Data Processing Engine Best Practices for Data Sync in CRM Analytics
After a batch job is created to run a Data Processing Engine definition with CRM
Analytics runtime, new and updated records from since the last run are synced in CRM Analytics. To
optimize the data sync process during definition runs, adopt a data sync strategy that works best
of your company.
Based on your org's daily data volumes, you can adopt either of these incremental data sync
strategies for objects that are used as data sources in definitions:
Frequency
Best For
Hourly
When more than 2 million records are created or updated every hour.
Daily
When less than 2 million records are created or updated every hour.
During peak business periods, such as festive seasons and financial year-ends, you can
run manual full syncs daily or weekly. CRM Analytics also provides the option for scheduled
periodic full syncs. Full syncs help ensure data integrity and reduce data drift.
Historical Data Filtering
Based on the data protection and privacy regulations of your region, you may need to store
customer data for an extended period of time. To make sure that Data Processing Engine
definitions don't consider historical data that isn't eligible for transformation, set up data
sync filters for objects to make sure definition runs ignore unnecessary data.
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