Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for determination of recommendations and alerts in such environments. A data pipeline or process can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. When provided as part of a software-as-a-service (SaaS) or cloud environment, the data sourced from a plurality of organizations can be aggregated, to leverage information gleaned from the collective or shared data. The system can be used to generate semantic alerts, including obtaining permission from; and analyzing the collective data of; the plurality of organizations, to determine operational advantages indicated by the data, and providing alerts associated with those operational advantages.

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for automatic generation of asserts in such environments. A data pipeline or process, such as, for example an extract, transform, load (ETL) process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema. During the process of populating a data warehouse instance, the system can automatically generate dynamic data-driven ETL asserts, including determining a list of columns for tables in the data warehouse; determining a data type for each column; generating an assert for each determined data type; validating the generated assert; and maintaining the generated assert.

Abstract: In accordance with an embodiment, described herein are systems and methods for determining or allocating an amount, quantity, or number of compute instances or virtual machines for use with extract, transform, load (ETL) processes. In an example embodiment, a particular (e.g., optimal) number of virtual machines (VM's) can be determined by predicting ETL completion times for customers, using historical data. ETL processes can be simulated with an initial/particular number of virtual machines. If the predicted duration is greater than the desired duration, the number of virtual machines can be incremented, and the simulation repeated. Actual completion times from ETL processes can be fed back, to update a determined number of compute instances or virtual machines. In accordance with an embodiment, the system can be used, for example, to generate alerts associated with customer service level agreements (SLA's).

Abstract: A segmentation platform enables a system that comprises a behavior service and a predictive service for determining a segment from a dataset. The behavior service can analyze data to determine information about behavior that has already occurred. The predictive service can analyze data to determine information about the predicted propensity for certain behavior to occur in the future. In some cases, the predictive service can determine the information by utilizing a training model that indicates predictions related to potential relationships among properties of a dataset. The segmentation platform also enables an interactive user interface that can be utilized to configure attributes of the segment, analyze information associated with the segment, and deliver the information to another device.

Abstract: A database system populates various fields with information from a plurality of data sources which is matched to geographic and segment information for different reference identifiers. A series of data unions provide a selectable result set. A selection of elements from the selectable result set are used to generate qualifiers that are monitored in real time to determine when the qualifiers have been satisfied in order to set a semaphore allowing access to the selection of elements.

Abstract: In accordance with various embodiments, described herein are systems and methods for use with an analytic applications environment, for ranking of database tables for use in controlling extract, transform, load (ETL) processes. In accordance with an embodiment, the system uses a ranking algorithm or process to rank database tables and/or table columns associated with a set of data. The table/column rankings can then be used to prioritize ETL processing of a customer's data for use with a data warehouse or other data analytics environment. In accordance with an embodiment, the method includes determining a global rank; a business rank; and a tenant or customer-specific rank, for a plurality of tables and columns in a customer's database; and aggregating or otherwise using the determined rankings to control the ETL process for a particular customer (tenant), to load their data into the data warehouse.

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for determination of recommendations and alerts in such environments. A data pipeline or process can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. When provided as part of a software-as-a-service (SaaS) or cloud environment, the data sourced from a plurality of organizations can be aggregated, to leverage information gleaned from the collective or shared data. The system can be used to generate semantic alerts, including obtaining permission from; and analyzing the collective data of; the plurality of organizations, to determine operational advantages indicated by the data, and providing alerts associated with those operational advantages.

Abstract: In accordance with an embodiment, described herein are systems and methods for determining or allocating an amount, quantity, or number of compute instances or virtual machines for use with extract, transform, load (ETL) processes. In an example embodiment, a particular (e.g., optimal) number of virtual machines (VM's) can be determined by predicting ETL completion times for customers, using historical data. ETL processes can be simulated with an initial/particular number of virtual machines. If the predicted duration is greater than the desired duration, the number of virtual machines can be incremented, and the simulation repeated. Actual completion times from ETL processes can be fed back, to update a determined number of compute instances or virtual machines. In accordance with an embodiment, the system can be used, for example, to generate alerts associated with customer service level agreements (SLA's).

Abstract: In accordance with an embodiment, described herein are systems and methods for use with an analytic applications environment, for automatic generation of asserts in such environments. A data pipeline or process, such as, for example an extract, transform, load (ETL) process, can operate in accordance with an analytic applications schema adapted to address particular analytics use cases or best practices, to receive data from a customer's (tenant's) enterprise software application or data environment, for loading into a data warehouse instance. Each customer (tenant) can additionally be associated with a customer tenancy and a customer schema. During the process of populating a data warehouse instance, the system can automatically generate dynamic data-driven ETL asserts, including determining a list of columns for tables in the data warehouse; determining a data type for each column; generating an assert for each determined data type; validating the generated assert; and maintaining the generated assert.

Abstract: In accordance with an embodiment, described herein are systems and methods for determining or allocating an amount, quantity, or number of compute instances or virtual machines for use with extract, transform, load (ETL) processes. In an example embodiment, a particular (e.g., optimal) number of virtual machines (VM's) can be determined by predicting ETL completion times for customers, using historical data. ETL processes can be simulated with an initial/particular number of virtual machines. If the predicted duration is greater than the desired duration, the number of virtual machines can be incremented, and the simulation repeated. Actual completion times from ETL processes can be fed back, to update a determined number of compute instances or virtual machines. In accordance with an embodiment, the system can be used, for example, to generate alerts associated with customer service level agreements (SLA's).

Abstract: A database system populates various fields with information from a plurality of data sources which is matched to geographic and segment information for different reference identifiers. A series of data unions provide a selectable result set. A selection of elements from the selectable result set are used to generate qualifiers that are monitored in real time to determine when the qualifiers have been satisfied in order to set a semaphore allowing access to the selection of elements.

Abstract: A database system populates various fields with information from a plurality of data sources which is matched to geographic and segment information for different reference identifiers. A series of data unions provide a selectable result set. A selection of elements from the selectable result set are used to generate qualifiers that are monitored in real time to determine when the qualifiers have been satisfied in order to set a semaphore allowing access to the selection of elements.

Abstract: A segmentation platform enables a system that comprises a behavior service and a predictive service for determining a segment from a dataset. The behavior service can analyze data to determine information about behavior that has already occurred. The predictive service can analyze data to determine information about the predicted propensity for certain behavior to occur in the future. In some cases, the predictive service can determine the information by utilizing a training model that indicates predictions related to potential relationships among properties of a dataset. The segmentation platform also enables an interactive user interface that can be utilized to configure attributes of the segment, analyze information associated with the segment, and deliver the information to another device.

Abstract: A database system populates various fields with information from a plurality of data sources which is matched to geographic and segment information for different reference identifiers. A series of data unions provide a selectable result set. A selection of elements from the selectable result set are used to generate qualifiers that are monitored in real time to determine when the qualifiers have been satisfied in order to set a semaphore allowing access to the selection of elements.