Abstract: Functionality configuration for applications is provided by a configuration service. An application may register with a configuration service for functionality configuration. A configuration model may be created for the application and deployed to the application for use in configuring the functionality and behavior of the application. The configuration model may be stored by the configuration service and maintained at the configuration service. The configuration model may be provided to multiple additional applications, or customized and provided to additional applications. Use of a single or related configuration models may standardize functionality and maintenance across related applications.

Abstract: Examples of partitioning a group of related database tables are provided herein. A database table in a group of related database tables can be designated as a lead database table. A partitioning field can also be determined by which database tables in the group are partitioned. A data load, with respect to the partitioning field, of the lead database table can be calculated. The data load can include a data distribution across different values of the partitioning field. A group partitioning scheme can be determined based on the data load of the lead database table. The database tables of the group can then be partitioned according to the group partitioning scheme.

Abstract: Functionality configuration for applications is provided by a configuration service. An application may register with a configuration service for functionality configuration. A configuration model may be created for the application and deployed to the application for use in configuring the functionality and behavior of the application. The configuration model may be stored by the configuration service and maintained at the configuration service. The configuration model may be provided to multiple additional applications, or customized and provided to additional applications. Use of a single or related configuration models may standardize functionality and maintenance across related applications.

Abstract: Embodiments relate to data archiving utilizing an existing big data platform (e.g., HADOOP) as a cost-effective target infrastructure for storage. Particular embodiments construct a logical structure (hereafter, “vault”) in the big data platform so that a source, type, and context of the data is maintained, and metadata can be added to aid searching for snapshots according to a given time, version, and other considerations. A vaulting process transforms relationally stored data in an object view to allow for object-based retrieval or object-wise operations (such as destruction due to legal data privacy reasons), and provide references to also store unstructured data (e.g., sensor data, documents, streams) as attachments. A legacy archive extractor provides extraction services for existing archives, so that extracted information is stored in the same vault. This allows for cross queries over legacy data and data from other sources, facilitating the application of new analysis techniques by data scientists.

Abstract: A system, a computer-implemented method, and a computer readable medium having stored thereon a computer executable program code for providing access to a database on the system. The database comprises entries stored across partitions. The system comprises a first storage device, a second storage device, and a computing device. The first storage device comprises one partition of the partitions. The second storage device comprises the other partitions except the one of the first storage device. Each of the partitions has a respective partition identification. Each of the entries comprises at least one data value indicative of allocation of the each of the entries in one of the partitions. Each of the entries is stored in one or more data rows of data tables stored in the database. Each of the data rows comprises a respective primary key for identification of that data row.

Abstract: Examples of partitioning a group of related database tables are provided herein. A database table in a group of related database tables can be designated as a lead database table. A partitioning field can also be determined by which database tables in the group are partitioned. A data load, with respect to the partitioning field, of the lead database table can be calculated. The data load can include a data distribution across different values of the partitioning field. A group partitioning scheme can be determined based on the data load of the lead database table. The database tables of the group can then be partitioned according to the group partitioning scheme.

Abstract: Embodiments relate to data archiving utilizing an existing big data platform (e.g., HADOOP) as a cost-effective target infrastructure for storage. Particular embodiments construct a logical structure (hereafter, “vault”) in the big data platform so that a source, type, and context of the data is maintained, and metadata can be added to aid searching for snapshots according to a given time, version, and other considerations. A vaulting process transforms relationally stored data in an object view to allow for object-based retrieval or object-wise operations (such as destruction due to legal data privacy reasons), and provide references to also store unstructured data (e.g., sensor data, documents, streams) as attachments. A legacy archive extractor provides extraction services for existing archives, so that extracted information is stored in the same vault. This allows for cross queries over legacy data and data from other sources, facilitating the application of new analysis techniques by data scientists.

Abstract: A system, a computer-implemented method, and a computer readable medium having stored thereon a computer executable program code for providing access to a database on the system. The database comprises entries stored across partitions. The system comprises a first storage device, a second storage device, and a computing device. The first storage device comprises one partition of the partitions. The second storage device comprises the other partitions except the one of the first storage device. Each of the partitions has a respective partition identification. Each of the entries comprises at least one data value indicative of allocation of the each of the entries in one of the partitions. Each of the entries is stored in one or more data rows of data tables stored in the database. Each of the data rows comprises a respective primary key for identification of that data row.

Abstract: Various embodiments of systems and methods for controlling a performance and scalability of a software application during development are described herein. A method includes generating a test environment to repeatedly trigger a test on an executable software module from a beginning of a development phase. In response to triggering, reading a set of input data, evaluating a measurement result corresponding to each input data, determining a relation of the measurement results as one of a linear and a non-linear relative to the set of input data, and initiating a corrective action for the software module if the non-linear relation is determined. The method employs the linearity (linear and non-linear relation) as a key performance indicator (KPI) to control the performance and/or scalability of the software module during development.