Abstract: A system implements a cloud-based digital platform allows developers to build new applications/services and then deploy to cloud platforms among continuous deployment, A/B test, blue/green deployment, and canary deployment. The system configures a service mesh on top of a cluster of computers. The system initializes a new service via templates that include common libraries, security scan pipeline, monitoring as code pipeline, and code coverage management for internal policy compliances, as well automated cloud resources request and provisioning. One or more proxy services, that extract data from the data sources using filters, can be executed. The system may use machine learning based models that are trained using the data extracted by the proxy service. The system allows automatic provisioning, computation orchestration, storage requests, and artificial intelligence insight feedback, as well as automated self-services to navigate complex systems and reduce on-boarding times of the platform.

Abstract: A system performs continuous delivery of a data pipeline on a cloud platform. The system receives a specification of the data pipeline that is split into smaller specifications of data pipeline units. The system identifies a target cloud platform and generates a deployment package for each data pipeline unit for the target cloud platform. The system creates a connection with the target cloud platform and uses the connection to provision computing infrastructure on the target cloud platform for the data pipeline unit according to the system configuration of the data pipeline unit. The data pipeline may be implemented as a data mesh that is a directed acyclic graph of nodes, each node representing a data pipeline unit. Different portions of the data mesh may be modified independent of each other. Partial results stored in different portions of the data mesh may be recomputed starting from different points in time.

Abstract: A system implements a cloud-based digital platform allows developers to build new applications/services and then deploy to cloud platforms among continuous deployment, A/B test, blue/green deployment, and canary deployment. The system configures a service mesh on top of a cluster of computers. The system initializes a new service via templates that include common libraries, security scan pipeline, monitoring as code pipeline, and code coverage management for internal policy compliances, as well automated cloud resources request and provisioning. One or more proxy services, that extract data from the data sources using filters, can be executed. The system may use machine learning based models that are trained using the data extracted by the proxy service. The system allows automatic provisioning, computation orchestration, storage requests, and artificial intelligence insight feedback, as well as automated self-services to navigate complex systems and reduce on-boarding times of the platform.

Abstract: A system performs delivery of a data pipeline on a cloud platform. The system receives a specification of the data pipeline that is split into smaller specifications of data pipeline units. The system identifies a target cloud platform and generates a deployment package for each data pipeline unit for the target cloud platform. The system creates a connection with the target cloud platform and uses the connection to provision computing infrastructure on the target cloud platform for the data pipeline unit according to the system configuration of the data pipeline unit. The data pipeline may be implemented as a data mesh that is a directed acyclic graph of nodes, each node representing a data pipeline unit. Different portions of the data mesh may be modified independent of each other. Partial results stored in different portions of the data mesh may be recomputed starting from different points in time.

Abstract: A system performs continuous delivery of a data pipeline on a cloud platform. The system receives a specification of the data pipeline that is split into smaller specifications of data pipeline units. The system identifies a target cloud platform and generates a deployment package for each data pipeline unit for the target cloud platform. The system creates a connection with the target cloud platform and uses the connection to provision computing infrastructure on the target cloud platform for the data pipeline unit according to the system configuration of the data pipeline unit. The data pipeline may be implemented as a data mesh that is a directed acyclic graph of nodes, each node representing a data pipeline unit. Different portions of the data mesh may be modified independent of each other. Partial results stored in different portions of the data mesh may be recomputed starting from different points in time.

Abstract: A system implements a cloud-based digital platform allows developers to build new applications/services and then deploy to cloud platforms among continuous deployment, A/B test, blue/green deployment, and canary deployment. The system configures a service mesh on top of a cluster of computers. The system initializes a new service via templates that include common libraries, security scan pipeline, monitoring as code pipeline, and code coverage management for internal policy compliances, as well automated cloud resources request and provisioning. One or more proxy services, that extract data from the data sources using filters, can be executed. The system may use machine learning based models that are trained using the data extracted by the proxy service. The system allows automatic provisioning, computation orchestration, storage requests, and artificial intelligence insight feedback, as well as automated self-services to navigate complex systems and reduce on-boarding times of the platform.

Abstract: An architecture for performing communications between a remote computer system and a host server over a “connection establishment” type network (i.e., dial-up) is described. The architecture comprises configuring the remote computer system with a Point-to-Point Protocol (PPP) stack. Similarly, an Address Resolution Protocol (ARP) service module and a Dynamic Host Configuration Protocol (DHCP) server are configured on the remote computer system. The PPP stack provides dial-up networking capabilities, while the DHCP server and the ARP service module provide “always connected” type network (i.e., LAN) messaging functionality. The PPP stack, the ARP service module, and the DHCP server are configured as a driver that is installed on the remote computer system. As such, the driver will enable a connection over the “connection establishment” type network that will appear to be an “always connected” type connection to the remote computer system.

Abstract: A system and method for defect analysis are disclosed wherein a defect data set is input into the system. A radius value is selected by a user, which is the maximum number of bits that bit failures can be separated from one another to be considered a bit cluster. When a defect data set is received, the system and method start with a fail bit and search for neighboring fail bits. The specified radius is used to qualify the found fail bits to be part of the bit cluster or not. If a minimum count of fail bits is not met, the system and method will stop searching- and move to the next fail bit. If a minimum count of fail bits is met, the search continues for the next fail bit until the maximum fail bit count specified by the user is reached. Aggregation is provided such that once bit clusters have been classified, the number of clusters that have the exact match or partial match to each other is counted. The user may set the partial match as a threshold count to establish a match.

Abstract: An architecture for performing communications between a remote computer system and a host server over a “connection establishment” type network (i.e., dial-up) is described. The architecture comprises configuring the remote computer system with a Point-to-Point Protocol (PPP) stack. Similarly, an Address Resolution Protocol (ARP) service module and a Dynamic Host Configuration Protocol (DHCP) server are configured on the remote computer system. The PPP stack provides dial-up networking capabilities, while the DHCP server and the ARP service module provide “always connected” type network (i.e., LAN) messaging functionality. The PPP stack, the ARP service module, and the DHCP server are configured as a driver that is installed on the remote computer system. As such, the driver will enable a connection over the “connection establishment” type network that will appear to be an “always connected” type connection to the remote computer system.

Abstract: An architecture for performing communications between a remote computer system and a host server over a “connection establishment” type network (i.e., dial-up) is described. The architecture comprises configuring the remote computer system with a Point-to-Point Protocol (PPP) stack. Similarly, an Address Resolution Protocol (ARP) service module and a Dynamic Host Configuration Protocol (DHCP) server are configured on the remote computer system. The PPP stack provides dial-up networking capabilities, while the DHCP server and the ARP service module provide “always connected” type network (i.e., LAN) messaging functionality. The PPP stack, the ARP service module, and the DHCP server are configured as a driver that is installed on the remote computer system. As such, the driver will enable a connection over the “connection establishment” type network that will appear to be an “always connected” type connection to the remote computer system.