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Abidemi Muibudeen for AWS Community Builders

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Deploy SQL Server on Amazon RDS: A Step-by-Step Guide

An advantage of the cloud is that it provides options to run your workloads in various ways, from running applications on virtual machines to deploying highly available microservices. Amazon Web Services (AWS) is the leading cloud computing provider and controls over 33% of the cloud computing market. AWS offers computing, analytics, machine learning, databases, and storage services. Amazon Relational Database Services (RDS) is a managed relational database offering from AWS that eliminates manual database administration tasks such as scaling, operating system (OS) maintenance, and data backup. RDS supports five database engines; MySQL, Microsoft SQL Server (MSSQL), PostgreSQL, MariaDB, and Oracle.

A bit about SQL Server

SQL Server is a relational database management system (RBDMS) developed by Microsoft, used for creating, maintaining, and implementing relational databases. MSSQL offers several editions targeted for specific workloads. These editions are enterprise, standard, web, developer, and express, which differ in capabilities and feature sets.

  • Enterprise Edition is the premium MSSQL offering for enterprise workloads.

  • Standard SQL Server Standard edition enables effective database management with minimal IT resources.

  • Web SQL Server Web edition is suitable for web applications.

  • Developer SQL Server Developer edition lets developers build and test applications on top of SQL Server.

  • Express editions SQL Server Express edition is a free database, ideal for learning and building desktop and small server data-driven applications.

Amazon RDS for SQL Server makes it easy to set up, operate, and scale SQL Server deployments in the cloud. In this tutorial, you will deploy a Microsoft SQL Server on an Amazon Relational Database Instance and connect to the instance locally using SSMS. You will also get to load data into a table on the database and query the data.


To follow along with this tutorial, you should be familiar with the AWS console, SQL, and RBDMS. It is also necessary to have a SQL server client such as SQL server management studio (SSMS) or Azure Data Studio installed locally. Download and install either client from SQL server management studio or Azure Data Studio.

Before you begin, please note that the AWS console is continuously improving; therefore, some panes may have moved, and the console may look different.

Step 1: Choose your AWS region

Sign in to the Amazon console and search for RDS in the search bar. On the RDS page, create a database. Take note of the AWS region in the pane; this is where you will deploy your database instance. For this tutorial, you will deploy the database instance in Ohio (us-east-2) region. You can learn more about AWS RDS regions and zones here.

Amazon RDS creation checklist

Step 2: Select a Database Engine

Choose Standard create as the database creation method. This method allows you to configure the database that fits your workload needs. Next, select Microsoft SQL Server as the database engine.

Amazon RDS creation checklist

For database type, accept the default Amazon RDS settings. This option enables you to get the full management capabilities of RDS. The express edition is sufficient for the use case in this tutorial. However, you should select the right edition for your workload.

Amazon RDS creation checklist

The database license cost is included with the instance cost by AWS. The most recent SQL Server 2019 engine version is selected by default, which is good enough for this scenario.

Amazon RDS creation checklist

Step 3: Create a user

Give your instance a name on the settings pane, then expand the credentials settings to create an administrative user account. This user will have complete control of the database instance, and you can create other users once the setup is complete.

Amazon RDS creation checklist

Step 4: Configure compute and storage

Leave the instance configuration settings as default; db.t3.small has two vCPUs and two GiB RAM, which is sufficient for this tutorial.

Amazon RDS creation checklist

Accept the default storage type and allocated storage settings on the storage pane. Uncheck storage autoscaling, the minimum allocated storage of 20GiB is sufficient for this tutorial.

Amazon RDS creation checklist

Step 5: Configure connectivity and networking

Since this database instance does not need to connect to a compute instance, accept the default settings for connection to compute resource, network type, and virtual private cloud (VPC) settings.

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For the VPC security group, select create new, then choose No preference for the availability zone.

Amazon RDS creation checklist

Accept the default port 1433 and leave RDS proxy unchecked.

Amazon RDS creation checklist

Leave the windows authentication option unchecked.

Amazon RDS creation checklist

Step 6: Configure performance settings

Uncheck the performance insights settings; it is an advanced performance monitoring feature for diagnosing performance challenges. The feature is not required for this tutorial. Leave the additional configurations as default.

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Step 7: Additional configurations

Accept all default settings for database options.

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Accept all default settings in the backup pane.

Amazon RDS creation checklist

Also, accept all default settings for maintenance.

Amazon RDS creation checklist

Step 8: Create the instance

Finally, you will see the estimated monthly cost for the database instance. Select create database to create the instance.

Amazon RDS creation checklist

This action takes a few minutes; wait until the status changes to created before connecting to the instance.

Amazon RDS creation checklist

Once complete, select the instance name to view its configuration setting and copy the instance endpoint; this is the server name to connect to.

Step 9: Connect to the database instance

This tutorial will make use of SSMS. On the object explorer pane in SSMS, select connect and select database engine. Enter the endpoint you copied earlier as the server name; the login name and password are the username and password you created in step 3. Leave SQL server authentication as default, then connect.

Amazon RDS creation checklist

Now that you are connected, expand the object explorer to view the default database created by RDS. There are also several tables created for administration, backup, and maintenance, which AWS will manage.

Amazon RDS creation checklist

Create a database in the instance.

To query data in a table, you must first create a database that will hold the table.


Click on the refresh button on SSMS to view the newly created database. Then import a flat file using SSMS. Next, load data into the table. The sales data for 2012 is n a local CSV file; you can SSMS to load data into the table.

SSMS Screenshot

Selecting the flat file on the import wizard page creates a table for you. You may edit the table name to adhere to your naming convention. Once the data has been imported, select next to preview the data. Select next and modify the columns if you wish to transform the data.

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Once the data modification is complete, click next to view the summary.

SSMS Screenshot

This process takes a while to complete depending on the size of your data. Once the data loading is completed, you will get a success message.

SSMS Screenshot

Great job so far. Next up, query the data.

To count the number of records in the row, run;

SELECT COUNT (*) itemcount from [dbo].[salesdata_2012]

Query result


To view all product items and the number of sales for each item, run;

SELECT COUNT(*) itemcount, productline FROM [dbo].[salesdata_2012]
GROUP BY productline
ORDER BY itemcount DESC
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Query result

itemcount productline
967 Classic Cars
607 Vintage Cars
331 Motorcycles
306 Planes
301 Trucks and Buses
234 Ships
77 Trains

Additional Reading


In this article, you deployed an SQL Server on AWS RDS, connected locally using SSMS, loaded data into the table, and queried the data.

I hope you found this post to be informative.

I'd love to connect with you at Twitter | LinkedIn | GitHub

See you in my next article. Take care!!!

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