Abstract: A system and method for parallel processing of semantically grouped data in data warehouse environments is disclosed. A datastore object having a number of records is generated in a data warehouse application. A hash value is added to each record. The hash value has an integer domain, and is uniformly distributed over the integer domain across the datastore object. A selection table is generated to create a number of tasks based on discrete ranges of the hash value. Then, a transformation routine is executed on each of the number of tasks in parallel to generate an infocube of data that corresponds to each range of the discrete ranges of the hash value.

Abstract: A system and method for parallel processing of semantically grouped data in data warehouse environments is disclosed. A datastore object having a number of records is generated in a data warehouse application. A hash value is added to each record. The hash value has an integer domain, and is uniformly distributed over the integer domain across the datastore object. A selection table is generated to create a number of tasks based on discrete ranges of the hash value. Then, a transformation routine is executed on each of the number of tasks in parallel to generate an infocube of data that corresponds to each range of the discrete ranges of the hash value.

Abstract: Embodiments of the present invention include in-memory processing for data warehouse applications. In one embodiment, data records from a data warehouse application are stored in a data storage structure of an in-memory database. Data received from the data warehouse may be stored in a queue and loaded into the data storage structure according to predefined rules. Stored data records are associated with in-memory database transactions that caused the stored data record to be stored, and may further be associated with transactions that caused the stored data records to be changed. A mapping is generated to associate requests from the data warehouse application with in-memory database transactions. The data warehouse application may retrieve data in a change data format calculated on-the-fly.

Abstract: Embodiments of the present invention include in-memory processing for data warehouse applications. In one embodiment, data records from a data warehouse application are stored in a data storage structure of an in-memory database. Data received from the data warehouse may be stored in a queue and loaded into the data storage structure according to predefined rules. Stored data records are associated with in-memory database transactions that caused the stored data record to be stored, and may further be associated with transactions that caused the stored data records to be changed. A mapping is generated to associate requests from the data warehouse application with in-memory database transactions. The data warehouse application may retrieve data in a change data format calculated on-the-fly.

Abstract: Data can be extracted from one or more data sources. Thereafter, the extracted data can be transformed to a format compatible with a plurality of structurally identical data targets so that the transformed data can be loaded into the plurality of data targets via data transfer processes. The data transfer processes define disjoint data sets among the plurality of data targets. Related apparatus, systems, methods, and articles are also described.

Abstract: Data can be extracted from one or more data sources. Thereafter, the extracted data can be transformed to a format compatible with a plurality of structurally identical data targets so that the transformed data can be loaded into the plurality of data targets via data transfer processes. The data transfer processes define disjoint data sets among the plurality of data targets. Related apparatus, systems, methods, and articles are also described.