In Redshift WLM, your process will be throttled, where as in EMR you will be charged for the aws resources. Like the previous comment, there is a trade off, if you dont want block the user queries, either use Redshift WLM or EMR. In this story I will focus on the data staging operation rather than on the data transformation itself.

Amazon Redshift vs Amazon S3: What are the differences? Redshift stores snapshots internally in Amazon S3 by using an encrypted Secure Sockets Layer (SSL) connection. The difference in structure and design of these database services extends to the pricing model also. Developers describe Amazon Redshift as "Fast, fully managed, petabyte-scale data warehouse service". In Comparing Amazon s3 to Redshift to RDS, an in-depth look at exploring their key features and functions becomes useful. Compare Amazon Redshift vs Amazon Simple Storage Service (S3) head-to-head across pricing, user satisfaction, and features, using data from actual users. For this reason, a typical scenario is the migration of raw data from S3 to Redshift. Why pay to store that data in Redshift when moving it to external tables on AWS S3 and query data with Spectrum is an option? Cost With regard to all basic table scans and small aggregations, Amazon Athena stands out as more effective in comparison with Amazon Redshift. This can save you a big dollars since you can get lifecycle data out of Redshift to S3. Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools.
If you reach the free snapshot storage limit, you incur charges for additional storage at your normal rate. For example, let’s say you have a 100 GB transactional table of infrequently accessed data.

I am using S3 -> Redshift, and the performance is pretty good. Amazon Redshift Vs DynamoDB – Pricing. Amazon Simple Storage Service (Amazon S3) Amazon’s Simple Storage Service (Amazon S3) is a cloud storage service comes that allows you to … By using the AWS Data Pipeline , data collecting on RDS databases, users interact with that side of your infrastructure with Amazon EC2 , Amazon S3 jobs could then move the data in bulk to your Redshift cluster to run those “heavy” queries. These results were calculated after copying the data set from S3 to Redshift which took around 25 seconds, and will vary as per the size of the data set. Final Notes: Performance vs. Amazon provides free storage for snapshots in an amount equal to the storage capacity of the backed-up cluster. Redshift is Amazon’s analytic database with ParAccel technology this is designed for heavy lifting, crunching big data queries against large datasets. Redshift pricing is defined in terms of instances and hourly usage, while DynamoDB pricing is defined in terms of requests and capacity units. Hopefully, the comparison below would help identify which platform offers the best requirements to match your needs.