Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for memory mapping to enhance data cube performance. In some implementations, a system accesses a data set that includes data to be processed into a data cube. The system generates a memory-mapped data cube that includes a plurality of files including different segments of the data cube. Generating the memory-mapped data cube includes allocating memory-mapped buffers in non-volatile data storage and responding to subsequent memory allocation requests with addresses for the buffers such that components of the data cube are accumulated in the buffers. The memory-mapped data cube is loaded by storing the files of the data cube in disk-based storage, mapping the stored files of the data cube to virtual memory addresses, and caching portions of the data cube in random-access memory.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for memory mapping to enhance data cube performance. In some implementations, a system accesses a data set that includes data to be processed into a data cube. The system generates a memory-mapped data cube that includes a plurality of files including different segments of the data cube. Generating the memory-mapped data cube includes allocating memory-mapped buffers in non-volatile data storage and responding to subsequent memory allocation requests with addresses for the buffers such that components of the data cube are accumulated in the buffers. The memory-mapped data cube is loaded by storing the files of the data cube in disk-based storage, mapping the stored files of the data cube to virtual memory addresses, and caching portions of the data cube in random-access memory.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for enhanced preparation and integration of data sets. In some implementations, data indicating user input that identifies a first data set that includes streaming data and a second data set that includes non-streaming data is received. The first data set and the second data set are integrated to generate a hybrid data set. The data processing system provides access to the hybrid data set through a (i) non-streaming access channel that provides a periodically-refreshed summary of both the streaming data and the non-streaming data and (ii) a streaming access channel that provides a data stream based on combined data of the first data set and the second data set. One or more application programing interfaces are provided. The one or more application programming interfaces allow at least one client device to access the non-streaming access channel and the streaming access channel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enterprise connectivity are described. In one aspect, a method includes the actions of receiving, by a data analysis server and from a web client running on a first device, a request to begin a web session, where the data analysis server is configured to generate queries to a data storage device based on user input to the web client. The actions further include receiving a request to generate a dashboard interface that provides a visualization of a portion of data in the data storage device. The actions further include generating the dashboard interface and a corresponding dashboard file that is viewable outside of the web client. The actions further include transmitting the dashboard file to a second device that includes a data analysis application that is configured to view the dashboard file.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for enhanced preparation and integration of data sets. In some implementations, data indicating user input that identifies a first data set that includes streaming data and a second data set that includes non-streaming data is received. The first data set and the second data set are integrated to generate a hybrid data set. The data processing system provides access to the hybrid data set through a (i) non-streaming access channel that provides a periodically-refreshed summary of both the streaming data and the non-streaming data and (ii) a streaming access channel that provides a data stream based on combined data of the first data set and the second data set. One or more application programing interfaces are provided. The one or more application programming interfaces allow at least one client device to access the non-streaming access channel and the streaming access channel.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for enterprise connectivity are described. In one aspect, a method includes the actions of receiving, by a data analysis server and from a web client running on a first device, a request to begin a web session, where the data analysis server is configured to generate queries to a data storage device based on user input to the web client. The actions further include receiving a request to generate a dashboard interface that provides a visualization of a portion of data in the data storage device. The actions further include generating the dashboard interface and a corresponding dashboard file that is viewable outside of the web client. The actions further include transmitting the dashboard file to a second device that includes a data analysis application that is configured to view the dashboard file.

Abstract: In one implementation, a query is received that is related to data stored in a database that is implemented in computer memory. Based on the received query, attributes of data stored in the database that are relevant to generating a response to the received query are identified. Information that is indicative of attributes for which values are recorded in different tables included in the database is accessed. Based on having accessed this information, tables included in the database that record values for the attributes identified as being relevant to generating a response to the received query are identified. These tables then are joined to create, within computer memory, a new table that reflects relationships between values of attributes identified as being relevant to generating a response to the received query.

Abstract: Database report generation technology for handling a single logical query that requires data that is physically located in two different sources. The system uses intelligent push-down joins, which move data from one source to the other and join data within that source. The system dynamically determines which direction to move data depending on how the data is used in a query.

Abstract: In one implementation, a query is received that is related to data stored in a database that is implemented in computer memory. Based on the received query, attributes of data stored in the database that are relevant to generating a response to the received query are identified. Information that is indicative of attributes for which values are recorded in different tables included in the database is accessed. Based on having accessed this information, tables included in the database that record values for the attributes identified as being relevant to generating a response to the received query are identified. These tables then are joined to create, within computer memory, a new table that reflects relationships between values of attributes identified as being relevant to generating a response to the received query.

Abstract: Database report generation technology for handling a single logical query that requires data that is physically located in two different sources. The system uses intelligent push-down joins, which move data from one source to the other and join data within that source. The system dynamically determines which direction to move data depending on how the data is used in a query.

Abstract: Database report generation technology for handling a single logical query that requires data that is physically located in two different sources. The system uses intelligent push-down joins, which move data from one source to the other and join data within that source. The system dynamically determines which direction to move data depending on how the data is used in a query.