Abstract: Executing functions in serverless platforms leveraging metadata is disclosed. Embodiments of the invention related to using metadata when placing and/or executing functions. The metadata includes request metadata, container or function metadata, node metadata, and/or data metadata. The metadata allows a function to be placed and executed in a manner that accounts for user intent, container requirements, node resources, and data location. Functions can be placed across different serverless platforms. Once the metadata is evaluated and a location is selected, a function is placed and executed at the selected location.

Abstract: Generating policy-based confidence scores for data is disclosed. Data captured by a data confidence fabric is annotated when the data is created, mutated, transited or otherwise handled in the data confidence fabric. The annotations are weighted by a policy to generate policy-based confidence scores. The policy-based confidence scores are used in determining whether the data is sufficiently trusted for use by an application.

Abstract: One example method includes annotating data with confidence information as the data transits a data confidence fabric, after the data has transited a portion of the data confidence fabric, using the confidence information to generate a data confidence score, assigning the data confidence score to the data, and adjusting the data confidence score. In some cases, the data confidence score may be automatically adjusted downward over time and/or may be adjusted based on input concerning the data.

Abstract: One example method is performed in connection with a data confidence fabric. This method includes generating information about data as the data transits a data confidence fabric, ingesting the information, mapping the ingested information to a timeline, evaluating the timeline, based on the evaluating, generating a recommendation for an action concerning the data, and implementing the action in the data confidence fabric. In this method, the evaluating includes determining if any unexpected time gaps occurred as the data transited the data confidence fabric.

Abstract: A data confidence fabric for generating data confidence scores for a build pipeline is disclosed. Confidence scores are generated for data or jobs in a build pipeline. The scores may be combined into a final confidence score that reflects a confidence in the artifact generated by the pipeline and in the pipeline. A user or infrastructure may or may not perform the artifact based on the associated confidence score.

Abstract: One example method includes receiving data at a node of a data confidence fabric, annotating, at the node, the data with an annotation that includes data confidence information, receiving a ledger stream at a ledger, and the ledger stream includes the annotation, and a representation of the data, creating, in a data structure associated with the ledger, a view node that corresponds to the data, creating, in the data structure, a representation of the annotation, and connecting, in the data structure, the representation of the annotation to the view node with an annotation edge.

Abstract: A method for managing data includes obtaining, by a local data manager, an actuation command request, performing a metadata analysis on confidence metadata associated with the actuation command request, making a determination that the actuation command request is valid, and in response to the determination, sending an actuation command to an actuation device based on the actuation command request.

Abstract: A data confidence fabric (DCF) is disclosed. The DCF may include a static configuration layer, a dynamic trust insertion layer, and a programmable scoring layer. The DCF may also include edge devices and applications that use the ingested data in some instances. The operation of the DCF allows data to be ingested and associated with a confidence or trustworthiness score. The confidence score can be used by applications that desire access to and use of the ingested data.

Abstract: A data confidence fabric (DCF) is disclosed. The DCF may include a static configuration layer, a dynamic trust insertion layer, and a programmable scoring layer. The DCF may also include edge devices and applications that use the ingested data in some instances. The operation of the DCF allows data to be ingested and associated with a confidence or trustworthiness score. The confidence score can be used by applications that desire access to and use of the ingested data.

Abstract: A method for managing data includes obtaining, by a local data manager, an actuation command request, performing a metadata analysis on confidence metadata associated with the actuation command request, making a determination that the actuation command request is valid, and in response to the determination, sending an actuation command to an actuation device based on the actuation command request.

Abstract: A method for managing data in accordance with one or more embodiments of the invention includes obtaining, by a data management system, a data confidence fabric (DCF) configuration request from a client, and in response to the DCF configuration request: providing a plurality of DCF configuration options to the client, obtaining acceptance of a portion of the plurality of DCF configuration options, generating a DCF configuration file based on the portion of the plurality of DCF configuration options, wherein the configuration file specifies a DCF pipeline, and deploying the DCF configuration file to a local data manager.