Abstract: Systems and methods utilize a data change repository that supplements existing data in a database by storing various data labels for each record. Each data label comprises a first data characteristic comprising a stateless value and a second data characteristic that comprises a modification characteristic of the stateless value. The systems and methods may then use these additional labels to identify and/or validate the underlying data. As the labels are used for the identification and/or validation (as opposed to the underlying data itself), the systems and methods do not affect, and function despite, security protocols, access restrictions, and/or viewing rights.

Abstract: In some implementations, a monitoring device may receive configuration information associated with an extract, transform, load (ETL) pipeline that includes one or more data sources and one or more data sinks. The monitoring device may generate, based on the configuration information, lineage data related to a data flow from the one or more data sources to the one or more data sinks in the ETL pipeline. The monitoring device may generate one or more predicted quality metrics associated with the ETL pipeline using a machine learning model. The monitoring device may generate a visualization in which multiple nodes are arranged to indicate the data flow from the one or more data sources to the one or more data sinks and further in which the one or more predicted quality metrics are encoded within the visualization.

Abstract: Systems and methods utilize a data change repository that supplements existing data in a database by storing various data labels for each record. Each data label comprises a first data characteristic comprising a stateless value and a second data characteristic that comprises a modification characteristic of the stateless value. The systems and methods may then use these additional labels to identify and/or validate the underlying data. As the labels are used for the identification and/or validation (as opposed to the underlying data itself), the systems and methods do not affect, and function despite, security protocols, access restrictions, and/or viewing rights.

Abstract: Systems and methods utilize a data change repository that supplements existing data in a database by storing various data labels for each record. Each data label comprises a first data characteristic comprising a stateless value and a second data characteristic that comprises a modification characteristic of the stateless value. The systems and methods may then use these additional labels to identify and/or validate the underlying data. As the labels are used for the identification and/or validation (as opposed to the underlying data itself), the systems and methods do not affect, and function despite, security protocols, access restrictions, and/or viewing rights.

Abstract: In some implementations, a monitoring device may receive configuration information associated with an extract, transform, load (ETL) pipeline that includes one or more data sources and one or more data sinks. The monitoring device may generate, based on the configuration information, lineage data related to a data flow from the one or more data sources to the one or more data sinks in the ETL pipeline. The monitoring device may generate one or more predicted quality metrics associated with the ETL pipeline using a machine learning model. The monitoring device may generate a visualization in which multiple nodes are arranged to indicate the data flow from the one or more data sources to the one or more data sinks and further in which the one or more predicted quality metrics are encoded within the visualization.

Abstract: In some implementations, a monitoring device may receive configuration information associated with an extract, transform, load (ETL) pipeline that includes one or more data sources and one or more data sinks. The monitoring device may generate, based on the configuration information, lineage data related to a data flow from the one or more data sources to the one or more data sinks in the ETL pipeline. The monitoring device may generate one or more predicted quality metrics associated with the ETL pipeline using a machine learning model. The monitoring device may generate a visualization in which multiple nodes are arranged to indicate the data flow from the one or more data sources to the one or more data sinks and further in which the one or more predicted quality metrics are encoded within the visualization.

Abstract: In some implementations, a monitoring device may receive configuration information associated with an extract, transform, load (ETL) pipeline that includes one or more data sources and one or more data sinks. The monitoring device may generate, based on the configuration information, lineage data related to a data flow from the one or more data sources to the one or more data sinks in the ETL pipeline. The monitoring device may generate one or more predicted quality metrics associated with the ETL pipeline using a machine learning model. The monitoring device may generate a visualization in which multiple nodes are arranged to indicate the data flow from the one or more data sources to the one or more data sinks and further in which the one or more predicted quality metrics are encoded within the visualization.