How to Seamlessly Automate Data Migration from AWS RDS to Redshift Using AWS Glue

Forget complex, brittle ETL scripts—discover a streamlined, automated approach to RDS-to-Redshift migration with AWS Glue that saves time and improves reliability without sacrificing control.

Efficiently migrating data from AWS RDS to Amazon Redshift is critical for enabling scalable analytics and high-performance querying. Automating this process with AWS Glue minimizes manual overhead, reduces errors, and ensures consistent data pipelines that empower data-driven decision-making.

In this post, I’ll walk you through how to leverage AWS Glue to automate your data migration from RDS to Redshift. By the end, you’ll have a clear, practical framework you can customize for your own data workflows.

Why Automate Data Migration with AWS Glue?

Traditionally, migrating data from an OLTP system like RDS into a data warehouse such as Redshift requires custom ETL scripts or manual export/import jobs. These approaches can be brittle:

• Manual intervention leads to inconsistent or delayed updates
• Custom scripts require maintenance and don’t scale well
• Errors during transformation or loading can cause incomplete datasets

AWS Glue is a fully managed ETL service that simplifies this process by handling schema discovery, job scheduling, and scaling behind the scenes. It supports native integration with both RDS and Redshift, enabling you to build repeatable pipelines with minimal code.

Step-by-Step Guide to Automate RDS-to-Redshift Data Migration Using AWS Glue

1. Prepare Your Environment

Before diving into Glue:

• Ensure your AWS RDS instance (MySQL, PostgreSQL, etc.) is accessible from AWS Glue. This often means placing both in the same VPC or configuring appropriate networking setup.
• Have an Amazon Redshift cluster up and running.
• Ensure your IAM roles have the necessary permissions for Glue to access RDS and Redshift.

2. Configure a Connection in AWS Glue for RDS

AWS Glue uses connections to securely connect to data sources.

• Go to the AWS Glue Console > Connections > Add connection.
• Choose connection type: JDBC.
• Provide the JDBC URL for your RDS database (e.g., jdbc:postgresql://your-rds-endpoint:5432/yourdb)
• Enter credentials (username/password) for accessing your database.
• Specify VPC/subnet/security group so Glue can communicate with your RDS database.

3. Create a Crawler to Catalog Your RDS Data

AWS Glue automatically creates metadata about your data through Crawlers.

• Under Crawlers > Add Crawler.
• Set crawler source type as JDBC connection pointing to RDS.
• Select tables or schemas you want cataloged.
• Run the crawler — this populates the Glue Data Catalog which tracks schema info.

4. Define Your Target in Redshift

Make sure your Redshift cluster has:

• A target schema/database where data will be loaded.
• Appropriate IAM roles attached so it can be accessed by Glue jobs.

You might pre-create tables in Redshift matching your source schema or configure your ETL job for dynamic table creation (depending on workflow).

5. Create an AWS Glue ETL Job

Now comes the core automation part: setting up a Glue Job that extracts from RDS, transforms if necessary, and loads into Redshift.

Here’s how:

• In the AWS Glue console, create a new Job.
• Choose Spark or Python Shell jobs depending on complexity (Spark is typically recommended).
• Select previously created connections and Data Catalog tables as source.

Example PySpark snippet inside the job script:

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from awsglue.context import GlueContext
from pyspark.context import SparkContext
from pyspark.sql import DataFrameWriter

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session

# Read from cataloged table (RDS)
datasource0 = glueContext.create_dynamic_frame.from_catalog(
    database="rds_database_name",
    table_name="your_table_name",
    transformation_ctx="datasource0"
)

# Apply any transformations if needed - e.g., filter columns
applymapping1 = ApplyMapping.apply(
    frame=datasource0,
    mappings=[("id","int","id","int"), ("name","string","name","string"), ("timestamp","timestamp","timestamp","timestamp")],
    transformation_ctx="applymapping1"
)

# Write into Redshift using JDBC connection string and credentials securely stored in secrets manager or param store
glueContext.write_dynamic_frame.from_jdbc_conf(
    frame=applymapping1,
    catalog_connection="redshift_connection",
    connection_options={"dbtable": "target_schema.target_table", "database": "dev"},
    redshift_tmp_dir="s3://your-temp-bucket/temp-dir/",
    transformation_ctx="datasink"
)

Key notes:

• Use write_dynamic_frame.from_jdbc_conf() as shown for direct write into Redshift via JDBC.
• The parameter redshift_tmp_dir points to S3 location needed by Spark/Glue for temporary storage while loading into Redshift.

6. Schedule & Monitor Your Glue Job

You can:

• Schedule this job using AWS Glue triggers (time-based/frequency triggers).
• Trigger it manually or via lambda events if migration needs tie-ins with other workflows.

Use the AWS Glue Console or CloudWatch logs for monitoring job progress and troubleshooting failures.

Additional Tips for Robust Pipelines

Conclusion

By automating your data migration from AWS RDS to Amazon Redshift using AWS Glue, you transform what once was a manual, error-prone chore into a reliable component of your analytics infrastructure. This approach allows you to focus more on leveraging insights rather than managing ETL pipelines.

Start today by setting up simple connection crawlers and jobs in AWS Glue—expand incrementally with transformations and automation suited exactly for your use case!

If you’ve worked with similar migrations or want me to share sample project repos—drop me a comment below!

Happy migrating! 🚀