A data source that you add must have certain characteristics.
The data must be from a Microsoft Excel spreadsheet file, with the XLSX extension (signifying a Microsoft Office Open XML Workbook file) and a maximum size of 50 MB.
Note:
Before you can upload a Microsoft Excel file as a data source, it must be structured in a data oriented way. This includes avoiding null values in dimension attributes and deleting aggregate rows. Also, ensure that all values for a dimension are placed in a single column. For example, don’t include a column for January, another for February, and so on.A data source either extends a dimension by adding attributes or extends facts by adding measures and optional attributes. Hierarchies cannot be defined in external data sources.
Various criteria apply to sources that extend a dimension by adding attributes:
Matches can be made only to a single dimension.
The set of values in matched columns must be unique in the external data source. For example, if the data source matches on zip code, then zip codes in the external source must be unique.
Matches can be between one or composite columns. An example of a one-column match is that “product key” matches “product key”. For composite columns, an example is that “company” matches “company” and “business unit” matches “business unit”.
All other columns must be attributes.
Various criteria apply to sources that add measures:
Matches can be made to one or more dimensions.
The set of values in matched columns does not need to be unique in the external data source. For example, if the data source is a set of sales matched to date, customer, and product, then you can have multiple sales of a product to a customer on the same day.
Matches can be between one or composite columns. An example of a one-column match is that “product key” matches “product key”. For composite columns, an example is that “company” matches “company” and “business unit” matches “business unit”.
The data source that adds measures can include attributes. You can use these attributes only alongside the external measures and not alongside the curated measures in visualizations. For example, when you add a source with the sales figures for a new business, you can match these new business sales to an existing time dimension and nothing else. The external data might include information about the products sold by this new business. You can show the sales for the existing business with those of the new business by time, but you can’t show the old business revenue by new business products, nor new business revenue by old business products. You can show new business revenue by time and new business products.
When you save a project or an analysis, the permissions are synchronized between the project or analysis and the external sources that it uses. If you share the project or analysis with other users, then the external sources are also shared with those same users.
Be aware of the effect of mixing columns that don’t match from data sources with a subject area. For example, suppose that you have a subject area that includes a Product attribute and an Orders attribute. You create a pivot table that includes those two attributes with a Revenue measure from a data source. The data source that includes the Revenue measure includes a Product column that matches the Product attribute in the subject area, but the data sources does not include an Orders columns. No Revenue data is shown in the pivot table for the Orders attribute.