Abstract: Sharing data in a data exchange across multiple cloud computing platforms and/or cloud computing platform regions is described. An example computer-implemented method can include creating a listing in a data exchange, the listing including a data set hosted by a first cloud computing entity. The data set can be shared with a second cloud computing entity. The method further includes receiving a request associated with a customer account of the second cloud computing entity to access the data set of the listing hosted by the first cloud computing entity and replicating at least a subset of the data set of the listing from the first cloud computing entity to a provider account at the second cloud computing entity to be accessible by the customer account at the second cloud computing entity.

Abstract: A method for identity resolution and data enrichment is performed by at least one hardware processor and includes detecting at an account of a data provider, a shared data object that is shared by an account of a data consumer with the account of the data provider. An application executing at the account of the data consumer is enabled for an identity resolution process based on the detecting of the shared data object. A request for source data received from the application is detected at the account of the data provider. The source data is managed by the account of the data provider. The source data is communicated to the application executing at the account of the data consumer, based on a verification that the application is enabled for the identity resolution process. The identity resolution process is performed at the account of the data consumer using the source data.

Abstract: Embodiments of the present disclosure may provide a data clean room allowing encryption based data analysis across multiple accounts of different database users. The data clean room may also restrict which data may be used in the analysis and may restrict the output. A requesting user's data can be encrypted using a key and a provider user can generate a shareable database function that accepts the key to decrypt the data to generate the results data without exposing each others' data.

Abstract: Disclosed herein are systems and methods for query processing with restrictions in a database clean room. In an embodiment, a system receives a query directed to a combination of a first source dataset from a first database account of a distributed database and a second source dataset from a second database account of the distributed database. The system generates an approved statements table that contains database statement language that can be executed against the combination of the first and second source datasets. Based on determining that the approved statements table includes the query, the system executes the query to produce results data, and stores the results data in the first database account.

Abstract: Provided herein are systems and methods for identity resolution and data enrichment. An example method performed by at least one hardware processor includes detecting at an account of a data provider, an update to personally identifiable information (PII). The PII is stored in a source table managed by an account of a data consumer. An identity resolution process is performed based on detecting the update. The identity resolution process includes generating a secure identifier of a user associated with the PII. The method further includes generating at the account of the data provider, a result table including the secure identifier. The result table is shared with the account of the data consumer.

Abstract: The subject technology receives first party training data provided by an end-user of a baseline machine learning model. The subject technology determines a first set of common features based on the first party training data. The subject technology receives, from at least one data source. The subject technology determines a second set of common features based on the set of datasets. The subject technology trains, using the first set of common features and the second set of common features, a second machine learning model, the second machine learning model incorporating additional training data from the external data supplier during training compared to the baseline machine learning model. The subject technology generates a boosted machine learning model based at least in part on the training, the boosted machine learning model comprising the trained second machine learning model.

Abstract: Techniques for identity resolution and data enrichment include configuring, during an onboarding process at an account of a data provider, at least one parameter associated with access to identity resolution functions by an account of a data consumer. A first shared data object is generated at the account of the data provider. The first shared data object corresponds to a second shared data object at the account of the data consumer. The second shared data object at the account of the data consumer is enabled for sharing of log data associated with an application executing at the account of the data consumer. The application is enabled for an identity resolution process based on the detecting of the second shared data object. Source data associated with the identity resolution functions is encoded for communication to the application at the account of the data consumer based on the enabling.

Abstract: Systems and methods for managing membership in a private data exchange are provided herein. In one embodiment, the method includes receiving a request for access to a first listing of a data exchange, each listing of the data exchange comprising version metadata. The method further includes, in response to the request, accessing a first version of a data set referenced by the first listing, wherein the first version of the data set comprises a first structure defined by first version metadata, a second listing of the data exchange references a second version of the data set, the second version of the data set comprising a second structure defined by second version metadata, and the second structure is incompatible with the first structure.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for sharing, on a distributed database, a database application to a first user of the distributed database, the database application generated by a second user of the distributed database. The training dataset includes a first database training dataset from the first user of the distributed database and a second database training dataset from the second user of the distributed database, the first database training dataset and the second database training dataset including non-overlapping dataset features. The database application further identifies a query from the second user to train the machine learning model on the training dataset and generates a trained machine learning model by training the machine learning model on a joined dataset according to the query. The database application generates outputs from the trained machine learning model by applying the trained machine learning model on new data.

Abstract: Disclosed herein are methods and systems for secure data comparison using data clean rooms. In an embodiment, a computer system generates a replica database based on a provider database, which stores a cross reference table that cross references a client dataset of a client database and a provider dataset of the provider database. The system receives, at the replica database, a table that is generated by the client database using the cross-reference table. The system transmits, from the replica database, the table to the provider database. The system receives, at the replica database, a results dataset that is generated by the provider database by applying a database statement to the provider database using the table generated by the client database. The system shares, from the replica database, the results dataset with the client database.

Abstract: Disclosed herein are systems and methods for query processing with restrictions in a database clean room. In an embodiment, a system receives a query directed to a combination of a first source dataset from a first database account of a distributed database and a second source dataset from a second database account of the distributed database. The system generates an approved statements table that contains database statement language that can be executed against the combination of the first and second source datasets. Based on determining that the approved statements table includes the query, the system executes the query to produce results data, and stores the results data in the first database account.

Abstract: Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

Abstract: Techniques for identity resolution and data enrichment include configuring, at an account of a data consumer, an outbound share. The outbound share is designating a share at an account of a data provider as a receiving share. An identity resolution application is instantiated at the account of the data consumer. An instruction originating from the account of the data provider is decoded at the account of the data consumer. The instruction is generated based on the configuring of the outbound share. The instruction enables the identity resolution application for an identity resolution process. Source data is retrieved from the account of the data provider at the account of the data consumer. The source data is associated with the identity resolution process.

Abstract: A method includes installing, in a consumer database account, a shared-instance database that includes a shared instance of a provider-account database that resides in a provider database account. The shared-instance database includes a first schema that includes provider-account training data, provider-account scoring data, a training function, and a scoring function. The method also includes invoking the training function from the consumer database account, which results in creation in the consumer database account of a second schema that includes a machine-learning-model instance of a machine learning model, and which also results in training the machine-learning model instance with at least the provider-account training data. Additionally, the method includes generating consumer-account scoring data by inputting, into the trained machine-learning-model instance, consumer-account input data that is stored in the consumer database account.

Abstract: Embodiments of the present disclosure may provide a data sharing system implemented as a local application of a distributed database. A query from a query template can be validated and executed against shared dataset that comprises portions of data from the database dataset and additional portions of data from another database of the distributed database.

Abstract: A method includes generating a defined access clean room in a provider database account, as well as installing, in a consumer account, an application instance that implements the defined access clean room. The method also includes sharing, by the provider database account, source provider data with the defined access clean room, where the sharing makes the source provider data accessible to the consumer database account via the application instance. The method further includes sharing, by the provider database account, a query template with the consumer database account via the application instance. Additionally, the method includes receiving a query generated by the consumer database account based on the query template, validating that the query is consistent with the query template, responsively generating query results by executing the query, as well as storing the query results in the consumer database account.

Abstract: Systems and methods for managing membership in a private data exchange are provided herein. In one embodiment, the method includes generating a plurality of listings in a data exchange, each of the plurality of listings comprising version metadata and referencing a data set within a database. The method further includes receiving a request for access to a first listing of the plurality of listings and, in response to the request, accessing a first version of the data set that is compatible with first version metadata of the first listing.

Abstract: A system for generating similarity data for different datasets in a cloud data platform. A first dataset of a plurality of datasets on the cloud data platform is identified, where the first dataset is associated with a first user of the cloud data platform. A semantic type for each feature the first dataset is identified, and each semantic type for the first dataset is compared with existing data of the first user. Semantic types for each feature of each dataset are identified, and each semantic type for the first dataset is compared to each semantic type of each dataset. Overlap requests are generated to output overlap datasets between the first dataset and each of the plurality of datasets. A results dataset is generated by applying the overlap requests to a joined dataset comprising data from the first dataset and data from each of the plurality of datasets.

Abstract: Providing access to consumer information on a private data exchange is described. In an example, privileges are assigned to a share object, granting access to consumer information obtained from a query against data referenced by a data listing of a data exchange. The data exchange comprises one or more data listings provided by one or more data providers. In response to the share object being added to a list of consumed share objects of a data provider that provided the data listing, a database is generated that references the consumer information based on the share object. Access to the database is granted to the data provider that provided the data listing.

Abstract: A system for generating similarity data for different datasets in a cloud data platform. A first dataset of a plurality of datasets on the cloud data platform is identified, where the first dataset is associated with a first user of the cloud data platform. A semantic type for each feature the first dataset is identified, and each semantic type for the first dataset is compared with existing data of the first user. Semantic types for each feature of each dataset are identified, and each semantic type for the first dataset is compared to each semantic type of each dataset. Overlap requests are generated to output overlap datasets between the first dataset and each of the plurality of datasets. A results dataset is generated by applying the overlap requests to a joined dataset comprising data from the first dataset and data from each of the plurality of datasets.