Abstract: A device may receive source code from a source data structure, and may receive information associated with a target data structure. The device may analyze the source code to extract statements, and may utilize natural language processing on the statements to identify functions and keywords associated with the source data structure. The device may train a machine learning model with the functions and the keywords to generate a trained machine learning model, and may process the information associated with the target data structure, with the trained machine learning model, to transform a source query to a target query compatible with the target data structure. The device may process the target query, with a neural network model, to generate an optimized target query, and may cause data from the source data structure to be migrated to the target data structure based on the optimized target query.

Abstract: Examples of a data integration and curation system are provided. The system may receive data extracting requirement related to a digital initiative and obtain digital initiative data associated with the same. The system may classify the digital initiative data into various data domains and extract metadata therefrom based on data analysis. The system may determine a word index including a plurality of words from a knowledge database. The plurality of words may be mapped with the data domains to identify a data anomaly in the digital initiative data. The system may determine an anomaly correction for resolving the data anomaly and update the plurality of data domains to include the anomaly correction. The system may identify a user parameter associated with the digital initiative. The system may associate the user parameter with the metadata to create user parameter data associated with the user parameter to generate a data extraction result.

Abstract: Examples of a data integration and curation system are provided. The system may receive data extracting requirement related to a digital initiative and obtain digital initiative data associated with the same. The system may classify the digital initiative data into various data domains and extract metadata therefrom based on data analysis. The system may determine a word index including a plurality of words from a knowledge database. The plurality of words may be mapped with the data domains to identify a data anomaly in the digital initiative data. The system may determine an anomaly correction for resolving the data anomaly and update the plurality of data domains to include the anomaly correction. The system may identify a user parameter associated with the digital initiative. The system may associate the user parameter with the metadata to create user parameter data associated with the user parameter to generate a data extraction result.

Abstract: Examples of systems and method for data transition are described. In an example, the present disclosure provides for automating the process of data movement from on premise to cloud, i.e., source Data warehouse (DWH) movement, ETL to cloud base DWH, ETL. The present disclosure provides for objects identification, metadata extraction, automated data type mapping, target data definition script creation, data extraction in bulk using source native optimized utilities, users and access control mapping to the target DWH, binary object movement, end-end audit report, and reconciliation reports.

Abstract: A device may receive source code from a source data structure, and may receive information associated with a target data structure. The device may analyze the source code to extract statements, and may utilize natural language processing on the statements to identify functions and keywords associated with the source data structure. The device may train a machine learning model with the functions and the keywords to generate a trained machine learning model, and may process the information associated with the target data structure, with the trained machine learning model, to transform a source query to a target query compatible with the target data structure. The device may process the target query, with a neural network model, to generate an optimized target query, and may cause data from the source data structure to be migrated to the target data structure based on the optimized target query.

Abstract: Examples of systems and method for data transition are described. In an example, the present disclosure provides for automating the process of data movement from on premise to cloud, i.e., source Data warehouse (DWH) movement, ETL to cloud base DWH, ETL. The present disclosure provides for objects identification, metadata extraction, automated data type mapping, target data definition script creation, data extraction in bulk using source native optimized utilities, users and access control mapping to the target DWH, binary object movement, end-end audit report, and reconciliation reports.