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Migrating Amazon Redshift Provisioned Cluster to Amazon Redshift Serverless

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Amazon Redshift has long been a popular choice for data warehousing, offering a scalable, high-performance solution for analytics workloads. However, as organizations evolve and data needs change, there’s a growing interest in adopting serverless architectures to optimize costs and simplify management. Migrating from Amazon Redshift provisioned to serverless can be a strategic move, and in this comprehensive guide, we’ll walk you through the detailed steps involved in this migration process.


Amazon Redshift Provisioned

This is a traditional data warehousing approach where compute and storage resources are pre-allocated based on anticipated workloads. It provides control and predictability but may lead to over-provisioning during periods of low usage.

Amazon Redshift Serverless

A serverless data warehouse solution that automatically scales resources based on the workload. It eliminates manual capacity planning and allows organizations to pay only for the processing and storage resources they consume. This is particularly beneficial for variable workloads.

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This table summarizes the key aspects of Amazon Redshift Provisioned and Serverless, offering a clear comparison of their features and suitability for different scenarios.



  • AWS Account: Ensure you can access an AWS account with the necessary permissions to create and manage Redshift clusters.
  • Amazon Redshift provisioned cluster


Step-by-Step Guide

Step 1: Create a Snapshot existing Amazon provisioned Cluster

Click on “Clusters” on the left side of the Amazon Redshift page, then Select the desired provisioned cluster, navigate to the “Actions” dropdown, and choose “Create Snapshot” to capture the current state for backup purposes.


Step 2: Create an Amazon Redshift Serverless Cluster

Click on “Clusters”, then select “Create Cluster”, and choose “Serverless” as the cluster type. Provide essential information, such as Cluster Identifier, Database Name, Master Username, and Password, while creating the serverless cluster. Review the configuration and initiate the cluster creation process.


Step 3: Migrate the Amazon Redshift provisioned to serverless

  • In the AWS Management Console, go to “Amazon Redshift”.
  • Select “Snapshots” from the left-side menu to view available snapshots.


  • Click on the snapshot corresponding to the provisioned Redshift cluster for migration.
  • In the dropdown menu, select “Restore Snapshot” and choose “Restore to Serverless Namespace”.
  • Under “Target Namespace”, select the desired namespace for migration.
  • Click “Restore” to initiate the migration process. Once it is successful, we can validate it.



Step 4: Validate
Here, redshift-cluster-1 is migrated successfully, as shown below.



Migrating from Amazon Redshift provisioned to serverless can bring numerous benefits, including cost savings, improved scalability, and simplified management.

Following the detailed steps outlined in this guide and considering best practices, you can successfully transition to a serverless data warehousing architecture. Remember that each migration is unique, so continuously monitor and optimize your serverless cluster based on your organization’s evolving needs.

Drop a query if you have any questions regarding Amazon Redshift and we will get back to you quickly.

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1. Can I switch from provisioned to serverless without data migration?

ANS: – Migration from provisioned to serverless is essential. This involves backing up data from the provisioned cluster and restoring it into the serverless cluster.

2. How often should I monitor and adjust resources in a serverless environment?

ANS: – Continuous monitoring is recommended to ensure optimal performance and cost efficiency. Set up Amazon CloudWatch Alarms to receive notifications based on key metrics and adjust scaling policies as needed.

3. What steps should be taken to minimize downtime during the migration?

ANS: – To reduce downtime, schedule migration during off-peak hours. Employ a pilot approach, first migrating a subset of workloads for validation before transitioning the entire workload.


Anusha works as Research Associate at CloudThat. She is an enthusiastic person about learning new technologies and her interest is inclined towards AWS and DataScience.



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