all AWS regions where WAF is supported, except the China (Beijing) and China (Ningxia) Regions. To learn more, see the AWS WAF developer guide. For more information about the service, visit the AWS WAF page.  

AWS Billing and Cost Management Console’s Payments page now features a Payments Account Summary that helps you view your AWS account’s financial status more efficiently. Critical account balance information is now summarized in a single, easy-to-access location on your Payments page.

Payments Account Summary shows your total outstanding balance, including current and past due amounts, alongside your total unapplied funds from credit memos, unapplied cash, and Advance Pay balance. You can use these unapplied funds to pay outstanding invoices by sending remittance instructions via the email address on your invoice, or by contacting AWS Customer Service. Customers with Advance Pay will have their balances automatically applied to eligible future invoices.

To start reviewing your Payments Account Summary, visit the Payments page in the AWS Billing and Cost Management Console.
 

 

​AWS Billing and Cost Management Console’s Payments page now features a Payments Account Summary that helps you view your AWS account’s financial status more efficiently. Critical account balance information is now summarized in a single, easy-to-access location on your Payments page. Payments Account Summary shows your total outstanding balance, including current and past due amounts, alongside your total unapplied funds from credit memos, unapplied cash, and Advance Pay balance. You can use these unapplied funds to pay outstanding invoices by sending remittance instructions via the email address on your invoice, or by contacting AWS Customer Service. Customers with Advance Pay will have their balances automatically applied to eligible future invoices. To start reviewing your Payments Account Summary, visit the Payments page in the AWS Billing and Cost Management Console.    

Amazon Bedrock Data Automation (BDA) now supports extraction of custom GenAI-powered insights from audio by specifying the desired output configuration through blueprints. BDA is a GenAI-powered capability of Bedrock that streamlines the development of generative AI applications and automates workflows involving documents, images, audio, and videos. Developers can now extract custom insights from audio using blueprints, which contain their desired output including a list of field names, the data format in which the response for the field is to be extracted as well as natural language instructions for each field. Developers can get started with blueprints by either using a catalog blueprint or creating a blueprint tailored to their needs.

With this launch, developers can extract custom insights such as summaries, key topics, intents, and sentiment from a variety of voice conversations such as customer calls, clinical discussions, and meetings. Insights from BDA can be used to improve employee productivity, reduce compliance costs, and enhance customer experience, among others. For example, customers can improve productivity of sales agents by extracting insights such as summaries, key action items, and next steps from conversations between sales agents and customers.

Amazon Bedrock Data Automation is available in US West (Oregon) and US East (N. Virginia) AWS Regions.

To learn more, visit the Bedrock Data Automation page, Amazon Bedrock Pricing page, or view documentation.

 

​Amazon Bedrock Data Automation (BDA) now supports extraction of custom GenAI-powered insights from audio by specifying the desired output configuration through blueprints. BDA is a GenAI-powered capability of Bedrock that streamlines the development of generative AI applications and automates workflows involving documents, images, audio, and videos. Developers can now extract custom insights from audio using blueprints, which contain their desired output including a list of field names, the data format in which the response for the field is to be extracted as well as natural language instructions for each field. Developers can get started with blueprints by either using a catalog blueprint or creating a blueprint tailored to their needs. With this launch, developers can extract custom insights such as summaries, key topics, intents, and sentiment from a variety of voice conversations such as customer calls, clinical discussions, and meetings. Insights from BDA can be used to improve employee productivity, reduce compliance costs, and enhance customer experience, among others. For example, customers can improve productivity of sales agents by extracting insights such as summaries, key action items, and next steps from conversations between sales agents and customers. Amazon Bedrock Data Automation is available in US West (Oregon) and US East (N. Virginia) AWS Regions. To learn more, visit the Bedrock Data Automation page, Amazon Bedrock Pricing page, or view documentation.  

Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (Hyderabad) region. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high 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.

With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs.

To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.

 

​Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available in the AWS Asia Pacific (Hyderabad) region. With Amazon Redshift Serverless, all users, including data analysts, developers, and data scientists, can use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high 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. With a few clicks in the AWS Management Console, you can get started with querying data using the Query Editor V2 or your tool of choice with Amazon Redshift Serverless. There is no need to choose node types, node count, workload management, scaling, and other manual configurations. You can create databases, schemas, and tables, and load your own data from Amazon S3, access data using Amazon Redshift data shares, or restore an existing Amazon Redshift provisioned cluster snapshot. With Amazon Redshift Serverless, you can directly query data in open formats, such as Apache Parquet, in Amazon S3 data lakes. Amazon Redshift Serverless provides unified billing for queries on any of these data sources, helping you efficiently monitor and manage costs. To get started, see the Amazon Redshift Serverless feature page, user documentation, and API Reference.  

Today, Amazon Elastic Container Service (Amazon ECS) announced a new feature that allows you to easily rollback your Amazon ECS service to a previous safe state if a deployment fails.

Amazon ECS customers can configure automated failure detection and remediation for their ECS service rolling updates using deployment circuit breaker and CloudWatch Alarms. Deployment circuit breaker automatically detects task launch failures while CloudWatch alarms allow you to detect issues that result in degradation in infrastructure (e.g. cpu utilization) or performance (e.g. response latency) metrics. Previously, in scenarios where a failing deployment was not detected by either of these mechanisms, customers had to manually trigger a new deployment to roll back to a previous safe state. With today’s release, customers can simply use the new stopDeployment API action and ECS automatically rolls back the service to the last service revision that reached steady state.

You can use the new stop-deployment API to rollback deployments for your ECS services using the AWS Management Console, API, SDK, and CLI in all AWS Regions. To learn more, visit our documentation.

 

​Today, Amazon Elastic Container Service (Amazon ECS) announced a new feature that allows you to easily rollback your Amazon ECS service to a previous safe state if a deployment fails. Amazon ECS customers can configure automated failure detection and remediation for their ECS service rolling updates using deployment circuit breaker and CloudWatch Alarms. Deployment circuit breaker automatically detects task launch failures while CloudWatch alarms allow you to detect issues that result in degradation in infrastructure (e.g. cpu utilization) or performance (e.g. response latency) metrics. Previously, in scenarios where a failing deployment was not detected by either of these mechanisms, customers had to manually trigger a new deployment to roll back to a previous safe state. With today’s release, customers can simply use the new stopDeployment API action and ECS automatically rolls back the service to the last service revision that reached steady state. You can use the new stop-deployment API to rollback deployments for your ECS services using the AWS Management Console, API, SDK, and CLI in all AWS Regions. To learn more, visit our documentation.  

Today, AWS announces the preview of the Amazon Q Developer integration in GitHub. With this launch, developers can use the power of Amazon Q Developer agents for feature development, code review, and Java transformation within GitHub.com and GitHub Enterprise Cloud projects to streamline their developer experience. 

After installing the Amazon Q Developer application from GitHub, developers can use labels to assign issues to Amazon Q Developer. Then, Amazon Q Developer agents automatically implement new features, generate bug fixes, run code reviews on new pull requests, and modernize legacy Java applications, all within the GitHub projects. While generating new code, the agents will automatically use any pull request workflows, refining the solution and ensuring all checks are passing. Developers can also collaborate with the agents by directly commenting on the pull request, and Amazon Q Developer will respond with improvements, allowing all teammates to stay in the loop. By bringing Amazon Q Developer into GitHub, development teams can confidently deliver high-quality software faster while maintaining their organization’s security and compliance standards.    

The Amazon Q Developer integration is available on GitHub, and you can get started today for free—no AWS account needed. To learn more, check out the Amazon Q Developer Integrations page or read the blog.

 

​Today, AWS announces the preview of the Amazon Q Developer integration in GitHub. With this launch, developers can use the power of Amazon Q Developer agents for feature development, code review, and Java transformation within GitHub.com and GitHub Enterprise Cloud projects to streamline their developer experience. 
After installing the Amazon Q Developer application from GitHub, developers can use labels to assign issues to Amazon Q Developer. Then, Amazon Q Developer agents automatically implement new features, generate bug fixes, run code reviews on new pull requests, and modernize legacy Java applications, all within the GitHub projects. While generating new code, the agents will automatically use any pull request workflows, refining the solution and ensuring all checks are passing. Developers can also collaborate with the agents by directly commenting on the pull request, and Amazon Q Developer will respond with improvements, allowing all teammates to stay in the loop. By bringing Amazon Q Developer into GitHub, development teams can confidently deliver high-quality software faster while maintaining their organization’s security and compliance standards.    
The Amazon Q Developer integration is available on GitHub, and you can get started today for free—no AWS account needed. To learn more, check out the Amazon Q Developer Integrations page or read the blog.  

The Amazon Web Services (AWS) ODBC Driver for PostgreSQL is now generally available for use with Amazon RDS and Amazon Aurora PostgreSQL-compatible edition database clusters. This database driver provides support for faster switchover and failover times, Aurora Limitless, and authentication with AWS Secrets Manager, AWS Identity and Access Management (IAM), or Federated Identity.

The Amazon Web Services (AWS) ODBC Driver for PostgreSQL is a standalone driver and supports RDS and community PostgreSQL and Amazon Aurora PostgreSQL. You can install the aws-pgsql-odbc package for Windows, Mac or Linux by following the Getting Started instructions on GitHub. The driver relies on monitoring the database cluster status and being aware of the cluster topology to determine the new writer. This approach reduces switchover and failover times from tens of seconds to single digit seconds compared to the open-source driver.

The AWS Advanced MySQL PostgreSQL driver is released as an open-source project under the Library General Public Licence, or LGPL.
 

 

​The Amazon Web Services (AWS) ODBC Driver for PostgreSQL is now generally available for use with Amazon RDS and Amazon Aurora PostgreSQL-compatible edition database clusters. This database driver provides support for faster switchover and failover times, Aurora Limitless, and authentication with AWS Secrets Manager, AWS Identity and Access Management (IAM), or Federated Identity. The Amazon Web Services (AWS) ODBC Driver for PostgreSQL is a standalone driver and supports RDS and community PostgreSQL and Amazon Aurora PostgreSQL. You can install the aws-pgsql-odbc package for Windows, Mac or Linux by following the Getting Started instructions on GitHub. The driver relies on monitoring the database cluster status and being aware of the cluster topology to determine the new writer. This approach reduces switchover and failover times from tens of seconds to single digit seconds compared to the open-source driver. The AWS Advanced MySQL PostgreSQL driver is released as an open-source project under the Library General Public Licence, or LGPL.    

Today, AWS Organizations is making resource control policies (RCPs) available in both AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions. RCPs help you centrally establish a data perimeter across your AWS environment. With RCPs, you can centrally restrict external access to your AWS resources at scale.

RCPs are a type of authorization policy in AWS Organizations that you can use to centrally enforce the maximum available permissions for resources in your organization. For example, an RCP can help enforce the requirement that “no principal outside my organization can access Amazon S3 buckets in my organization,” regardless of the permissions granted through individual S3 bucket policies.

For an updated list of AWS services that support RCPs, refer to the list of services supporting RCPs. To learn more, visit the RCPs documentation.

 

​Today, AWS Organizations is making resource control policies (RCPs) available in both AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions. RCPs help you centrally establish a data perimeter across your AWS environment. With RCPs, you can centrally restrict external access to your AWS resources at scale. RCPs are a type of authorization policy in AWS Organizations that you can use to centrally enforce the maximum available permissions for resources in your organization. For example, an RCP can help enforce the requirement that “no principal outside my organization can access Amazon S3 buckets in my organization,” regardless of the permissions granted through individual S3 bucket policies. For an updated list of AWS services that support RCPs, refer to the list of services supporting RCPs. To learn more, visit the RCPs documentation.  

Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB now supports AWS Graviton3-based M7g database instances in Asia Pacific (Jakarta), Middle East (UAE), South America (Sao Paulo), Asia Pacific (Osaka), Asia Pacific (Melbourne), Israel (Tel Aviv), Europe (Zurich) and AWS GovCloud (US-East) Regions. R7g is now supported in Middle East (Bahrain), South America (Sao Paulo) and AWS GovCloud (US-West) Regions. Graviton3-based instances provide up to a 30% performance improvement over Graviton2-based instances on RDS for open-source databases depending on database engine, version, and workload.

Graviton3 processors offer several improvements over the second-generation Graviton2 processors. Graviton3-based M7g and R7g are the first AWS database instances to feature the latest DDR5 memory, which provides 50% more memory bandwidth compared to DDR4, enabling high-speed access to data in memory. M7g and R7g database instances offer up to 30Gbps enhanced networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). M7g and R7g on Amazon RDS for MySQL and MariaDB will also support Optimized Writes. With Optimized Writes you can improve write throughout by up to 2x at no additional cost.

M7g and R7g database instances are supported on RDS for MySQL versions 8.0 and 8.4, RDS for PostgreSQL versions 13.4 (and higher), 14.5 (and higher), 15, 16 and 17 and RDS for MariaDB versions 10.4, 10.5, 10.6, 10.11 and 11.4. For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. Get started using the Amazon RDS Management Console.
 

 

​Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB now supports AWS Graviton3-based M7g database instances in Asia Pacific (Jakarta), Middle East (UAE), South America (Sao Paulo), Asia Pacific (Osaka), Asia Pacific (Melbourne), Israel (Tel Aviv), Europe (Zurich) and AWS GovCloud (US-East) Regions. R7g is now supported in Middle East (Bahrain), South America (Sao Paulo) and AWS GovCloud (US-West) Regions. Graviton3-based instances provide up to a 30% performance improvement over Graviton2-based instances on RDS for open-source databases depending on database engine, version, and workload. Graviton3 processors offer several improvements over the second-generation Graviton2 processors. Graviton3-based M7g and R7g are the first AWS database instances to feature the latest DDR5 memory, which provides 50% more memory bandwidth compared to DDR4, enabling high-speed access to data in memory. M7g and R7g database instances offer up to 30Gbps enhanced networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). M7g and R7g on Amazon RDS for MySQL and MariaDB will also support Optimized Writes. With Optimized Writes you can improve write throughout by up to 2x at no additional cost. M7g and R7g database instances are supported on RDS for MySQL versions 8.0 and 8.4, RDS for PostgreSQL versions 13.4 (and higher), 14.5 (and higher), 15, 16 and 17 and RDS for MariaDB versions 10.4, 10.5, 10.6, 10.11 and 11.4. For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. Get started using the Amazon RDS Management Console.