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AWS CodePipeline adds native Amazon EC2 deployment support

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AWS CodePipeline introduces a new action to deploy to Amazon Elastic Compute Cloud (EC2). This action enables you to easily deploy your application to a group of EC2 instances behind load balancers.

Previously, if you wanted to deploy to EC2 instances, you had to use CodeDeploy with an AppSpec file to configure the deployment. Now, you can simply use this new EC2 deploy action in your pipeline to deploy to EC2 instances, without the necessity of managing CodeDeploy resources. This streamlined approach reduces your operational overhead and simplifies your deployment process.

To learn more about using the EC2 deploy action in your pipeline, visit our tutorial and documentation . For more information about AWS CodePipeline, visit our product page . This new action is available in all regions where AWS CodePipeline is supported, except the AWS GovCloud (US) Regions and the China Regions.
 

AWS Database Migration Service now supports Multi-ENI networking for homogeneous migrations.

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AWS Database Migration Service (DMS) now supports network interfaces. Customers can now choose a secure connection type, which improves connectivity to on-premises databases for DMS homogeneous migrations. For each data migration, DMS now assigns a private IP address. Additionally, a non-static public IP address may be assigned if the connection is configured for public access. This access can be established within the local VPC or through VPC peering, Direct Connect or VPN.

For information see documentation for AWS DMS Homogeneous Migrations .
For AWS DMS regional availability, please refer to the AWS Region Table .
 

Amazon RDS for PostgreSQL supports minor versions 17.4, 16.8, 15.12, 14.17, 13.20

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Amazon Relational Database Service (RDS) for PostgreSQL now supports the latest minor versions 17.4, 16.8, 15.12, 14.17, and 13.20. Please note, this release supports the versions released by the PostgreSQL community on February, 20,2025 to address the regression that was part of the February 13, 2025 release. We recommend that you upgrade to the latest minor versions to fix known security vulnerabilities in prior versions of PostgreSQL, and to benefit from the bug fixes added by the PostgreSQL community.

You can use automatic minor version upgrades to automatically upgrade your databases to more recent minor versions during scheduled maintenance windows. You can also use Amazon RDS Blue/Green deployments for RDS for PostgreSQL using physical replication for your minor version upgrades. Learn more about upgrading your database instances, including automatic minor version upgrades and Blue/Green Deployments in the Amazon RDS User Guide .

Amazon RDS for PostgreSQL makes it simple to set up, operate, and scale PostgreSQL deployments in the cloud. See Amazon RDS for PostgreSQL Pricing for pricing details and regional availability. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console.
 

Amazon MSK adds support for Apache Kafka version 3.8

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Amazon Managed Streaming for Apache Kafka (Amazon MSK) now supports Apache Kafka version 3.8. You can now create new clusters using version 3.8 with either KRAFT or ZooKeeper mode for metadata management or upgrade your existing ZooKeeper based clusters to use version 3.8. Apache Kafka version 3.8 includes several bug fixes and new features that improve performance. Key new features include support for compression level configuration. This allows you to further optimize your performance when using compression types such as lz4, zstd and gzip, by allowing you to change the default compression level. For more details and a complete list of improvements and bug fixes, see the Apache Kafka release notes for version 3.8 .

Amazon MSK is a fully managed service for Apache Kafka and Kafka Connect that makes it easier for you to build and run applications that use Apache Kafka as a data store. Amazon MSK is compatible with Apache Kafka, which enables you to quickly migrate your existing Apache Kafka workloads to Amazon MSK with confidence or build new ones from scratch. With Amazon MSK, you can spend more time innovating on streaming applications and less time managing Apache Kafka clusters. To learn how to get started, see the Amazon MSK Developer Guide .

Support for Apache Kafka version 3.8 is offered in all AWS regions where Amazon MSK is available.

Announcing fine-grained access control via AWS Lake Formation with EMR on EKS

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We are excited to announce the general availability of fine-grained data access control (FGAC) via AWS Lake Formation for Apache Spark with Amazon EMR on EKS. This enables you to enforce full FGAC policies (database, table, column, row, and cell-level) defined in Lake Formation for your data lake tables from EMR on EKS Spark jobs. We are also sharing the general availability of Glue Data Catalog views with EMR on EKS for Spark workflows.

Lake Formation simplifies building, securing, and managing data lakes by allowing you to define fine-grained access controls through grant and revoke statements, similar to RDBMS. The same Lake Formation rules now apply to Spark jobs on EMR on EKS for Hudi, Delta Lake, and Iceberg table formats, further simplifying data lake security and governance.

AWS Glue Data Catalog views with EMR on EKS allows customers to create views from Spark jobs that can be queried from multiple engines without requiring access to referenced tables. Administrators can control underlying data access using the rich SQL dialect provided by EMR on EKS Spark jobs. Access is managed with AWS Lake Formation permissions, including named resource grants, data filters, and lake formation tags. All requests are logged in AWS CloudTrail.

Fine-grained access control for Apache Spark batch jobs on EMR on EKS is available with the EMR 7.7 release in all regions where EMR on EKS is available. To get started, see Using AWS Lake Formation with Amazon EMR on EKS .
 

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