Many of the applications are moving from on premise to AWS cloud as part of modernization journey and AWS being chosen cloud partner also the product is expected to expand across more regions and this system is expected to serve increase in the transaction volume. And also we have a requirement to persist transaction data for ~10years to have those available for analytics/data science use cases.
So the team is thinking of splitting it into two parts 1)OLTP type use case in which we will persist/write the transaction data faster and show it to the UI related apps , in near real time/quickest possible time. and this database will store Max 60-90 days of transaction data. Not sure if we have an option of Oracle exadata equivalent on AWS, so team planning of using/experimenting with Aurora postgres. Please correct me, if there are any other options we should use otherwise?
2)Then move the data beyond ~90 days into another database or object storage S3 which will keep it there for ~10 years and will be queryable using the necessary API's. That is supposed to cater to Olap/analytics/data science use cases etc.
Is the above design is okay? and also in regards to the second point above i.e. persisting the historical data (that to be in queryable state), should we go for some database like snowflake or should just keep it on S3 as is and make those queryable through APIs.
"OK" is relative, but it's what we did in a similar situation: two years of data on-line and 5 years of data in compressed files in S3. (We're required to keep data for 7 years, but they never ask for records more than 2 years old. If they ever do, we'll manually load the relevant data back into PG.)
(I can't imagine that querying billions of unindexed flat-file records via S3 would be fast.)
How often do end users look for data more than 90 days old? Two years old?
How quickly does the old data need to be available?
Maybe a four-tiered system of PG-Snowflake-S3-S3_Glacier would balance speed and cost (or maybe Snowflake would just drive up costs).