Abstract: Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.

Abstract: Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.

Abstract: Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.

Abstract: Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.

Abstract: Techniques related to a domain-specific language for transformations are disclosed. A server computer may process a table definition composed in a domain-specific language. The table definition may include a sequence of one or more transformations to be performed on one or more source tables to generate a target table. The sequence may include a customized transformation. A source dataset may be provided as input to an implementation of the customized transformation. An output dataset may be generated as a result of executing the implementation. An intermediate table may be generated based on performing at least one transformation on a particular source table. A supplemental portion for the intermediate table may be generated based on performing the at least one transformation on an appended portion of the particular source table. The target table may be generated based on combining the supplemental portion with the intermediate table.

Abstract: Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.

Abstract: Various embodiments disclosed herein relate to collection and aggregation of metrics for software services operating across a plurality of computing nodes.

Abstract: Various embodiments disclosed herein relate to collection and aggregation of metrics for software services operating across a plurality of computing nodes.

Abstract: Example embodiments involve a metrics collection system for collecting software usage metrics from one or more client devices at deployments. A computer, such as a server configured to execute the metrics collection system, collects software usage metrics (e.g., as a metrics submission from a client device) of the software product at the deployment, identifies a metrics type of the software usage metrics collected, assigns the software usage metrics to a metrics category, and calculates and updates a metrics score of the metrics category, based on the software usage metrics collected.

Abstract: Various embodiments disclosed herein relate to collection and aggregation of metrics for software services operating across a plurality of computing nodes.

Abstract: Techniques related to a domain-specific language for transformations are disclosed. A server computer may process a table definition composed in a domain-specific language. The table definition may include a sequence of one or more transformations to be performed on one or more source tables to generate a target table. The sequence may include a customized transformation. A source dataset may be provided as input to an implementation of the customized transformation. An output dataset may be generated as a result of executing the implementation. An intermediate table may be generated based on performing at least one transformation on a particular source table. A supplemental portion for the intermediate table may be generated based on performing the at least one transformation on an appended portion of the particular source table. The target table may be generated based on combining the supplemental portion with the intermediate table.

Abstract: Techniques related to a domain-specific language for dataset transformations are disclosed. A server computer may process a table definition composed in a domain-specific language. The table definition may include a sequence of one or more dataset transformations to be performed on one or more source tables to generate a target table. The sequence may include a customized transformation. A source dataset may be provided as input to an implementation of the customized transformation. An output dataset may be generated as a result of executing the implementation. An intermediate table may be generated based on performing at least one dataset transformation on a particular source table. A supplemental portion for the intermediate table may be generated based on performing the at least one dataset transformation on an appended portion of the particular source table. The target table may be generated based on combining the supplemental portion with the intermediate table.

Abstract: Techniques related to a domain-specific language for dataset transformations are disclosed. A server computer may process a table definition composed in a domain-specific language. The table definition may include a sequence of one or more dataset transformations to be performed on one or more source tables to generate a target table. The sequence may include a customized transformation. A source dataset may be provided as input to an implementation of the customized transformation. An output dataset may be generated as a result of executing the implementation. An intermediate table may be generated based on performing at least one dataset transformation on a particular source table. A supplemental portion for the intermediate table may be generated based on performing the at least one dataset transformation on an appended portion of the particular source table. The target table may be generated based on combining the supplemental portion with the intermediate table.

Abstract: Techniques related to a domain-specific language for dataset transformations are disclosed. A server computer may process a table definition composed in a domain-specific language. The table definition may include a sequence of one or more dataset transformations to be performed on one or more source tables to generate a target table. The sequence may include a customized transformation. A source dataset may be provided as input to an implementation of the customized transformation. An output dataset may be generated as a result of executing the implementation. An intermediate table may be generated based on performing at least one dataset transformation on a particular source table. A supplemental portion for the intermediate table may be generated based on performing the at least one dataset transformation on an appended portion of the particular source table. The target table may be generated based on combining the supplemental portion with the intermediate table.

Abstract: Techniques related to a domain-specific language for dataset transformations are disclosed. A server computer may process a table definition composed in a domain-specific language. The table definition may include a sequence of one or more dataset transformations to be performed on one or more source tables to generate a target table. The sequence may include a customized transformation. A source dataset may be provided as input to an implementation of the customized transformation. An output dataset may be generated as a result of executing the implementation. An intermediate table may be generated based on performing at least one dataset transformation on a particular source table. A supplemental portion for the intermediate table may be generated based on performing the at least one dataset transformation on an appended portion of the particular source table. The target table may be generated based on combining the supplemental portion with the intermediate table.