A method of matching records from a plurality of data sources having variation between them in matching quality of their data, the method including repeatedly matching and filtering records from the data sources to obtain matched records using successively less strict matching rules, the matching rules being defined based on the variation in the matching quality.

A method of matching records from a plurality of data sources having variation between them in matching quality of their data, the method including repeatedly matching and filtering records from the data sources to obtain matched records using successively less strict matching rules, the matching rules being defined based on the variation in the matching quality.

Aspects of the disclosure relate to an automated query analysis and remediation tool. A computing platform may receive a query for analysis. The computing platform may load an extensible markup language (XML) query execution plan for the received query. In addition, the query execution plan may include a sequence of operations used to access data in a relational database. The computing platform may shred XML data from the query execution plan into relational database tables. The computing platform may identify tuning parameters based on the shredded XML data. Based on the identified tuning parameters and using a machine learning engine, the computing platform may generate an optimized query. The computing platform may cause the optimized query to be displayed on one or more user interfaces.

Aspects of the disclosure relate to an automated query analysis and remediation tool. A computing platform may receive a query for analysis. The computing platform may load an extensible markup language (XML) query execution plan for the received query. In addition, the query execution plan may include a sequence of operations used to access data in a relational database. The computing platform may shred XML data from the query execution plan into relational database tables. The computing platform may identify tuning parameters based on the shredded XML data. Based on the identified tuning parameters and using a machine learning engine, the computing platform may generate an optimized query. The computing platform may cause the optimized query to be displayed on one or more user interfaces.

Methods and systems may be associated with a cloud platform computing environment. A mapping expression data store may contain mapping expressions previously created by customer tenants of the cloud platform. Each mapping expression may be associated with at least one source mapping element and at least one target mapping element. A mapping expression generator may receive, from a user of a first customer tenant, a mapping expression search request that includes at least one requested source mapping element and at least one requested target mapping element. The mapping expression generator may then search the mapping expression data store based on the at least one requested source mapping element and at the least one requested target mapping element and output a result of the search to the user. The result of the search may include at least one mapping expression created by a customer tenant other than the first customer tenant.

Methods and systems may be associated with a cloud platform computing environment. A mapping expression data store may contain mapping expressions previously created by customer tenants of the cloud platform. Each mapping expression may be associated with at least one source mapping element and at least one target mapping element. A mapping expression generator may receive, from a user of a first customer tenant, a mapping expression search request that includes at least one requested source mapping element and at least one requested target mapping element. The mapping expression generator may then search the mapping expression data store based on the at least one requested source mapping element and at the least one requested target mapping element and output a result of the search to the user. The result of the search may include at least one mapping expression created by a customer tenant other than the first customer tenant.

Embodiments are described for a system and method to analyze data at a plurality of data sources. A data analytic workflow may be received. The data analytic workflow may include at least one operation to be performed on a plurality of data sets stored at a plurality of data sources. Instructions may be created based on the operation to be performed and a type of platform that operates the data sources. Furthermore, the instructions may be transmitted to the data sources such that the data sources may execute the operations on the data sets stored at the data sources.

A data transformation tool includes a receiver, an extraction engine, a transformation engine, and a delivery engine. The receiver receives from a user a request indicating an identifier for reference data, a format, and a delivery type. The reference data includes a data field, a list of permissible values for the data field, and a textual description of the data field. The extraction engine generates, based on the request, a database query requesting reference data identified by the identifier from a database and communicates the query to the database. The transformation engine receives, from the database, the reference data in response to communicating the query to the database and transforms the received reference data into a flat file based on the format indicated by the request. The delivery engine delivers the flat file to the user according to the user using the deliver type indicated by the request.

A data transformation tool includes a receiver, an extraction engine, a transformation engine, and a delivery engine. The receiver receives from a user a request indicating an identifier for reference data, a format, and a delivery type. The reference data includes a data field, a list of permissible values for the data field, and a textual description of the data field. The extraction engine generates, based on the request, a database query requesting reference data identified by the identifier from a database and communicates the query to the database. The transformation engine receives, from the database, the reference data in response to communicating the query to the database and transforms the received reference data into a flat file based on the format indicated by the request. The delivery engine delivers the flat file to the user according to the user using the deliver type indicated by the request.

Embodiments of the disclosure provide methods, systems, computer program products for manipulating a table with an XML column in database. According to the method, a SQL statement for a table is received first, wherein the table comprises an XML column which is a representation of a plurality of logical columns of the table, there is at least one XML element in at least one row and in the XML column of the table, and each of the at least one XML element corresponds to a non-null value in a specific row and in a logical column of the plurality of logical columns of the table. Then the SQL statement is parsed. And then the SQL statement is transformed into a hybrid statement being able to process the XML column in response to the parsing result indicating that the SQL statement relates to at least one logical column of the plurality of logical columns. At last the hybrid statement is executed.