A configuration for a component of a primary node is synchronized with a configuration for a component of a partner node in a different cluster by replicating the primary node configuration with the partner node. A baseline configuration replication comprises a snapshot of a component configuration on the primary. The baseline configuration can be generated by traversing through the configuration objects, capturing their attributes and encapsulating them in a package. The baseline package can then be transferred to the partner node. The configuration objects can be applied on the partner node in the order in which they were captured on the primary node. Attributes of the configuration objects are identified that are to be transformed. Values for the identified attributes are transformed from a name space in the primary node to a name space in the partner node.

Systems, methods, and devices for generating a secure join of database data are disclosed. A method creates a secure view of datapoints of a consumer account and processes, using a secure user defined function (UDF), the datapoints of the consumer account and datapoints of a provider account to generate a secure join key. The secure UDF returns a count of matching data points between the consumer account and the provider account, and the method provides the count of matching data points to the consumer account.

Systems and methods are provided for managing and accessing data using one or more data cloud servers. An exemplary method includes: receiving from one or more data sources, a first data set; stratifying the first data set into first samples; receiving from second one or more data sources, a second data set; stratifying the second data set into second samples; computing a projection factor for each of the second samples using the first samples; computing projected samples using the projection factor for each of the second samples; receiving from third one or more data sources, a third data set; computing a parameter using the third data set; selecting one or more of the projected samples to form a fourth data set; and performing a computer operation for estimating the data using the fourth data set and the parameter.

A computer-implemented method for database management is provided. The method comprises: receiving, from a client device, first data to be stored in a database system that comprises first data storage configured to store a data table and a deletion history table; storing the first data in second data storage that is external to the database system and that is in communication with the database system via a network; obtaining a link that enables access, via the network, to the first data stored in the second data storage; storing the link in the data table; and performing a deletion operation of the first data, in response to a request from the client device to delete the first data from the database system, wherein the deletion operation comprises: deleting the link from the data table without deleting the first data from the second data storage; and storing the link in the deletion history table with a timestamp corresponding to a point in time when the link is deleted from the data table.

Systems and methods are provided for a computing system in a cloud computing environment to receive a request for planning data via a user interface of a computing device accessing a planning application executing in the cloud computing environment and to execute queries corresponding to the request for planning data against an SAP Business Warehouse Integrated Planning Cube (BW cube). The computing system loads data received from the executed queries into an application layer of the planning application executing in the cloud computing environment and stores each manipulation action to the loaded data in the application layer of the planning application executing in the cloud computing environment without persisting any data to the BW cube. The computing system persists the updated data to the BW cube only upon detecting completion of the manipulation actions.

Embodiments of the present disclosure may provide a data clean room architecture that restricts data included in the clean room. The data clean room architecture can implement a policy to enable data restrictions for data shared between multiple parties via a distributed database. Multiple database accounts can implement validation instances to validate queries when received from other database accounts. One or more of the database accounts can provide a query template that is congruent with the validation instance for use by the other database accounts to generate queries against the data shared in the data clean room.

Methods and apparatuses related to federated search of multiple sources with conflict resolution are disclosed. A method may comprise obtaining a set of data ontologies (e.g., types, properties, and links) associated with a plurality of heterogeneous data sources; receiving a selection of a graph comprising a plurality of graph nodes connected by one or more graph edges; and transforming the graph into one or more search queries across the plurality of heterogeneous data sources. A method may comprise obtaining a first data object as a result of executing a first search query across a plurality of heterogeneous data sources; resolving, based on one or more resolution rules, at least the first data object with a repository data object; deduplicating data associated with at least the first data object and the repository data object prior to storing the deduplicated data in a repository that has a particular data model.

Various embodiments provide for replicating a share across deployments of a data platform, where the share can be on a source deployment and the share can be replicated on one or more target deployments, and where the share is replicated with one or more database objects of the source deployment associated with the share. Some embodiments analyze the share to be replicated and, based on the analysis, determine one or more database objects that would be replicated to the one or more target deployments to enable a replica of the share on the one or more target deployments.

This disclosure provides methods and techniques of data replication involving cross replication group (RG) references. The present disclosure avoids automatic replication failing when an entity in an RG refers to another entity external to the RG. The entity to be replicated within the RG is referred to as the “referring entity,” and the entity as the dangling reference is referred to as the “referred entity.” Although the referring and referred entities are not replicated together in a replication operation, the referred entity may have already been replicated to the target account in another replication operation on a different replication group. In such cases, the data replication procedure may, according to aspects of the present disclosure, check if the referred entity has already been replicated, and if so, proceed to replicate the referring entity without fail, and link the referring and referred entities to enable normal functioning of the referring entity.

According to aspects of the disclosure, there is provided a scalable cloud distributed database system for hosting data of multiple database tenants. In some embodiments, the database may be serverless. The serverless database may be configured to automatically and dynamically match resources to workload demands for tenants of the database. Databases described herein may include replica sets hosting multiple tenants. Tenants may be migrated from one replica set to another replica set based on usage. Usage of tenants may be throttled during migration between replica sets. Tenants with lower usage may be selected for migration between replica sets. During migration, files containing both a document and a history of updates to the document may be migrated. Databases described herein may include multiple storage tiers having different read and/or write speeds.