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Summary Databases and analyticsarchitectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization.
Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly. When there is a schema change in our production database, Fivetran automatically rebuilds or materializes the new piece of data in a new table. ” 36.
Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly. When there is a schema change in our production database, Fivetran automatically rebuilds or materializes the new piece of data in a new table. ” 36.
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Analytics Architecture 
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