Which service fits the most here?

imho you could do a PoC to see which service is more feasible for you. It really depends on how much data you have, what queries and what load you plan to execute.

AWS Redshift is intended for OLAP on top of peta- or exa-bytes scale handling heavy parallel workload. RS can as well aggregate data from other data sources (jdbc, s3,..). However RS is not OLTP, it requires more static server overhead and extra skills for managing the deployment.

So without more numbers and use cases one cannot advice anything. Cloud is great that you can try and see what fits you.

Answer from gusto2 on Stack Overflow
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Reddit
reddit.com โ€บ r/aws โ€บ deciding between rds and redshift for "data warehouse"
r/aws on Reddit: Deciding between RDS and Redshift for "data warehouse"
January 27, 2023 -

Hi all,

I have a business request to ETL data from several sources into a data warehouse to run BI tools on (Tableau or PowerBI for example).

The dataset isn't that big, 400GB growing 4GB per week. I can't find any good explanation on if I should use RDS or Redshift to store the data.

Can anyone with experience on this topic guide me please?

Thank you!

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TechTarget
techtarget.com โ€บ searchcloudcomputing โ€บ tip โ€บ When-to-use-Amazon-RDS-vs-Redshift
When to use Amazon RDS vs. Redshift | TechTarget
Manages compute and storage cloud infrastructure to support data warehouse and data lake approaches -- enables Redshift to access and analyze large amounts of data stored internally or remotely. Essentially, Amazon RDS and Redshift equip application owners with a programmatic way to store and access data and make it available to end users. While these two AWS database services share similarities, they were built to solve different problems.
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Amazon Athena is well suited for interactive analytics and exploration of data in your data lake or any data source through an extensible connector framework without worrying about ingesting or processing data. Amazon Athena is built on open-source engines and frameworks such as Spark, Presto, and Apache Iceberg, giving customers the flexibility to use Python or SQL or work on open data formats. If customers want to do ***interactive analytics*** using open-source frameworks and data formats, Amazon Athena is a great place to start. Amazon Redshift is a fully managed data warehouse. With its Massively Parallel Processing (MPP) architecture that separates storage and compute and machine learning led automatic optimization capabilities, a data warehouse like Amazon Redshift, whether it's serverless or provisioned, is a great choice for customers that need the best price performance at any scale for ***complex BI and analytics workloads***. Using Amazon Redshift, you can also directly query data in open formats (such as Parquet or ORC) in the Amazon S3 data lake, or query data in operational databases, such as Amazon Aurora and Amazon RDS PostgreSQL and MySQL. Amazon QuickSight is a cloud-scale business intelligence (BI) service that you can use to deliver easy-to-understand insights to the people who you work with, wherever they are. Amazon QuickSight connects to your data in the cloud and combines data from many different sources. Here is a document on the Modern Data architecture with AWS which gives an idea on how the purpose built services can be used together. [https://aws.amazon.com/big-data/datalakes-and-analytics/modern-data-architecture/]
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