Abstract: Systems and methods described herein relate to contextual navigation for embedded analytics integrations. An embedded analytics user interface is presented within a first application. The embedded analytics user interface is provided by a second application and includes a plurality of user-selectable data items of the second application. A user selection of a first data item from among the plurality of user-selectable data items is detected via the embedded analytics user interface. Context data related to the user selection is identified. The context data is identified based at least partially on a stored mapping between the first data item of the second application and a second data item of the first application. One or more navigation options associated with the context data are obtained from the first application. An interface element that includes the one or more navigation options is presented within the embedded analytics user interface.

Abstract: Systems, methods, and computer media for visualizing data are provided herein. The described examples allow one-dimensional data visualizations generated using visualization tools and backend components to be displayed in response to a request to view data that includes one or more sets of Cartesian columns. A data visualization request can specify data for inclusion in a data visualization and properties for the visualization, including transpose factors for data sources. The data sources having transpose factors can be cloned to represent respective combinations of the data source with each transpose factor, and then joined to other cloned or non-cloned data sources to generate the resulting one-dimensional visualization.

Abstract: Systems, methods, and computer media for visualizing data are provided herein. The described examples allow one-dimensional data visualizations generated using visualization tools and backend components to be displayed in response to a request to view data that includes one or more sets of Cartesian columns. A data visualization request can specify data for inclusion in a data visualization and properties for the visualization, including transpose factors for data sources. The data sources having transpose factors can be cloned to represent respective combinations of the data source with each transpose factor, and then joined to other cloned or non-cloned data sources to generate the resulting one-dimensional visualization.