What is a Cloud Pivot Table?

A Cloud Pivot Table allows you to take the raw data from your tech stack and import it as a Pivot table, rather than importing the raw data directly into Google Sheets and then using the Sheets pivot feature. The Coefficient Cloud Pivot Table feature skips the middle steps - saving you time, and keeping the imports focused, and the Workbook/Sheet lightweight. 🙌🏼 The Cloud Pivot Table includes the ability to group by rows, columns, and values - just like the Sheets pivot.

How to Import Using a Cloud Pivot Table (Salesforce)

(This example is an import from Salesforce using “Objects and Fields”. We want to show the Total ARR for Opportunities for each Owner by Quarter)

  1. Open the sidebar and select “Import from…”

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  1. Select “Salesforce” from the list of data sources.

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  1. Select “From Objects & Fields”.

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  2. Select “Opportunity” as the object.

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  1. Click the “Select fields…” button to select the fields to import for your pivot table.

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  2. Add your fields (selected fields will show in blue).

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  3. Your fields will be listed. You can re-order them with drag/drop. Turn on the “Pivot Mode”.

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  4. Scroll down and drag the fields you need for your “Rows”, “Columns”, and “Values”.

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  1. You can set attributes for your pivot table like, “Group by Quarter”, “Year”, “SUM”, “COUNT”, etc.”. Then select, “RUN”.

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  2. Congrats on your Cloud Pivot Import with Coefficient! 🙌🏼

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How to Import Using a Cloud Pivot Table (MySQL)

(This example is a MySQL import using the “Objects and Fields” option. We want to create a pivot table showing the Total Number of each Rating per Release Year from the film_rentals table)

  1. Open the sidebar and select “Import from…”

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  2. Select “MySQL” from the list of data sources.

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  3. Choose “From Tables & Columns”.

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  4. Select the desired table from the list. (e.g. “film_rentals.film”).

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  5. The fields within the table selected in Step 4 would appear for you to select (check/uncheck) for your import. Toggle the “Pivot” option on and “Import”.

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  6. Add the fields for the “ROWS”, “COLUMNS”, and “VALUES”. In this example, the “release_year” in the “ROWS”, the “rating” in the “COLUMNS”, and “film_id” for the (COUNT) “VALUES”. (The expected result should show the number of films depending on their rating (columns) for each release year (rows). )

(Values can be aggregated by “SUM”, “COUNTUNIQUE”, “AVERAGE”, “MAX”, and “MIN”.)