Could you perhaps provide a bit more context so we can level with your situation? What are you trying to achieve? What kind of use cases do you have? What data? Etc.

Happy to help!

• use more strict filters (Timeframe, Doc Types, Company Codes, ...)
• Do not extract all columns of a table. Just select the ones you really need
• One central data pool for data extractions. Export / Import data into other data pools
• Cleanup not necessary tmp tables at the end of your transformations

Remove the Irrlevent/Unsused columns from the table

If you are extracting the data from Views instead of tables, check the Data types

Add additional filter based on language and other fields

Convert data model tables into the Views

[Entered by Bryan Carter]
Of exceeding dismay, Celonis does not do Performance Optimization / Data Normalizing in their Marketplace soluions.
It almost seems this is done with intent as to cause more APC/Data Consumption space needs to seemingly ‘upsell’ APC space.

Of Celonis offiering their specific Marketplace solutions, they need to remove all unneeded/unnecessary columns from their extractions.

We are stuck with a huge tech-debt now trying to remove all unneeded/unnecessary columns from the orig data source extraction, of which are solutions direct from Celonis Marketplace.This should not be a tech debt burden (translation: expesive human resource costs) placed on the Celonis customser.