AWS, Cloud Computing

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Comparison between Amazon RDS and Amazon Redshift

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AWS (Amazon Web Services) has emerged as a major force in the quickly changing world of cloud computing, providing a full range of services to meet the various requirements of companies and developers. Amazon RDS and Amazon Redshift, two of their well-known database services, are crucial for keeping and analyzing data. However, they were created for different uses and are best in different fields. To assist you in choosing the service that best meets your unique data management and analytics needs, we compare Amazon RDS versus Amazon Redshift in this article as we delve into AWS database options.


Amazon RDS:

Amazon RDS is a managed relational database service that excels in transactional applications and guarantees data integrity, whereas Amazon Redshift is a fully managed data warehousing service that is optimized for analytical processing on sizable datasets. You can employ this comparison to help you pick the best solution for your data needs by learning more about their capabilities, use cases, scalability, pricing models, and other factors. Understanding the distinctions between these services is crucial for optimizing your data infrastructure on AWS, whether you’re a startup launching a new application or a company handling enormous amounts of historical data.

Six well-known database engines are available through Amazon RDS, a managed relational database service, including Amazon Aurora, MySQL, MariaDB, Oracle, Microsoft SQL Server, and PostgreSQL. This implies that you may utilize Amazon RDS with the same code, programs, and tools you currently use with your existing databases. Routine database functions, including provisioning, patching, backup, recovery, failure detection, and repair, are handled by Amazon RDS.

Amazon Redshift:

A petabyte-scale data warehouse service called Amazon Redshift is quick, potent, and completely managed in the cloud. The administration of traditional data warehouses takes a lot of time and resources, especially for huge datasets. Building, maintaining, and expanding a self-managed, on-premises data warehouse is a significant financial expense. In addition to dramatically reducing the cost of a data warehouse, Amazon Redshift also makes it simple to analyze huge amounts of data quickly.

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Difference between Amazon RDS and Amazon Redshift


Use Cases

  • Applications that require transactional processing, such as E-Commerce platforms, order management systems, and banking applications, are best served by Amazon RDS. It supports ACID compliance and maintains data consistency and integrity for these kinds of applications.
  • Websites and blogs that use content management systems (CMS) frequently use Amazon RDS to store and manage structured content, user data, and transactional logs.
  • Amazon RDS’s support for structured data and dependable transactions makes it useful for enterprise-level applications like customer relationship management (CRM), enterprise resource planning (ERP), and human resources management systems (HRMS).
  • Amazon RDS can be simpler and more affordable if you create a unique application with particular relational database needs but don’t want Amazon Redshift’s comprehensive analytical features.
  • Amazon RDS might outperform Amazon Redshift, designed for analytics, in cases with a high frequency of read-and-write operations and complicated data linkages.


Amazon RDS and Amazon Redshift cater to distinct database needs within the AWS environment. While Amazon RDS excels in managing transactional workloads, ensuring data consistency, and supporting applications like E-Commerce platforms and content management systems, Amazon Redshift is optimized for processing complex analytical queries on large datasets.

Leveraging the strengths of both services through integration, facilitated by AWS Glue and other migration tools, can provide a comprehensive and efficient data management solution. Choosing between Amazon RDS and Amazon Redshift should be based on the specific requirements of your business, ensuring an optimal fit for your data infrastructure and analytical needs within the AWS ecosystem. 

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

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1. Can I use Amazon Redshift for transactional workloads?

ANS: – Amazon Redshift is frequently used for tasks related to business analytics and is optimized for quick querying of huge datasets. Amazon Redshift can be used for transactional workloads, but as it is not a fully transactional database, it is not advised. Amazon RDS may be a better choice if you’re searching for a fully managed relational database service to set up and scale relational databases in the cloud easily.

2. Is it possible to put together Amazon RDS and Amazon Redshift?

ANS: – Amazon RDS and Amazon Redshift can be used in tandem. For instance, you can use Redshift to store historical data and RDS to keep transactional data. A data pipeline may be built using AWS Glue to transfer your data from Amazon RDS to Amazon Redshift for analysis.

3. What is the most effective method for moving data from Amazon RDS to Amazon Redshift?

ANS: – Depending on your unique needs and requirements, you can migrate data from Amazon RDS to Amazon Redshift in several ways. However, a few typical techniques consist of:

  • Using the AWS Data Migration Service (DMS): The AWS DMS service enables you to move data across several sources and targets, such as Amazon RDS and Amazon Redshift.
  • Using AWS Glue: A service called AWS Glue can assist you in building and maintaining data pipelines. A data pipeline that transfers data from Amazon RDS to Amazon Redshift can be built using AWS Glue.
  • Using a third-party migration tool: You can move data from Amazon RDS to Amazon Redshift using a variety of third-party technologies.

WRITTEN BY Ritushree Dutta



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