We, as individual users or an organization, choose Relational Databases for our software system or application as per the requirements. What if the user wants to migrate their data from self-managed, open-source, and commercial Databases to fully managed Databases of the same engine?
There is the option of a fully-managed Amazon Relational Database Service (RDS) or Amazon Aurora relational database available in the market. Users can migrate from one relational database to AWS databases of the same engine using third-party tools or via AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT). We will use AWS Database Migration Service (AWS DMS) to connect from the most widely used commercial and open-source databases, such as Microsoft SQL Server, to Amazon Aurora.
In this article, you will learn the steps to connect Microsoft SQL Server to Amazon Aurora. The source(SQL Server) and target(Aurora) databases must be on AWS services. So, read along to understand more about Amazon Aurora SQL Server Integration.
What is SQL Server?
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SQL Server is a popular Relational Database Management System(RDBMS). Microsoft develops SQL Server, so it is also called Microsoft SQL Server or sometimes MSSQL. SQL Server was initially released on April 24, 1989, and it is written in programming languages like C and C++. SQL Server is extensively used for Windows and launched for Linux systems in 2016.
SQL Server is a versatile database server used to store data and retrieve data by various software applications from local systems and different machines on the network. SQL Server is a relational database system storing data in tabular format, and the relationship between tables is maintained.
For smooth and reliable database transactions, SQL Server follows the principle of ACID properties: Atomicity, Consistency, Isolation, and Durability.
To install SQL Server click here. Further in this article, you will learn about Amazon Aurora SQL Server integration.
Understanding SQL Server Architecture
The architecture of SQL Server is illustrated in the below image:
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There are two core components of SQL Server:
1) SQL Server Database Engine
Database Engine is the core component of SQL Server which controls data storage, data processing, and data security. It consists of a relational engine that manages SQL commands, queries, and database file storage. It also creates and executes stored procedures, views, triggers, and other database objects.
2) SQLOS
SQLOS is the SQL Server Operating System sitting beneath the SQL Server database engine. SQLOS handles lower-level functions such as job scheduling, memory, I/O management, and data locking to avoid conflict.
A network interface layer sits above the Database Engine and uses external protocols to facilitate request and response interactions with database servers.
What is Amazon Aurora?
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Amazon Aurora is a fully managed Relational Database Engine developed by Amazon Web Services in October 2014. Aurora provides fully managed, built-in security, serverless compute with a high-performance storage sub-system, and a less than one-minute cross-region disaster recovery feature. It is a highly scalable and durable database built for cloud-based commercial purposes at 1/10th of the cost.
Aurora is super compatible with MySQL and PostgreSQL. Even the code the user uses with MySQL and PostgreSQL works with Amazon Aurora. Aurora can be easily migrated to and from MySQL and PostgreSQL. It can deliver up to five times the throughput of MySQL and up to three times the throughput of PostgreSQL with some workloads without impacting most of the existing applications.
Aurora automatically grows the underlying storage as per requirement. Aurora handles the most challenging aspects of database configuration of automation and standardization of clustering and replication.
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Understanding Amazon Aurora Architecture
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Key points of Amazon Aurora Architecture are
- Amazon Aurora decouples storage and query processing.
- Aurora focuses on reusing its components, making it work like query execution, transaction, and recovery efficient and faster.
- Amazon Aurora ensures safe backups with virtual storage and various data clones creation.
- Aurora prioritizes data access restrictions and stores changelog on the remote disk.
- Aurora uses a primary-replica setup.
Steps to Set Up Amazon Aurora SQL Server Integration
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This section will discuss steps to set up Amazon Aurora SQL Server Integration.
Prerequisites
- Configure Your Microsoft SQL Server Source Database as per the instructions mentioned in the link.
- Configure Your Aurora MySQL Target Database as per the instructions mentioned in the link.
So, let’s get started with the Amazon Aurora SQL Server Integration.
Step 1: Install the JDBC Drivers and AWS Schema Conversion Tool
The first step is to install JDBC drivers for Microsoft SQL Server and Amazon Aurora MySQL and the AWS Schema Conversion Tool (AWS SCT) on your local computer. Do the following:
- Download the JDBC driver for Microsoft SQL Server (mssql-jdbc-7.2.2.jre11.jar) from the link.
- Download the JDBC driver for Aurora MySQL(mysql-connector-java-8.0.29.tar.gz) from here as Amazon Aurora MySQL uses the MySQL driver.
- Download and install AWS SCT based on your operating system.
- Set up AWS SCT. using the below steps:
- Start AWS SCT, choose Settings, and then Global Settings.
- In Global Settings, choose Drivers, and then Browse for Microsoft SQL Server Driver Path.
- Browse the JDBC driver for MySQL driver path, and choose OK.
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Step 2: Use the AWS SCT to Connect the SQL Server to Aurora MySQL.
Launch the AWS Schema Conversion Tool(SCT) to connect SQL Server to Aurora MySQL.
- In the AWS SCT, choose File, then choose New Project. Create a new project named Connect SQL Server to Aurora MySQL, specify the project folder’s location, and click OK.
- Choose to add a source database to your project, choose Microsoft SQL Server, and then choose Next.
- Add the below information to connect to the Source Microsoft SQL Server database and choose Test Connection.
| Parameter | Description |
| Connection name | Enter Microsoft SQL Server in this field. |
| Server name | Enter the server name. |
| Server port | Enter the SQL Server port number. The default is 1433. |
| Instance name | Enter the SQL Server database instance name. |
| User name | Enter the SQL Server admin user name. |
| Password | Enter the password for the admin user. |
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- Once the test connection is made, click OK to close the alert box and choose Connect for connection with the Microsoft SQL Server database instance. The left panel of AWS SCT displayed the Microsoft SQL Server database instance structure.
- Click on Add target and choose Amazon Aurora (MySQL compatible) to add it as a target database to your project and then click Next.
- Add the below information to connect to the target Amazon Aurora database and choose Test Connection.
| Parameter | Description |
| Connection name | Enter Aurora MySQL in the tree in the right panel. |
| Server name | Enter the server name. |
| Server port | Enter the SQL Server port number. The default is 3306. |
| User name | Enter the Aurora MySQL admin user name. |
| Password | Enter the password for the admin user. |
- Choose OK to close the alert box and then click on Connect connect to the Aurora MySQL database instance.
- Select the source schema in the left panel tree of AWS SCT. In the right panel tree, select your target Aurora MySQL database. And then choose to Create mapping.
- Successful mapping means a successful connection from Microsoft SQL Server to Amazon Aurora.
After a successful connection, we can migrate the SQL Server schema to Aurora and generate reports.
Conclusion
In this article, you learned about the Aurora SQL Server Integration. You also understood the detailed steps to set up Aurora SQL Server Integration.
Amazon Web services offer vast, flexible, and fully-managed database services. And also provide an option for schema and data migration to Amazon Aurora. Microsoft SQL Server can successfully connect to the Amazon Aurora database following the above steps.
Similarly, other relational databases can be connected to Amazon Aurora and smoothly migrate their schema and data to Aurora. Read Working with Amazon Aurora MySQL Simplified or Power BI Aurora Integration: 2 Easy Methods to explore more.
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Share your experience with Amazon Aurora SQL Server Integration in the comments section below!
Nidhi is passionate about carrying out in-depth research on topics related to data integration and analysis, and loves to see the impact by offering informative content to assist individuals in delving deeper into these intricate subjects.