![]() RDS is better for queries that don't span millions of rows, since Redshift has a more sophisticated query optimizer and execution planner. When it comes to performance, Redshift and RDS offer different benefits. ![]() But if your data set is more minor, RDS will be more than sufficient. So, when choosing between the two, it really comes down to the size of your data set. If you're dealing with large amounts of data, Redshift is the way to go. However, RDS should not be discounted it can still scale to a respectable 32 TB. With the ability to scale up to petabytes of data, it's clear that Redshift is built for larger workloads. When it comes to storage capacity and scalability, Redshift definitely has the upper hand. However, if you need more scalability and power, Redshift is worth the investment. Keep in mind that with Redshift, you'll typically use multiple nodes, which will increase the price per hour. If you're looking for a cheaper option for storage and compute, RDS is the way to go. That said, Redshift does offer an elastic resize feature for supported instances, which can minimize downtime. Additionally, Redshift's concurrency scaling option allows it to support virtually unlimited concurrent users without sacrificing performance. ![]() Redshift, on the other hand, has a more complex architecture which makes scaling less seamless. Scaling in RDS is very simple and can be done in just a few minutes via the AWS console. Both Redshift and RDS allow customers to scale as needed in order to meet performance requirements and budget restrictions. When comparing Redshift vs RDS, one of the most important factors to consider is scaling. We will be looking at the most critical factors that a data architect would use to decide between these technologies. Now that we have gone over the AWS Relational database service and Redshift, it is time to compare the two managed services. However, using Redshift in this way is not recommended.
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