https://aws.amazon.com/dms/ now supports AI agent automation through the AWS MCP Server. You can connect AI coding agents, including Kiro, Claude Code, and Cursor, to DMS Schema Conversion and run complete migration workflows using natural language directly from your IDE. Agents create projects, browse source metadata, convert schemas, generate assessment reports, and export results autonomously. The DMS Schema Conversion skill, dms-schema-conversion, loads on demand and provides agents with predefined procedures, including API patterns, schema exclusions, and operational sequencing rules. Agents follow these procedures rather than improvising from general knowledge, reducing trial-and-error loops. They can also help convert remaining code objects such as stored procedures, functions, and triggers, building on the generative AI capabilities launched at re:Invent 2024. AI agent automation is available for all existing DMS Schema Conversion source and target engine pairs at no additional charge. For regional availability, see the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_SchemaConversion.html#schema-conversion-supported-regions page. To get started, see https://docs.aws.amazon.com/dms/latest/userguide/sc-genai-agents.html in the documentation.

AWS Database Migration Service (DMS) Schema Conversion now supports AI agent automation through the AWS MCP Server. You can connect AI coding agents, including Kiro, Claude Code, and Cursor, to DMS Schema Conversion and run complete migration workflows using natural language directly from your IDE. Agents create projects, browse source metadata, convert schemas, generate assessment reports, and export results autonomously. The DMS Schema Conversion skill, dms-schema-conversion, loads on demand and provides agents with predefined procedures, including API patterns, schema exclusions, and operational sequencing rules. Agents follow these procedures rather than improvising from general knowledge, reducing trial-and-error loops. They can also help convert remaining code objects such as stored procedures, functions, and triggers, building on the generative AI capabilities launched at re:Invent 2024. AI agent automation is available for all existing DMS Schema Conversion source and target engine pairs at no additional charge. For regional availability, see the Supported AWS Regions page. To get started, see Using AI agents with DMS Schema Conversion in the documentation.

AWS Database Migration Service (AWS DMS) Schema Conversion with generative AI is now available. The feature is currently available for database schema conversion from commercial engines, such as Microsoft SQL Server, to Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service (Amazon RDS) for PostgreSQL. Using generative AI recommendations, you can simplify and accelerate your database migration projects, particularly when converting complex code objects which typically require manual conversion, such as stored procedures, functions, or triggers. AWS DMS Schema Conversion with generative AI converts up to 90% of your schema. AWS DMS Schema Conversion with generative AI is currently available in three https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/: US East (N. Virginia), US West (Oregon), and Europe (Frankfurt). You can use this feature in the AWS Management Console or AWS Command Line Interface (AWS CLI) by selecting a commercial database such as Microsoft SQL Server as your source database and Amazon Aurora PostgreSQL or Amazon RDS for PostgreSQL as your target when initiating a schema conversion project. When converting applicable objects, you will see an option to enable generative AI for conversion. To get started, visit the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_SchemaConversion.html and check out this https://aws.amazon.com/blogs/aws/aws-data-migration-service-improves-database-schema-conversion-with-generative-ai.

Today, AWS Databases including Amazon Aurora PostgreSQL, Amazon Aurora DSQL, and Amazon DynamoDB are generally available on the Vercel Marketplace, enabling you to create and connect to an AWS database directly from Vercel in seconds. To get started, you can create a new AWS Account from Vercel that includes access to the three databases and $100 USD in credits. These credits can be used with any of these database option for up to six months. Once your account is set up, you can have a production-ready Aurora database or DynamoDB table powering your Vercel projects within seconds. You can also manage your plan, add payment information, and view usage details anytime by visiting the AWS settings portal from the Vercel dashboard. To learn more, visit the https://vercel.com/marketplace/aws on the Vercel Marketplace. The integration includes serverless options for https://aws.amazon.com/rds/aurora/, https://aws.amazon.com/rds/aurora/dsql/, and https://aws.amazon.com/dynamodb/?nc2=type_a to simplify your application needs and reduce costs by scaling to zero when not in use. You can create a database in the following https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Tokyo), and Asia Pacific (Mumbai) with more Regions coming soon. AWS Databases deliver security, reliability, and price performance without the operational overhead, whether you're prototyping your next big idea or running production AI and data driven applications. For more information, visit the https://aws.amazon.com/products/databases/

Today, AWS Databases including Amazon Aurora PostgreSQL, Amazon Aurora DSQL, and Amazon DynamoDB are generally available on the Vercel Marketplace, enabling you to create and connect to an AWS database directly from Vercel in seconds. To get started, you can create a new AWS Account from Vercel that includes access to the three databases and $100 USD in credits. These credits can be used with any of these database option for up to six months. Once your account is set up, you can have a production-ready Aurora database or DynamoDB table powering your Vercel projects within seconds. You can also manage your plan, add payment information, and view usage details anytime by visiting the AWS settings portal from the Vercel dashboard. To learn more, visit the AWS landing page on the Vercel Marketplace. The integration includes serverless options for Amazon Aurora PostgreSQL, Amazon Aurora DSQL, and Amazon DynamoDB to simplify your application needs and reduce costs by scaling to zero when not in use. You can create a database in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Tokyo), and Asia Pacific (Mumbai) with more Regions coming soon. AWS Databases deliver security, reliability, and price performance without the operational overhead, whether you're prototyping your next big idea or running production AI and data driven applications. For more information, visit the AWS Databases webpage.

https://docs.aws.amazon.com/glue/latest/dg/zero-etl-using.html now supports zero-ETL for self-managed database sources in seven additional regions. Using Glue zero-ETL, you can setup an integration to replicate data from Oracle, SQL Server, MySQL or PostgreSQL databases which are located on-premises or on AWS EC2 to Redshift with a simple experience that eliminates configuration complexity. AWS zero-ETL for self-managed database sources will automatically create an integration for an on-going replication of data from your on-premises or EC2 databases through a simple, no-code interface. You can now replicate data from Oracle, SQL Server, MySQL and PostgreSQL databases into Redshift. This feature further reduces users' operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines to ingest data from self-managed databases to Redshift. AWS Glue zero-ETL for self-managed database sources are available in the following additional AWS Regions: Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (London), South America (São Paulo), and US (Virginia) regions. To get started, sign into the https://us-east-1.console.aws.amazon.com/ec2/home?region=us-east-1#LaunchInstances:instanceType=r8a.large. For more information visit the https://aws.amazon.com/glue/ or review the https://docs.aws.amazon.com/glue/latest/dg/zero-etl-using.html documentation.

AWS Database Migration Service (DMS) Schema Conversion is a fully managed feature of DMS that automatically assesses and converts database schemas to formats compatible with AWS target database services. Today, we are excited to announce that Schema Conversion now supports conversions from IBM Db2 for z/OS to Amazon Relational Database Service (RDS) for Db2. Using Schema Conversion, you can automatically convert database objects from your IBM Db2 for z/OS source to an Amazon RDS for Db2 target, including stored procedures, functions, views, and other database structures. This is especially valuable for mainframe migrations as it simplifies complex processes by resolving syntax differences and compatibility issues between environments. Schema Conversion also provides detailed assessment reports to help you plan and execute your migration effectively. To learn more refer to using IBM Db2 z/OS as a source for AWS DMS Schema Conversion and using IBM Db2 for z/OS as a source for AWS DMS. For AWS DMS Schema Conversion regional availability, please refer to the AWS Region Table.

AWS Glue now supports zero-ETL for self-managed database sources in seven additional regions. Using Glue zero-ETL, you can setup an integration to replicate data from Oracle, SQL Server, MySQL or PostgreSQL databases which are located on-premises or on AWS EC2 to Redshift with a simple experience that eliminates configuration complexity. AWS zero-ETL for self-managed database sources will automatically create an integration for an on-going replication of data from your on-premises or EC2 databases through a simple, no-code interface. You can now replicate data from Oracle, SQL Server, MySQL and PostgreSQL databases into Redshift. This feature further reduces users' operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines to ingest data from self-managed databases to Redshift. AWS Glue zero-ETL for self-managed database sources are available in the following additional AWS Regions: Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (London), South America (São Paulo), and US (Virginia) regions. To get started, sign into the AWS Management Console. For more information visit the AWS Glue page or review the AWS Glue zero-ETL documentation.

https://aws.amazon.com/dms/ now supports Data Masking, enabling customers to transform sensitive data at the column level during migration, helping to comply with data protection regulations like GDPR. Using AWS DMS, you can now create copies of data that redacts information at a column level that you need to protect. AWS Data Masking will automatically mask the portions of data you specify. Data Masking offers three transformation techniques: digit randomization, digit masking, and hashing. It's available for all endpoints supported by DMS Classic and DMS Serverless in version 3.5.4. To learn more about Data Masking with AWS DMS, please please refer to the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Tasks.CustomizingTasks.TableMapping.SelectionTransformation.Masking.html.

AWS Database Migration Service Serverless (AWS DMSS) now supports files on S3 source endpoints. Using AWS DMSS, you can now migrate data in CSV or parquet format from S3 to any supported AWS DMSS target endpoint. AWS DMSS supports both full load and Change Data Capture (CDC) migration modes for CSV and Parquet files with S3 source endpoints using AWS DMS console, AWS CLI, or AWS SDKs in https://docs.aws.amazon.com/dms/latest/userguide/CHAP_ReplicationInstance.html For information about AWS DMS S3 source endpoints see https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.S3.html. Visit https://aws.amazon.com/dms/ to get started. For AWS DMS regional availability, please refer to the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.

https://aws.amazon.com/dms/ now supports offline source conversion for Microsoft SQL Server, enabling you to convert SQL Server schemas and code without direct connectivity to your source databases. You extract metadata using standard database commands in your own environment, then upload it to DMS Schema Conversion for processing. This eliminates security reviews, firewall changes, and VPN setup that delay migration projects, while delivering the same conversion results as the connected approach. Offline Source is ideal for organizations with security policies that restrict external tool access to production SQL Server databases. Database administrators generate human-readable metadata files within their existing environment, and security teams can review the commands and output before uploading, making approval straightforward. By removing the connectivity requirement, Offline Source transforms weeks of security reviews into a simple command-and-upload workflow. Offline Source supports all DMS Schema Conversion targets at no additional conversion charge. For regional availability, see the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_SchemaConversion.html#schema-conversion-supported-regions page. To get started, see https://docs.aws.amazon.com/dms/latest/userguide/virtual-data-provider.html in the DMS Schema Conversion documentation.

AWS Database Migration Service (DMS) Schema Conversion now supports offline source conversion for Microsoft SQL Server, enabling you to convert SQL Server schemas and code without direct connectivity to your source databases. You extract metadata using standard database commands in your own environment, then upload it to DMS Schema Conversion for processing. This eliminates security reviews, firewall changes, and VPN setup that delay migration projects, while delivering the same conversion results as the connected approach. Offline Source is ideal for organizations with security policies that restrict external tool access to production SQL Server databases. Database administrators generate human-readable metadata files within their existing environment, and security teams can review the commands and output before uploading, making approval straightforward. By removing the connectivity requirement, Offline Source transforms weeks of security reviews into a simple command-and-upload workflow. Offline Source supports all DMS Schema Conversion targets at no additional conversion charge. For regional availability, see the Supported AWS Regions page. To get started, see Using Offline Source in the DMS Schema Conversion documentation.

はじめに来年のプロジェクトに向けAWSDatabaseMigrationServiceについて調査する機会があったため構築手順をブログにしますスコープとなっているデータベースはMySQ

AWS Database Migration Service (DMS) Schema Conversion with GenAI is now available in nine additional AWS Regions: Asia Pacific (Tokyo, Osaka, Sydney), Europe (Ireland, London, Stockholm, Paris), Canada (Central) and US East (Ohio). This feature leverages Amazon Bedrock foundation models—including Claude 3.5 Sonnet v2, Claude 3.7 Sonnet, and Claude Sonnet 4—to automate database schema and code conversion, helping organizations accelerate their database modernization initiatives. The regional expansion enables customers to process their migration workloads locally, reducing latency and supporting data residency requirements. DMS Schema Conversion with GenAI automatically converts database schemas and code from Oracle, SQL Server, MySQL, PostgreSQL, and Sybase to Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL. By automating the conversion process, the service significantly reduces manual effort and accelerates migration project timelines, enabling database administrators and migration specialists to focus on strategic modernization activities rather than time-consuming manual code transformation. DMS Schema Conversion is available at no additional charge and can be accessed through the AWS Management Console or AWS Command Line Interface (CLI). To learn more about supported database engines, conversion capabilities, and regional availability, visit the https://docs.aws.amazon.com/dms/latest/userguide/schema-conversion-convert.databaseobjects.html and https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Security.DataProtection.CrossRegionInference.html.

https://docs.aws.amazon.com/glue/latest/dg/zero-etl-using.html now supports zero-ETL for self-managed database sources in seven additional regions. Using Glue zero-ETL, you can setup an integration to replicate data from Oracle, SQL Server, MySQL or PostgreSQL databases which are located on-premises or on AWS EC2 to Redshift with a simple experience that eliminates configuration complexity. AWS zero-ETL for self-managed database sources will automatically create an integration for an on-going replication of data from your on-premises or EC2 databases through a simple, no-code interface. You can now replicate data from Oracle, SQL Server, MySQL and PostgreSQL databases into Redshift. This feature further reduces users' operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines to ingest data from self-managed databases to Redshift. AWS Glue zero-ETL for self-managed database sources are available in the following additional AWS Regions: Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (London), South America (São Paulo), and US (Virginia) regions. To get started, sign into the https://us-east-1.console.aws.amazon.com/ec2/home?region=us-east-1#LaunchInstances:instanceType=r8a.large. For more information visit the https://aws.amazon.com/glue/ or review the https://docs.aws.amazon.com/glue/latest/dg/zero-etl-using.html documentation.

AWS https://aws.amazon.com/dms/ (DMS) https://docs.aws.amazon.com/dms/latest/userguide/CHAP_SchemaConversion.html is a fully managed feature of DMS that automatically assesses and converts database schemas to formats compatible with AWS target database services. Today, we're excited to announce that Schema Conversion now supports conversions from SAP Adaptive Server Enterprise (ASE) database (formerly known as Sybase) to Amazon RDS PostgreSQL and Amazon Aurora PostgreSQL, powered by Generative AI capability. Using Schema Conversion, you can automatically convert database objects from your SAP (Sybase) ASE source to an to Amazon RDS PostgreSQL and Amazon Aurora PostgreSQL target. The integrated generative AI capability intelligently handles complex code conversions that typically require manual effort, such as stored procedures, functions, and triggers. Schema Conversion also provides detailed assessment reports to help you plan and execute your migration effectively. To learn more about this feature, see the documentation for using SAP (Sybase) ASE as a source forhttps://docs.aws.amazon.com/dms/latest/userguide/dm-data-providers-source-sybase-ASE.html and using SAP (Sybase) ASE as a source for https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.SAP.html for data migration. For details about the generative AI capability, please refer to the https://docs.aws.amazon.com/dms/latest/userguide/schema-conversion-convert.databaseobjects.html#schema-conversion-convert.databaseobjects.genai. For AWS DMS Schema Conversion regional availability, please refer to the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_SchemaConversion.html#schema-conversion-supported-regions page.

AWS Database Migration Service (AWS DMS) now supports Data Resync, a new feature that automatically corrects data inconsistencies identified during validation between source and target databases. Data Resync integrates with your existing DMS migration tasks and supports both Full Load and Change Data Capture (CDC) phases. It uses your current task settings—including connection configurations, table mappings, and transformations—to apply corrections automatically, helping ensure accurate and reliable migrations without manual intervention. With Data Resync, AWS DMS can detect and resolve common data issues, such as missing records, duplicate entries, or mismatched values, based on validation results. Data Resync is available starting with AWS DMS replication engine version 3.6.1, and currently supports migration paths from Oracle and SQL Server to PostgreSQL. For detailed information on how Data Resync enhances migration accuracy, please refer to the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Validating.DataResync.html

https://aws.amazon.com/dms/ (AWS DMS Serverless) now offers storage scaling. With this enhancement you never have to worry about exceeding the DMS Serverless 100GB default replication storage capacity limit when processing very large transaction volumes or using detailed logging. You can now use AWS DMS Serverless for replicating even the highest of transaction volumes since there is no longer any storage capacity limits. AWS DMS Severless will automatically increase the storage for your replications any time the existing capacity reaches it limits. To learn more, see the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Serverless.Components.html#CHAP.Serverless.storage.autoscaling. For AWS DMS regional availability, please refer to the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.

AWS https://aws.amazon.com/dms/ (DMS) Schema Conversion is a fully managed feature of DMS that automatically assesses and converts database schemas to formats compatible with AWS target database services. Today, we are excited to announce that Schema Conversion now supports conversions from IBM Db2 for z/OS to https://aws.amazon.com/rds/db2/. Using Schema Conversion, you can automatically convert database objects from your IBM Db2 for z/OS source to an Amazon RDS for Db2 target, including stored procedures, functions, views, and other database structures. This is especially valuable for mainframe migrations as it simplifies complex processes by resolving syntax differences and compatibility issues between environments. Schema Conversion also provides detailed assessment reports to help you plan and execute your migration effectively. To learn more refer to using https://docs.aws.amazon.com/dms/latest/userguide/sc-data-providers-db2.html and https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.DB2zOS.html. For AWS DMS Schema Conversion regional availability, please refer to the https://docs.aws.amazon.com/dms/latest/userguide/CHAP_SchemaConversion.html#schema-conversion-supported-regions.  

Amazon Database Migration Service (DMS) now supports the Multi-ENI networking model and Credentials Vending System for DMS Homogenous Migrations. Customers can now choose the Multi-ENI connection type and use the Credentials Vending System, providing a simplified networking configuration experience for secure connectivity to their on-premises database instances. For information see documentation for AWS https://docs.aws.amazon.com/dms/latest/userguide/data-migrations.html. For AWS DMS regional availability, please refer to the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.  

https://aws.amazon.com/dms/ (AWS DMSS) now supports premigration assessments for replications . A premigration assessment evaluates the source and target databases of a database migration task to help identify problems that might prevent a migration from running as expected. By identifying and fixing these issues before a migration starts, you can avoid delays in completing the database migration. The premigration assessments will obtain detailed information about the source schema and tables to provide recommendations on the AWS DMS settings that should be used. For example, the assessment can suggest which method of reading redo logs for change data capture (CDC) should be used or it could check if the recommended settings have been enabled, providing best practice recommendations from AWS DMS experts. To learn more, see https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Tasks.AssessmentReport.html. AWS DMSS premigration assessments are available in all AWS Regions where DMSS is available. For AWS DMSS regional availability, please refer to the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.  

https://aws.amazon.com/dms/ now allows customers to use Kerberos authentication to securely connect to Oracle and SQL Server source endpoints. This feature allows users to authenticate database connections using the Kerberos protocol, a widely adopted standard for secure identity verification. With this update, AWS DMS can authenticate using credentials stored in AWS Directory Service for Microsoft Active Directory or in an on-premises Microsoft Active Directory that has a forest trust relationship with an AWS Managed Active Directory. Customers can use this feature for Oracle Database and SQL Server without additional licensing costs, simplifying secure migrations. This feature can be configured directly in the AWS DMS Console, CLI, or SDK. To learn more about Kerberos authentication with AWS DMS, please refer to our https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Security.Kerberos.html.

AWS DMS Schema Conversion converts up to 90% of your schema to accelerate your database migrations and reduce manual effort with the power of generative AI.

https://aws.amazon.com/dms/ has enhanced data validation performance for database migrations, enabling customers to validate large datasets with significantly faster processing times. This enhanced data validation is now available in version 3.5.4 of the replication engine for both full load and full load with CDC migration tasks. Currently, this enhancement supports migration paths from Oracle to PostgreSQL, SQL Server to PostgreSQL, Oracle to Oracle, and SQL Server to SQL Server, with additional migration paths planned for future releases. To learn more about data validation performance improvements with AWS DMS, please refer to thehttps://docs.aws.amazon.com/dms/latest/userguide/CHAP_Validating.html#CHAP_Validating_Enhanced.

https://aws.amazon.com/dms/ (AWS DMSS) now offers improved throughput for Oracle to Amazon S3 full load migrations. With this enhancement, you can now migrate data from Oracle databases to S3 up to two times faster than previously possible with AWS DMSS. AWS DMSS Oracle to Amazon S3 Full Load performance enhancements will be applied automatically whenever AWS DMSS detects a full load migration between an Oracle database and Amazon S3. For detailed information on these improvements, refer to the AWS DMSS https://aws.amazon.com/blogs/database/enhanced-full-load-performance-in-aws-dms-serverless/ documentation. To learn more, see the AWS DMS https://docs.aws.amazon.com/dms/latest/sbs/chap-manageddatabases.oracle2rds.load.html documentation. For AWS DMS regional availability, please refer to the https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.