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0/196Learned
CRM Analytics and Einstein Discovery Consultant
Study Checklist
Checklist progress
0/196Learned
Given data sources, use Data Manager to extract and load the data into the CRM Analytics application to create datasets.
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Given business needs and consolidated data, implement refreshes for data syncs and dataflows/recipes while keeping limits and considerations in mind.
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Given business/user requirements, perform data transformations in dataflows/recipes.
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What is the difference between a dataflow and a recipe in CRM Analytics?
Learn this conceptGiven user requirements or ease of use strategies, manage dataset extended metadata (XMD) by editing labels, values, and colors.
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Implement delivery management strategies in dataflows/recipes including versioning and conversion.
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Dataflows and recipes are tools used to create datasets in CRM Analytics, but they serve different primary functions in the data layer. Dataflows are best for combining data from multiple sources, whereas recipes are designed for transforming and preparing data. The choice depends on whether you need to merge sources or focus on data transformation and performance.