Category: Database

Amazon Aurora offers a high-performance advanced auditing feature that logs detailed database activity to the database audit logs in Amazon CloudWatch. If you are using Aurora 1.10.1 or greater, you can use advanced auditing to meet regulatory or compliance requirements by capturing eligible events like tables queried, queries issued, and connections and disconnections. You can […]

AWS Database Migration Service (AWS DMS) helps you migrate databases to AWS quickly and securely. You can migrate your data to and from most widely used commercial and open source databases, such as Oracle, Microsoft SQL Server, and PostgreSQL. The service supports homogeneous migrations such as Oracle to Oracle, and also heterogeneous migrations between different […]

We are excited to announce a new feature that provides a pre-migration checklist in AWS Database Migration Service (AWS DMS). AWS DMS does a great job of helping you move your data between multiple supported sources and targets. However, migrations can be difficult, especially when you’re moving from one database engine to another (known as […]

Before reading this blog post, we recommend that you look at the AWS DMS, AWS SCT and AWS Snowball blogs and get to know these services. More than 40,000 databases have been migrated to AWS using AWS Database Migration Service (AWS DMS), either as a one-time migration or with ongoing replication. AWS Database Migration Service (AWS DMS) and AWS […]

Written by David Yahalom, CTO and co-founder of NAYA Tech—a leading database, big data, and cloud professional and consulting service provider, located in San Jose, CA. In this post, I discuss how Amazon Aurora can serve as a powerful and flexible alternative to Oracle RAC. Both Oracle RAC and Amazon Aurora are designed to provide increased high availability and performance scalability for your databases.

We always treat database migration projects as a development exercise. Each project should have proper planning (that is, design), execution, and of course testing. Each step involves developer collaboration. Developers also just like to collaborate on tools, methodologies, and a nice piece of code. We want to facilitate developer collaboration for migration projects, to bring more tools and methods to the community to allow better, smoother, and faster migrations. As part of this effort, we recently launched two GitHub repositories, for AWS DMS samples and AWS DMS tools.

In our previous post, we covered the initial steps to debug AWS Database Migration Service (DMS) migrations for environmental issues. In this post, we continue the debugging process, discussing problems with migration tasks that aren’t in the list preceding and that aren’t due to environmental issues. We review CloudWatch graphs and task and table states to shed light on DMS migrations.

This blog post is the first in a two-part series. In this first post, we discuss how moving to Amazon Relational Database Service (Amazon RDS) can change your role as a traditional DBA and bring more value to you, the business, key projects, and end users. In the next post, we will discuss how to use other AWS products to automate any remaining regular tasks in Amazon RDS.

This post walks you through a troubleshooting flow chart to help you understand what could go wrong with migrations using AWS DMS, and it discusses best practices for debugging your AWS DMS migrations. This process involves creating the required AWS DMS components—like the replication instance, source and target endpoints, and the replication task to migrate data from the source endpoint to the target endpoint.

This blog post gives you a quick overview of how you can schedule migration tasks for the purpose of automating your migration. You create these tasks by using the AWS Database Migration Service (AWS DMS) and using native operating system tools for either Linux or Microsoft Windows.