Are you tired of dealing with Microsoft’s licensing scheme for SQL Server? If so, you’re not alone. In recent years, there has been a rise in alternative data platforms that offer cost-effective solutions for running enterprise data workloads. One such platform is Amazon’s Aurora, a fully managed relational database service that supports both MySQL and PostgreSQL.

As someone who has been supporting SQL Server for over two decades, I understand the challenges that come with bugs and performance problems. However, I’ve also witnessed the continuous improvements made by Amazon’s Aurora, making it a compelling option for businesses looking to migrate their data platform. One of the key advantages of Aurora is its lack of license costs, resulting in significantly lower operating expenditures.

Let’s compare a 64-core Business Critical Azure Managed Instance with a 64-core instance of Aurora MySQL. The cost of two nodes of Aurora MySQL is less than half the cost of Azure SQL Server Managed Instances. Additionally, Azure Managed Instances have limitations such as supporting only 100 databases and providing only 5.1 GB of RAM per vCore. In contrast, Aurora offers greater flexibility with 512 GB of RAM, making it a more scalable option for your data needs.

Now that we’ve discussed the benefits of migrating to Aurora, let’s delve into the process of how to make the transition. The migration process can be broken down into two steps: Schema Conversion and Data Migration.

Schema Conversion

When migrating from SQL Server to Aurora, schema conversion is made simple with the help of AWS SCT (Schema Conversion Tool). This tool allows you to convert your existing SQL Server schema to a format compatible with Aurora. It’s important to note that you’ll need the JDBC drivers for SQL Server to use AWS SCT. While there may be a minor inconvenience of not being able to use “.” for a local host, typing the server name is a straightforward task.

During the schema conversion process, AWS SCT performs complex actions such as converting triggers. Since triggers aren’t a concept used in MySQL, the conversion is not a simple one-to-one mapping. However, with the assistance of AWS SCT, migrating from SQL Server to Aurora can be a relatively seamless process.

Data Migration

Another tool provided by AWS to simplify the migration process is AWS DMS (Data Migration Service). This service allows you to migrate your data from SQL Server to Aurora PostgreSQL with ease. In fact, AWS has recently introduced Babelfish for Aurora PostgreSQL, a product that enables SQL Server’s T-SQL code to run on PostgreSQL. This further streamlines the migration process and ensures compatibility between the two platforms.

By migrating to Amazon Aurora, you not only save on licensing costs but also modernize your data platform. With the support of AWS SCT and AWS DMS, the migration process becomes more accessible and efficient.

In conclusion, if you’re looking for a cost-effective and feature-rich alternative to SQL Server, Amazon Aurora is a compelling choice. Its continuous improvements, lack of license costs, and scalability make it an attractive option for businesses of all sizes. With the help of AWS SCT and AWS DMS, migrating from SQL Server to Aurora becomes a manageable task that can bring significant benefits to your organization.