Abstract: Integration of structural information into an asset management catalog (e.g., using a computerized tool), is enabled. For example, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on schema data applicable to data stored in a data storage asset, determining structure data representative of a structure of the data stored in the data storage asset, in response to determining the structure data, generating a data entry, applicable to the structure data, to be added to an asset management catalog associated with the data storage asset, and in response to generating the data entry, associating the data entry with the data stored in the data storage asset.

Abstract: Integration of semantic information into an asset management catalog (e.g., using a computerized tool), is enabled. For example, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: based on structure data representative of a structure of data stored in a data storage asset, determining semantic metadata applicable to the data stored in the data storage asset, in response to determining the semantic metadata, generating a data entry, applicable to the semantic metadata, to be added to an asset management catalog associated with the data storage asset, and in response to generating the data entry, associating the data entry with the data stored in the data storage asset.

Abstract: One example method includes injecting processing into sessions including IO sessions. Events in a file system are intercepted and processed. During processing, policies may be applied to the events. Some of the policies are triggered such that external actions or processing is applied to the event. Once the actions have been performed, the event may be processed by the file system.

Abstract: Detecting a decrease or loss in performance of a prediction engine configured to predict a compressor for compressing data. The prediction engine suffers a loss in performance when the compressor inferred by the prediction engine does not match or does not sufficiently match a compressor inferred by a compressor selector. Using compressors inferred by two different models allows the loss in performance to be detected. The loss in performance may constitute a violation of a service level agreement (SLA) or service level objective (SLO). Then the loss in performance is determined.

Abstract: One example method includes receiving a data stream, collecting a sequence of one or more batches of data from the data stream, analyzing the batches of data in the sequence, obtaining compressor choices for the batches of data in the sequence, obtaining a new batch of data from the data stream, analyzing the new batch of data, based on the analyzing and the compressor choices for the batches of data in the sequence, and the analyzing of the new batch of data, generating a prediction that identifies recommended data compressor for the new batch of data, and in response to a change in the data stream, compressing the new batch of data using the recommended data compressor.

Abstract: Shared function execution mitigates cold start penalties for execution units. When a serverless platform receives a request, the request is performed when the serverless platform has a warm execution unit for the request. If a warm execution unit is not available or running, the serverless platform may send the request to another serverless platform rather than cold start an execution unit. The cold start is performed when the warm execution unit is not available at other platforms.

Abstract: A method includes identifying, by a server module running at a stream data platform, a set of data to be moved to a target data storage tier of the stream data platform, querying, by the server module, service level agreement (SLA) objectives of the target data storage tier, fetching, by the server module, the set of data, selecting, by the server module, a compressor based on the SLA objectives, compressing the set of data using the selected compressor, and storing the set of data in the target data storage tier.

Abstract: One method includes receiving a user-defined function, generating a binary based on the user-defined function, copying the binary to a target host where the user-defined function will run, in response to a lookup request, received from an API gateway based on a function invocation by a user, looking up the target host and a route, and returning, to the API gateway, a target host ID and the route, so as to enable the API gateway to request the target host to respond to the function invocation.

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: One example method includes receiving a data event notification, retrieving data to which the data event notification pertains, analyzing the data, based on the analyzing, generating metadata pertaining to the data, and transmitting the metadata to a repository. These operations may be performed automatically in response to receipt of the data event notification. The data event notification may likewise be generated automatically in response to implementation of the data event, which may be any of a data create, read, update, or delete, operation.

Abstract: One example method includes data orchestration. Data sets are available to users in an online marketplace. Based on requests for data sets, other data sets may be recommended. The recommended data sets are moved or copied to recommended locations. This ensures that the data sets are available to the user more quickly if subsequently requested.

Abstract: One example method includes receiving, by an administrator from a user computer system, a query identifying content the user wants to access, and the administrator and user computer system are elements of a system that includes a group of networked user computer systems, parsing, by the administrator, the query to identify metadata included in the query, identifying, by the administrator, any content of a datastore that matches the metadata, packaging, by the administrator, the datastore content into a virtual filesystem, and exposing, by the administrator, the virtual filesystem to one of the user computer systems.

Abstract: One example method includes declaring a management requirement for a collection of data, defining metadata that embodies the requirement, associating the management requirement with the collection of data, and performing a filesystem operation, with respect to the collection of data, based on the metadata. The declaring and defining operations may be performed by an administrator operating in a centralized datastore that includes a metadata repository, and the defined metadata may be stored in the metadata repository.

Abstract: One example method includes, at a computer system that is an element of a network, intercepting stream elements of a stream associated with the computer system, selecting one or more of the stream elements, analyzing the selected stream elements, and based on the analyzing, emitting the selected stream elements to an external, query-able, storage entity that is external to the computer system. The metadata emitted to the storage entity may further include metadata that was natively generated at the computer system.

Abstract: One example method includes extracting content metadata from data, storing the content metadata in a data catalogue, receiving at the data catalogue, from a user, a request to access the data, transmitting, by the data catalogue to a security service provider, an access request that includes the extracted content metadata and metadata relating to the access request, accessing, by the security service provider, identity metadata concerning an identity of the user, and a data access policy, and transmitting, by the security service provider to the data catalogue, a decision as to whether or not access can be granted to the data, and the decision is based on the data access policy, the identity metadata, and the metadata in the access request.

Abstract: One example method includes selecting items to be repatriated from a cloud site to an on-premises site, and the items include a workload and a data set accessed by the workload, transmitting a repatriation request from the on-premises site to the cloud site, and the repatriation request identifies the selected items, receiving, by the on-premises site from the cloud site, a compressed data set which includes the data set in compressed form, receiving, by the on-premises site from the cloud site, a compressed workload which includes the workload in compressed form, and the compressed workload and the compressed data set have been compressed with a compression algorithm automatically selected based on content, and/or context, of data in the data set, decompressing, at the on-premises site, the compressed data set and the compressed workload, and deploying the decompressed data set and the decompressed workload locally at the on-premises site.

Abstract: One example method includes receiving stream data batches at a client node. The client node determines if the stream data batches are compressible. For the compressible stream data batches, a request is sent to a server node for an on-demand stream compression service, the request including an indicator of a stream data type for the compressible data batches. The on-demand stream compression service is deployed and launched at the client node. The on-demand stream compression service includes a compressor pool of compressors that are able to compress the stream data type of the compressible stream data batches. A compressor of the compressor pool is selected and used to compress the compressible stream data batches. The compressed stream data batches are sent to the server node.

Abstract: One example method includes receiving, by a graph proxy, an event from an event generator, and the event includes information about an IO and information about data affected by the IO, comparing, by the graph proxy, the data to a schema, when the data is determined by the graph proxy to map to the schema, identifying, by the graph proxy, a rule that is associated with the event, and the rule specifies performance of an action when a condition is met, and when the condition is met, performing, by the graph proxy, the action, and the action is performed with respect to a graph.

Abstract: A compression system is disclosed. A compression service removes compression responsibilities from an application. The compression system can deploy virtual network engines to locations near the applications. The virtual network engines compress the data using a compressor selected from multiple compressors. The compressed data can then be transmitted, decompressed, and delivered to the destination.

Abstract: One example method includes file specific compression selection. Compression metrics are generated for a chunk of a file. Using a set of training data, the compression metrics are corrected using a correction factor to determine estimated file compression metrics. A compressor is then selected to compress the file based on at least the estimated file compression metrics.