![]() In using Chartio, we can do all of the above without writing any SQL but leveraging the Data Explorer and the Data Pipeline features. Then after adding a PIVOT DATA step into the Data Pipeline, we’ll get a table properly arranged in the proper format to set up a line chart showing how clicks are compared over time. When you piece all three of those columns for one SELECT STATEMENT and throw in the rest of the necessary pieces to build a SQL query, it all take shape below. The resulting table of this CASE STATEMENT with corresponding emails alone. ![]() "Provider" = 'Gmail' THEN 'Gmail'Īnd, the else statement would be ‘Other’ for every other email address provider.
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