Abstract: Techniques and solutions are described for performing analytics on, or generating displays based on, data retrieved from a plurality of data sources, where the data sources can use one or both of different execution formats or different data schemas. For selected data, one or more analytic queries are generated. Analytic query results are provided in a standardized schema. A blend query is executed against data from the plurality of data sources, including the analytic query results in the standardized schema. Disclosed technologies can facilitate the use of data maintained in different formats or maintained in data sources that have different execution formats or protocols without requiring data replication among the data sources. The disclosed technologies can also provide a platform to which new data sources can easily be added, and can facilitate the use of multiple data sources by non-technical users.

Abstract: Techniques and solutions are described for performing analytics on, or generating displays based on, data retrieved from a plurality of data sources, where the data sources can use one or both of different execution formats or different data schemas. For selected data, one or more analytic queries are generated. Analytic query results are provided in a standardized schema. A blend query is executed against data from the plurality of data sources, including the analytic query results in the standardized schema. Disclosed technologies can facilitate the use of data maintained in different formats or maintained in data sources that have different execution formats or protocols without requiring data replication among the data sources. The disclosed technologies can also provide a platform to which new data sources can easily be added, and can facilitate the use of multiple data sources by non-technical users.

Abstract: Embodiments provide a multi-buffering system in an application layer to support unified read/write access to different data source types of an underlying database layer. In a particular embodiment, the database layer comprises both a multi-dimensional data source (e.g. a cube), and another data source type (e.g. a Data Store Object—DSO). An abstract buffer component of the multi-buffering system is specialized into a delta buffer configured to read/write data of the cube, and into an after image buffer configured to read/write data of the DSO. A storage mechanism component of the multi-buffering system is specialized into a cache store configured to read/write data of the cube, and a calculation scenario store configured to read/write data of the DSO. The after image buffer may support certain aggregation rules during a DSO read/write operation. The calculation scenario store may leverage capabilities of an in memory database during a DSO read/write operation.

Abstract: Embodiments provide a multi-buffering system in an application layer to support unified read/write access to different data source types of an underlying database layer. In a particular embodiment, the database layer comprises both a multi-dimensional data source (e.g. a cube), and another data source type (e.g. a Data Store Object—DSO). An abstract buffer component of the multi-buffering system is specialized into a delta buffer configured to read/write data of the cube, and into an after image buffer configured to read/write data of the DSO. A storage mechanism component of the multi-buffering system is specialized into a cache store configured to read/write data of the cube, and a calculation scenario store configured to read/write data of the DSO. The after image buffer may support certain aggregation rules during a DSO read/write operation. The calculation scenario store may leverage capabilities of an in memory database during a DSO read/write operation.