Abstract: Provided is an architecture for creation and management of data pipelines. A user action designer tool renders a user interface on a user device and records user actions performed on the user interface. The user actions include selection of pipeline stages to be included in a data pipeline and selection of a technology for each pipeline stage. A pipeline definition for the data pipeline is generated based on the user actions. A user action script compiler generates a user action script based on the pipeline definition. An orchestrator communicates sections of the user action script to gentech script generators. Each gentech script generator generates a set of gentech scripts. Each set of gentech scripts is communicated to an interpreter for the technology selected for a corresponding pipeline stage. Each pipeline stage is executed by a corresponding technology based on technology-specific scripts generated by a corresponding technology-specific interpreter.

Abstract: Provided is a method for facilitating for facilitating monitoring of development of software products. The actions/activities executed during the stages of a product development for a software product are tracked. Further, each activity log associated with a corresponding activity is tagged to indicate an association of a corresponding activity log with one of the stages. The activities are further classified into one of a value-added activity or a non-value added activity, based on each activity log. The development of the software product is thus monitored by way of a value stream map that is generated based on the classification of each activity. The value stream map is indicative of an efficiency score for each of the stages.

Abstract: A system for facilitating analysis of a software product is provided. The system includes processing circuitry that collects data logs from various technologies associated with at least one stage of a software development life cycle (SDLC) of the software product. The processing circuitry identifies entities associated with each collected data log, and standardizes the collected data logs such that each collected data log is standardized based on standard data formats associated with the identified entities. Each entity corresponds to at least one stage of the SDLC of the software product. Further, the processing circuitry updates data models associated with the entities based on the standardized data logs and generates a unified data model that is indicative of a correlation between the entities. Based on the correlation indicated by the unified data model, the processing circuitry executes an automated action.

Abstract: Provided is an architecture for facilitating deployment of a software product. A user action designer records user actions performed on a user interface for deployment of first and second microservices. User actions include input of first and second sets of configuration details for the deployment of the first and second microservices, respectively. A first set of configuration details is indicative of a first machine configuration, a first deployment mode, and a first cloud technology. A second set of configuration details is indicative of the first machine configuration, the first deployment mode, and a second cloud technology. An orchestrator facilitates provisioning of first and second cloud instances at the first and second cloud technologies for the deployment of the first and second microservices based on the first and second sets of configuration details, respectively.

Abstract: Provided is a method for facilitating for facilitating monitoring of development of software products. The actions/activities executed during the stages of a product development for a software product are tracked. Further, each activity log associated with a corresponding activity is tagged to indicate an association of a corresponding activity log with one of the stages. The activities are further classified into one of a value-added activity or a non-value added activity, based on each activity log. The development of the software product is thus monitored by way of a value stream map that is generated based on the classification of each activity. The value stream map is indicative of an efficiency score for each of the stages.

Abstract: Provided is an architecture to facilitate definition, design, development, or deployment of an application. A user action designer renders a user interface (UI) on a user device and records user actions performed on the UI. The user actions include selection of an operation and a technology for executing the operation. The user action designer stores metadata associated with the user actions. A user action script compiler generates, based on the metadata, a user action script. An orchestrator communicates the user action script to a gentech script generator associated with the technology. The gentech script generator converts the user action script to a gentech script indicative of the operation. The interpreter converts the gentech script to a technology-specific script for the technology and communicates the technology-specific script to the technology. The technology executes the operation based on the technology-specific script.

Abstract: The disclosed computer-implemented method for detecting security anomalies in a public cloud environment using network activity monitoring, application profiling, and self-building host mapping may include (1) collecting host information that identifies (A) at least one communication channel that has previously facilitated communication between at least one host computing platform within a cloud computing environment and at least one additional computing platform and/or (B) at least one application that has previously run on the host computing platform, (2) monitoring network traffic involving the host computing platform, (3) detecting, while monitoring the network traffic, network activity that is inconsistent with the collected host information, and then (4) determining that the detected network activity represents a potential security threat within the cloud computing environment due at least in part to the detected network activity being inconsistent with the collected host information.

Abstract: The present disclosure relates to protecting temporary virtual machine instances in a cloud computing platform from security risks. An example method generally includes monitoring a cloud platform for the assignment of a temporary virtual machine instance to a workload. A security system obtains information about a configuration of the temporary virtual machine instance and applications deployed on the temporary virtual machine instance. Based on the configuration of the temporary virtual machine instance and applications deployed on the temporary virtual machine instance, the security system generates a security policy to apply to the temporary virtual machine instance.

Abstract: A method for prioritizing vulnerabilities of an asset in a virtual computing environment is provided. The method includes determining a vulnerability score for the asset, based on at least one of a base vulnerability score or a temporal vulnerability score and receiving information about a threat. The method includes correlating the information about the threat with information about the open vulnerabilities on the asset and also about the asset to determine a threat score for the asset and determining a contextual score for the asset based on at least one tag of the asset. The method includes deriving a prioritization score for the asset, the prioritization score a combination of the vulnerability score, the threat score and the contextual score, wherein at least one method action is performed by a processor.

Abstract: The disclosed computer-implemented method for detecting security anomalies in a public cloud environment using network activity monitoring, application profiling, and self-building host mapping may include (1) collecting host information that identifies (A) at least one communication channel that has previously facilitated communication between at least one host computing platform within a cloud computing environment and at least one additional computing platform and/or (B) at least one application that has previously run on the host computing platform, (2) monitoring network traffic involving the host computing platform, (3) detecting, while monitoring the network traffic, network activity that is inconsistent with the collected host information, and then (4) determining that the detected network activity represents a potential security threat within the cloud computing environment due at least in part to the detected network activity being inconsistent with the collected host information.

Abstract: The disclosed method may include (1) identifying a data center application whose functionality is provided by a set of systems, (2) organizing, automatically by the computing device, the set of systems into one or more application model groups by, for each system in the set of systems, identifying an attribute of the system that is indicative of a security context under which the system should operate and assigning the system to an application model group for which the security context will be provided, and (3) for each application model group in the one or more application model groups, protecting the application model group by selecting a firewall configuration that will provide the security context for the application model group and by using the selected firewall configuration to protect the application model group. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed method may include (1) identifying a data center application whose functionality is provided by a set of systems, (2) organizing, automatically by the computing device, the set of systems into one or more application model groups by, for each system in the set of systems, identifying an attribute of the system that is indicative of a security context under which the system should operate and assigning the system to an application model group for which the security context will be provided, and (3) for each application model group in the one or more application model groups, protecting the application model group by selecting a firewall configuration that will provide the security context for the application model group and by using the selected firewall configuration to protect the application model group. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for performing customized large-scale data analytics may include (1) providing a logical-data-model user interface to enable modifying a logical data model of a relational multi-dimensional analytic database, (2) receiving, via the logical-data-model user interface, user input to modify the logical data model of the relational multi-dimensional analytic database, (3) modifying the logical data model of the relational multi-dimensional analytic database based on the user input, (4) providing a visualization user interface, based on the logical data model, to enable performing online analytical processing operations, and (5) receiving, via the visualization user interface, a request to perform an online analytical processing operation that provides a view of data stored within the relational multi-dimensional analytic database in accordance with the logical data model. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for performing customized large-scale data analytics may include (1) providing a logical-data-model user interface to enable modifying a logical data model of a relational multi-dimensional analytic database, (2) receiving, via the logical-data-model user interface, user input to modify the logical data model of the relational multi-dimensional analytic database, (3) modifying the logical data model of the relational multi-dimensional analytic database based on the user input, (4) providing a visualization user interface, based on the logical data model, to enable performing online analytical processing operations, and (5) receiving, via the visualization user interface, a request to perform an online analytical processing operation that provides a view of data stored within the relational multi-dimensional analytic database in accordance with the logical data model. Various other methods, systems, and computer-readable media are also disclosed.