How to Transform and Integrate Data into Salesforce Einstein...
Before data is loaded to a target destination, it must be transformed to meet any format and structural requirements of the target database. This involves cleaning the data, applying business rules, checking for data integrity, joining data from two sources, and more. In most cases, one or more transformations are required to meet the technical and operational requirements, such as joining, concatenating, dis-aggregation, lookup, transposing data, validation, and others.
How to Transform and Integrate Data into Salesforce Einstein...
Get to Know Data sets
An informational index is a gathering of related, discrete things of related information that might be gotten too exclusively or in mix or oversaw in general element.
An informational collection is composed into some sort of information structure. It may be might contain a gathering of business information like names, compensations, contact data, deals figures, and so on...
Transformations for Analytics Data flows
A change alludes to the control of information. You can add changes to a data flow to extricate information from Salesforce objects or data sets, change data sets that contain Salesforce or outside information, and enroll data sets.
A couple transformation underneath identified with data flow:
Append Transformation:
The add change consolidates columns from different data sets into a solitary data set.
Augment Transformation: An info data set by joining segments from another related data set.
Compute Expression Transformation:
It empowers you to add determined fields to a data set. The qualities for got fields aren’t extricated from the information source.
Compute Relative Transformation:
The above and register relative are same. In any case, it performs computations in view of fields inside a similar column.
To dissect inclines in your information by adding determined fields to a data set in light of esteems in different columns.
Delta Transformation:
When Analytics processes dates, it creates the following epoch date columns for each date processed:
It ascertains changes in the estimation of a measure segment in a dataset over some stretch of time.
Export Transformation:
It makes an information record and an outline document from information in a predefined source hub in your data flow. It would be ideal if you look at our post in regards to Data Transformation & data Integration? What’s more, on the off chance that you found this post important, Make without question, you can associate for more tips, tricks and strategies for effectively to uncover exceptional piece of learning from your data.
