Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: In an example implementation consistent with the features disclosed herein, high availability of a stateful application is achieved by orchestrating multiple computing clusters. A stateful application is deployed at a first application operator and a second application operator of a first computing cluster and a second computing cluster, respectively. The first application operator includes a first control loop that changes the first computing cluster based on first resources of the first computing cluster. The second application operator includes a second control loop that changes the second computing cluster based on second resources of the second computing cluster. Synchronization of the first resources with the second resources is configured, such as via an object storage service. The first application operator and the second application operator are scaled to, respectively, nonzero and zero. A load balancer is configured to route requests to the first computing cluster.

Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: In an example implementation consistent with the features disclosed herein, high availability of a stateful application is achieved by orchestrating multiple computing clusters. A stateful application is deployed at a first application operator and a second application operator of a first computing cluster and a second computing cluster, respectively. The first application operator includes a first control loop that changes the first computing cluster based on first resources of the first computing cluster. The second application operator includes a second control loop that changes the second computing cluster based on second resources of the second computing cluster. Synchronization of the first resources with the second resources is configured, such as via an object storage service. The first application operator and the second application operator are scaled to, respectively, nonzero and zero. A load balancer is configured to route requests to the first computing cluster.

Abstract: In an example implementation consistent with the features disclosed herein, a tenant service in an internal network is made accessible to clients in an external network through a load balancer service. A request is transmitted by a tenant computing cluster in response to discovering creation of the tenant service at the tenant computing cluster. The request is received by a gateway computing cluster. The load balancer service is deployed at the gateway computing cluster in response to receiving the request. An external IP address for the external network is assigned to the load balancer service. The load balancer service is configured to forward client requests received at the external IP address to the tenant service. The external IP address is then sent to the tenant computing cluster.

Abstract: Examples described herein relate to an authentication management system and a method for managing authentication of a user between an enterprise-level authentication protocol and a cloud-native authentication protocol. The authentication management system may receive an authentication request from an application hosted on a private on-premise network to authenticate a user using the enterprise-level authentication protocol. The authentication request is generated by the application upon receiving an access request from the user via a software as-a-service (SaaS) cloud platform in a cloud-native authentication protocol. Further, the authentication management system may generate an authentication response compliant with the enterprise-level authentication protocol based on an authentication reply that is formatted in compliance to the cloud-native authentication protocol and received from a cloud-native identity and authentication management system based on the authentication request.

Abstract: Embodiments described herein are generally directed to integration of multiple services across multiple clouds within a unified IAM control plane. According to an example, an MSP provides (i) a user interface through which users of multiple tenants are able to configure permissions for and access multiple resources of a set of services associated with a hybrid cloud; and (i) a unified IAM control plane across the set of services, each of which potentially uses a different IAM protocol or scheme. A centralized IAM service is maintained by the MSP containing information regarding the permissions for the resources. Multiple service integrations for the set of services are supported by the MSP, including providing a first set of APIs that facilitate a direct integration with the unified IAM control plane in which the centralized IAM service maintains access control information for resources associated with a first service of the set of services.

Abstract: Examples described herein relate to policy management in target environments. A workload attestation request including a workload specification of a workload is received. A workload profile is determined based on the workload specification. A policy stored in a policy database is identified based on the workload profile. An attestation identifier indicating the workload profile is provided in response to the workload attestation request. On receiving a policy request including the attestation identifier from a controller node at a target environment, policies are compiled from the policy database using the attestation identifier, and provided to the controller node, which applies the policy in the target environment.

Abstract: Example implementations relate to data synchronization. According to an example, a system includes a processing system to execute a workload container, the workload container including a user workload application, the user workload application including a sync client; a data storage system, coupled to the processing system, to store user data; and a sync controller to control synchronization of the user data between the sync client and a sync server executing on a customer computing system and pull a selected portion of the user data from the customer computing system to the sync client. The sync client incrementally updates the user data stored on the data storage system with the selected portion of the user data to synchronize the user data between the customer computing system and the cloud computing system.

Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: In an example, a container cluster management system includes a first node, a second node and redistribution manager. The first node has an allocated external IP address, and comprises a utilization monitor to provide data relating to a utilization of the first node. The redistribution manager may receive the data relating to the utilization of the first node from the first node and determine whether the utilization of the first node has exceeded a predetermined threshold. Responsive to the utilization exceeding the predetermined threshold, the redistribution manager may reallocate the external IP address from the first node to the second node.

Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: Embodiments described herein are generally directed to integration of multiple services across multiple clouds within a unified IAM control plane. According to an example, an MSP provides (i) a user interface through which users of multiple tenants are able to configure permissions for and access multiple resources of a set of services associated with a hybrid cloud; and (i) a unified IAM control plane across the set of services, each of which potentially uses a different IAM protocol or scheme. A centralized IAM service is maintained by the MSP containing information regarding the permissions for the resources. Multiple service integrations for the set of services are supported by the MSP, including providing a first set of APIs that facilitate a direct integration with the unified IAM control plane in which the centralized IAM service maintains access control information for resources associated with a first service of the set of services.

Abstract: Embodiments described herein are generally directed to integration of multiple services across multiple clouds within a unified IAM control plane. According to an example, an MSP provides (i) a user interface through which users of multiple tenants are able to configure permissions for and access multiple resources of a set of services associated with a hybrid cloud; and (i) a unified IAM control plane across the set of services, each of which potentially uses a different IAM protocol or scheme. A centralized IAM service is maintained by the MSP containing information regarding the permissions for the resources. Multiple service integrations for the set of services are supported by the MSP, including providing a first set of APIs that facilitate a direct integration with the unified IAM control plane in which the centralized IAM service maintains access control information for resources associated with a first service of the set of services.

Abstract: Examples described herein relate to a system and method for deploying and managing services in tenant clusters using a management cluster. The management cluster includes an integration service in communication with a service controller. The integration service communicates with a service endpoint using privileged credentials for receiving tenant cluster-specific configuration information. The integration service generates access credentials for the new tenant cluster to communicate with the service provider. The integration service provides tenant cluster-specific configuration information and access credentials to the service controller for the deployment of the service in the new tenant cluster. The service controller deploys the service at the new tenant cluster and manages the lifecycle of the service at the new tenant cluster.

Abstract: Examples described herein relate to a system and method for deploying and managing services in tenant clusters using a management cluster. The management cluster includes an integration service in communication with a service controller. The integration service communicates with a service endpoint using privileged credentials for receiving tenant cluster-specific configuration information. The integration service generates access credentials for the new tenant cluster to communicate with the service provider. The integration service provides tenant cluster-specific configuration information and access credentials to the service controller for the deployment of the service in the new tenant cluster. The service controller deploys the service at the new tenant cluster and manages the lifecycle of the service at the new tenant cluster.

Abstract: Examples described herein relate to policy-based logging using workload profiles. A workload profile of a first workload is identified. Based on the workload profile, a logging policy, which includes a predefined log pattern and a predefined log depth, is compiled. Workload log messages of a predetermined log level that are associated with the first workload are stored in a cache memory. On detecting the predefined log pattern in the workload log messages stored in the cache memory, the workload log messages are logged to a log file based on the predefined log depth.

Abstract: Examples described herein relate to policy-based logging using workload profiles. A workload profile of a first workload is identified. Based on the workload profile, a logging policy, which includes a predefined log pattern and a predefined log depth, is compiled. Workload log messages of a predetermined log level that are associated with the first workload are stored in a cache memory. On detecting the predefined log pattern in the workload log messages stored in the cache memory, the workload log messages are logged to a log file based on the predefined log depth.