Joining Record Types Versus Linking Datasets
In SuiteAnalytics Workbook, joins within a dataset happen through SQL queries running in the background. Specifically, a left outer join runs for each record type you add to a dataset. This means the results include all rows from the primary record type, plus any that match from the joined record type. No aggregation happens, so you might see too many or too few results depending on the relationship between record types and the join order. For more information, see Guidelines for Joining Record Types in SuiteAnalytics Workbook.
In contrast, linked datasets work more like an SQL union. In a union, two queries run to give one set of results, defined by fields that share the same data type in each query. In Workbook, this means each linked dataset runs as a background query, and then the results are aggregated and shown based on the common keys you set in the link.
To sum up: joins in a dataset let you combine and analyze fields from several record types in one set of results. Linked datasets let you analyze and aggregate results from two queries as if they were one. For more information about linking datasets and common keys, see Link Datasets in a Workbook.
To learn more about the differences between joining record types in a dataset and linking datasets in a workbook, see the following table:
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Joining Record Types |
Linking Dataset |
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SQL execution |
Left outer join. Results include all fields from the primary record, plus any matches from the joined records. |
Union. The results from two datasets show up in one visualization, including only distinct values, and aggregation is based on the fields you set as common keys when you create the link. For more information, see Common Keys. |
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Number of data sources |
Unlimited. You can join as many record types as you want, but too many joins can cause performance issues. |
Two datasets. |
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Data duplication |
Data duplication can happen depending on the relationship between record types and the join order. For more information, see Data Duplication Based on Record Joins. |
No. Results only contain distinct values from each dataset, so there is no data duplication. |
|
Aggregation |
No. This is intentional, since datasets are meant to be used as raw data for your visualizations. |
Yes. Aggregation occurs automatically based on the fields you define as common keys and where you place them in the visualization. |
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Sharing |
Yes. After you join record types in a dataset, you can save and share it with others in your account. |
Yes, but only as part of the workbook they're linked in. |
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Requirements |
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