Data-modeling-made-easy

Wednesday, September 2, 2026

Guide: Migrating MySQL to AWS RDS SQL Server

Step 1: Define PK-Range Slicing for Extraction
- Break down massive MySQL tables into manageable, smaller chunks using Primary Key (PK) ranges so your export runs smoothly without crashing your system.
Step 2: Export Data to CSV Chunks
- Export those sliced chunks from MySQL into individual CSV files saved securely on your local computer hard drive.
Step 3: Create the Empty Table in AWS RDS SQL Server
- Use SQL Server Management Studio (SSMS) to connect to your AWS RDS SQL Server database and run a CREATE TABLE script (loading it as an unordered "heap" for maximum ingestion speed).
Step 4: Upload CSV Chunks to AWS S3 (Cloud Storage)
- Create an AWS S3 bucket (cloud folder) and upload your local CSV chunk files into it so AWS can access them.
Step 5: Import Data Using Bulk Insert & S3 Integration
- Configure AWS RDS native backup/restore or credential integration, and run an automated loop script to bulk-insert millions of rows straight from your S3 bucket into your AWS SQL table in seconds.
Step 6: Verify Row Counts and Create Indexes
- Run a quick row-count verification query in SSMS and build a clustered index on your table to make future searches and queries lightning-fast.
Step 7: Track and Compare Counts Using an Excel Sheet
- Maintain a simple migration checklist in Excel or Google Sheets to log your original MySQL counts side-by-side with your final AWS SQL counts to guarantee a 100% successful migration.

What is AWS S3 and why do we need it?
Just like Azure Blob Storage, AWS RDS SQL Server lives in the cloud and cannot read files directly from your personal laptop's hard drive. S3 (Simple Storage Service) acts as a secure cloud folder where we dump your CSV files so AWS SQL can grab them.
What is Bulk Insert in AWS?
Once your files are in S3 and your empty table is ready in AWS RDS, we use high-speed bulk loading commands to dump millions of rows into your database all at once instead of inserting them one by one.

Why this step is needed: Trying to export billions of rows all at once will crash your system. We slice the data using Primary Key ranges (e.g., IDs 1 to 1,000,000) to keep exports manageable.

Why this step is needed: Exporting your sliced ranges into individual CSV files prepares them for cloud staging.

Why this step is needed: You need a destination table ready before data arrives. Load it initially as an unordered heap for speed.
How to do it: Connect via SSMS using your AWS RDS endpoint, open a New Query window, and run your CREATE TABLE script.

Why this step is needed: AWS RDS cannot see your local hard drive, so files must be staged in the cloud.
How to do it: Log into the AWS Management Console, go to S3, click Create bucket, give it a unique name, and upload your CSV chunk files into it.

Why this step is needed: AWS RDS SQL Server uses special built-in stored procedures (like rds_download_from_s3 or native bulk insert options via S3 integration) to securely pull files from your S3 bucket into your database tables.
How to do it: Set up your AWS IAM role/option group for S3 access, open SSMS, and run your bulk insert or migration script pointing to your S3 path.

Why this step is needed: Ensure no data is missing and restore database performance with indexes.
How to do it: Run SELECT COUNT(*) FROM your_table; in SSMS and create your clustered index (e.g., CREATE CLUSTERED INDEX IX_table_id ON your_table (id);).

Why this step is needed: Maintain absolute data integrity proof by comparing source and target counts side-by-side.
How to do it: Log your original MySQL row counts and final AWS RDS row counts in an Excel checklist to verify a 100% successful migration.