Hello All,
We currently have financial systems transaction data streams to Oracle exadata(X9) on-premise. This database supports processing of 400million transactions per day. A single transaction for us is a combination of 7-8 inserts into different transaction tables with Indexes , unique constraints etc defined on those. The transactions processed/committed in batches(~1000 batch size) in the database. And this system persists data for ~6 months. We do have all sorts of OLAP(daily/monthly batch reports running) applications run on the same database along with some user facing UI applications showing customer transactions. So it's basically currently serving a hybrid workload and is one stop solution for all use cases.
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. Please advice?
Although it's not exactly related to opensource postgre but want to ask this question here to understand colleagues' view, considering having decades of experience in the database world, We want some guidance, if the below design looks okay for our customer use case.
We currently have financial systems transaction data streams to Oracle exadata(X9) on-premise. This database supports processing of 400million transactions per day. A single transaction for us is a combination of 7-8 inserts into different transaction tables with Indexes , unique constraints etc defined on those. The transactions processed/committed in batches(~1000 batch size) in the database. And this system persists data for ~6 months. We do have all sorts of OLAP(daily/monthly batch reports running) applications run on the same database along with some user facing UI applications showing customer transactions. So it's basically currently serving a hybrid workload and is one stop solution for all use cases.
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. Please advice?
