Category: AWS Database Migration Service

In this post, we deep dive into AWS Database Migration Service Data Resync, a feature that was introduced in DMS version 3.6.1 to detect and resolve data inconsistencies during database migrations, eliminating the need for manual intervention.

AWS DMS accommodates a broad range of source and target data repositories, such as relational databases, data warehouses, and NoSQL databases. Proper preparation and design are vital for a successful migration process, especially when it comes to optimizing performance and addressing potential delay issues. In this blog post, we offer guidance about recognizing potential root causes of complete load and CDC delays early in the process and provide suggestions for optimally clustering tables to achieve the best performance for an AWS DMS task.

AWS recently announced virtual target mode in AWS Database Migration Service (AWS DMS) Schema Conversion. This feature helps you start migration planning without provisioning target databases. In this post, we show you how to get started using virtual target mode in AWS DMS Schema Conversion.

In this post, we demonstrate how to use DMS Schema Conversion to assess an Amazon RDS for SQL Server database and convert it to Amazon Aurora PostgreSQL-Compatible Edition. We walk you through how to automate the setup and configuration of DMS Schema Conversion components, generate an assessment report, convert database storage and code objects, export the converted code to Amazon S3, and apply the converted code to the target database.

In this post, we discuss handling large collections that are approaching 32 TiB for Amazon DocumentDB. We demonstrate solutions for transitioning from uncompressed to compressed collections using AWS DMS. This migration not only accommodates larger uncompressed data volumes, but also significantly reduces storage, compute costs associated with Amazon DocumentDB and improves performance.

AWS DMS customers might choose not to use the data validation feature provided by the AWS DMS service due to the time it takes to complete validation after a load, a large dataset transfer or a data reload, where business requires rapid availability of data in the target environment. As a result, you might opt to perform validation manually or use a single pass full load only validation, which requires additional effort and time. In this post, we walk you through how to build a custom AWS DMS data validation solution with AWS serverless services.

Migrating from SQL Server to Amazon Aurora can significantly reduce database licensing costs and modernize your data infrastructure. To accelerate your migration journey, we have developed a migration solution that offers ease and flexibility. You can use this migration accelerator to achieve fast data migration and minimum downtime while customizing it to meet your specific business requirements. In this post, we showcase the core features of the migration accelerator, demonstrated through a complex use case of consolidating 32 SQL Server databases into a single Amazon Aurora instance with near-zero downtime, while addressing technical debt through refactoring.

In this post, we provide a comprehensive, step-by-step guide for migrating an on-premises self-managed encrypted MySQL database to Amazon Aurora MySQL using AWS DMS homogeneous data migrations over a private network. We show a complete end-to-end example of setting up and executing an AWS DMS homogeneous migration, consolidating all necessary configuration steps and best practices.

When using AWS DMS, replication lags, task stalls, or resource bottlenecks can occur—and identifying the root cause quickly can become critical. The enhanced monitoring dashboard is a comprehensive monitoring tool that provides visibility into critical metrics for database migration tasks and replication instances. In this post, we discuss some use cases showcasing how you can use the enhanced monitoring dashboard.

Data validation is an important process during data migrations, helping to verify that the migrated data matches the source data. In this post, we present alternatives you can use for data validation when dealing with tables that lack primary keys. We discuss alternative approaches, best practices, and potential solutions to make sure that your data migration process remains thorough and reliable, even in the absence of traditional primary key-based validation methods. Specifically, we demonstrate how to perform data validation after a full load migration from SQL Server to Amazon Aurora PostgreSQL-Compatible Edition using the PostgreSQL tds_fdw extension.