Aspects of the disclosure relate to a data wrapper engine. A computing platform may receive a query comprising a request for data stored as a CLOB. The computing platform may obtain, from a data storage system, the data stored as a CLOB. The computing platform may generate a file wrapper for the data, wherein generating the file wrapper comprises converting the CLOB to a VARCHAR object and storing the VARCHAR object in the file wrapper. The computing platform may generate, using the VARCHAR object stored in the file wrapper, a SQL response to the query. The computing platform may execute the dynamic SQL response to generate a response to the query. The computing platform may send, to a user device, the response to the query and commands directing the user device to display the response to the query, which may cause the user device to display the response.

A computer-implemented method for deduplicating records includes generating a block comprising of a group of records. The method also includes creating one or more record pairs from the block, and calculating one or more features based on one or more fields within the one or more record pairs. The method further includes inputting the one or more features into a machine language trained model to predict a probability score. The probability score indicates whether two records are duplicates. The method also includes storing the probability score as links between two vertices in a graph, and displaying one or more duplicate records by querying the graph.

A computer-implemented method includes receiving a message comprising an origin, a destination and a relationship type for a relationship between the origin and the destination. The message further includes a payload. A first node is created in a graph database for the origin and a second node is created in the graph database for the destination. A relationship is set between the first node and the second node in the graph database based on the relationship type. A node is created in the graph database for the message while preventing the payload from being stored in the graph database. A relationship is created between the first node and the node for the message. The message, including the payload, is stored in a searchable datastore separate from the graph database.

Distributed query processing is often performed by a set of nodes that apply MapReduce to a data set and materialize partial results to storage, which are then aggregated to produce the query result. However, this architecture requires a preconfigured set of database nodes; can only fulfill queries that utilize MapReduce processing; and may be slowed down by materializing partial results to storage. Instead, distributed query processing can be achieved by choosing a node for various portions of the query, and generating customized code for the node that only performs the query portion that is allocated to the node. The node executes the code to perform the query portion, and rather than materializing partial results to storage, streams intermediate query results to a next selected node in the distributed query. Nodes selection may be involve matching the details of the query portion with the characteristics and capabilities of the available nodes.

Examples herein include systems and methods for processing natural language queries across database platforms. An example method can include storing relational graphs representing relational paths between resources, such as by using nodes and edges. When a user inputs a query in natural language format, the method can identify and extract a matching intent and entity using a natural language understanding tool trained with an automated script. The method can include fetching a relational path and formatting it as an ordered list of nodes and edges. The list can be translated into a framework specific to a first database relevant to the query to obtain a translated path. The translated path can be used to execute the query at the database. Returned results can be displayed as a list of objects on a GUI.

Techniques are disclosed to migrate data via query co-evaluation. In various embodiments, an input data associated with a source database S and a target schema T to which the input data is to be migrated is received. A set of relational conjunctive queries from target schema T to source database S is received. Query co-evaluation is performed on the received set of relational conjunctive queries to transition data from source database S to target schema T.

Techniques are provided for processing a database command in a sharded database. The processing of the database command may include generating or otherwise accessing a shard key expression, and evaluating the shard key expression to identify one or more target shards that contain data used to execute the database command.

A building system including one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to generate an event subscription for a consuming system, the event subscription defining events to be sent to the consuming system. The building system operates to receive an event from an event source, the event comprising data and a timestamp and identify contextual data describing the event in a digital twin, the digital twin comprising a virtual representation of a building. The building system operates to enrich the event with the contextual data and provide, based on the event subscription and the contextual data of the enriched event, the enriched event to the consuming system.

A distributed database system maintains data for a logical table by storing, on a plurality of storage nodes, a collection of key-item pairs. The distributed database system receives a query of the logical table, and identifies one or more portions of a key specified by the query. Based on the one or more portions of the key, the distributed database causes at least one of a get, range query, or scan operation to be performed by one or more of the storage nodes. Results for the query are generated based on one or more items obtained by performance of the operation.

A database query is received at a primary database in a query language of the primary database. A determination is made whether the database query is to be handled by a secondary database different from the primary database but storing synchronized records of at least a portion of the primary database. In response to determining that the database query is to be handled by the secondary database, the database query is translated to a query language of the secondary database, including by determining a tree data structure representation of the database query, translating one or more elements of the tree data structure representation, and synthesizing the tree data structure representation to automatically generate the database query in the query language of the secondary database. The automatically generated database query is provided in the query language of the secondary database to the secondary database.