Abstract: A system for data partitioning based on discovery of discrete partitioning information. The system can receive data sets in table format from source system. The data can be stored in the source system to be partitioned and transmitted from the source system to a target system. The system can determine a respective partitioning column for each data set. The system can determine a number of partitions. The system can determine, for each data set, a respective set of discrete values from the plurality of discrete values of the respective partitioning column. The number of the discrete values of the set of discrete values can be based at least in part on the number of partitions. The system can the discrete value sets with each other. The system can determine a final set of discrete values based at least in part on the comparison.

Abstract: Techniques are described herein are directed toward a method for discovery of discrete partitioning information. A system can receive data sets in table format from source system. The data can be stored in the source system to be partitioned and transmitted from the source system to a target system. The system can determine a respective partitioning column for each data set. The system can determine a number of partitions. The system can determine, for each data set, a respective set of discrete values from the plurality of discrete values of the respective partitioning column. The number of the discrete values of the set of discrete values can be based at least in part on the number of partitions. The system can the discrete value sets with each other. The system can determine a final set of discrete values based at least in part on the comparison.

Abstract: Techniques for providing intelligent data profiling services are disclosed. A distributed computing system can include a data profiling service and a data recommender service. During an interactive data session, a user can select a data column of a data entity and request data profiling information for the data column. The data profiling service can compute the requested profile. The data recommender service can then determine a priority rank for the data columns of the data entity. Based on the priority rank, the data profiling service can compute a second data profile for a second data column of the data entity. The second data profile can be stored in a cache for rapid retrieval when the user requests the second data profile.

Abstract: Techniques for instantiating a reference type to create a first descendant loader and a second descendant loader. The first descendant loader and the second descendant loader may be isolated loaders. The first descendant loader may load a first plugin and a first set of libraries. The first set of libraries may be needed to complete a first operation. The second descendant loader may load a second plugin and a second set of libraries. The second set of libraries may be needed to complete a second operation. The first set of libraries may conflict with the second set of libraries. A first method of the first plugin may be invoked through a plugin interface in order to complete the first operation. A second method of the second plugin may be invoked through the plugin interface to complete the second operation.

Abstract: Techniques are described for the discovery of source range partitioning information. An example method includes a device determining a partition boundary value for the data based at least in part on the following steps. The device can determine a first plurality of bounded value sets and a second plurality of bounded value sets. The device can calculate a first average value of a first value and a second average value. The device can determine a first deviation value of the first average value from the first value and a second deviation value of the second average value from a third value. The device can determine the first partition boundary value based at least in part on the first deviation value and the second deviation value, the first partition boundary value being the first candidate partition boundary value or the second candidate partition boundary value.

Abstract: The present embodiments relate to metadata value-based mapping during a data load in a data integration job. A computing device can receive a first data set from a source system and computer-readable instructions to load data into a target system. The device can receive a first metadata set from the target system that describe destinations. The computing device can identify a first data value of the first data set that matches a metadata value of the first metadata set. The device can receive a data integration mapping of the second data value of the first data set to a data field associated with the matching metadata value of the first metadata set. The device can load the second data value of the first data set from the source system into the target system pursuant to the mapping and the computer-readable instructions.

Abstract: The present embodiments relate to metadata value-based mapping during a data load in a data integration job. A computing device can receive a first data set from a source system and computer-readable instructions to load data into a target system. The device can receive a first metadata set from the target system that describe destinations. The computing device can identify a first data value of the first data set that matches a metadata value of the first metadata set. The device can receive a data integration mapping of the second data value of the first data set to a data field associated with the matching metadata value of the first metadata set. The device can load the second data value of the first data set from the source system into the target system pursuant to the mapping and the computer-readable instructions.

Abstract: Techniques for providing intelligent data profiling services are disclosed. A distributed computing system can include a data profiling service and a data recommender service. During an interactive data session, a user can select a data column of a data entity and request data profiling information for the data column. The data profiling service can compute the requested profile. The data recommender service can then determine a priority rank for the data columns of the data entity. Based on the priority rank, the data profiling service can compute a second data profile for a second data column of the data entity. The second data profile can be stored in a cache for rapid retrieval when the user requests the second data profile.