Abstract: A Services Delivery Platform (SDP) architecture is provided that is configured to onboard new data sets into an SDP data lake. The SDP enables the crowdsourcing of data on-boarding by configuring this process into an interactive, intuitive, step-by-step guided workflow while governing/controlling key functions like verification, acceptance and execution.

Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.

Abstract: A Services Delivery Platform (SDP) architecture is provided that is configured to onboard new data sets into an SDP data lake. The SDP enables the crowdsourcing of data on-boarding by configuring this process into an interactive, intuitive, step-by-step guided workflow while governing/controlling key functions like verification, acceptance and execution.

Abstract: Systems, methods, and computer-readable media for identifying an optimal cluster configuration for performing a job in a remote cluster computing system. In some examples, one or more applications and a sample of a production load as part of a job for a remote cluster computing system is received. Different clusters of nodes are instantiated in the remote cluster computing system to form different cluster configurations. Multi-Linear regression models segmented into different load regions are trained by running at least a portion of the sample on the instantiated different clusters of nodes. Expected completion times of the production load across varying cluster configurations are identified using the multi-linear regression models. An optimal cluster configuration of the varying cluster configurations is determined for the job based on the identified expected completion times.