| CPC G06F 16/116 (2019.01) | 17 Claims |
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1. A method for dynamic data processing on a cloud environment by utilizing one or more processors along with allocated memory, the method comprising:
implementing a platform and language agnostic dynamic data processing module (DDPM) configured to create a library or a framework that enables parsing and transforming of data dynamically and dynamic processing of expressions to be applied to the data hosted on an application server without depending on Structured Query Language Server Reporting Services (SSRS), the DDPM including a hosting module, a receiving module, a defining module, a validating module, a generating module, a parsing module, a loading module, applying module, saving module, reading module, executing module, and a transforming module, wherein each module being called via corresponding Application Programming Interface (API);
hosting, by calling the hosting module via a first API, an application on a cloud server;
receiving, by calling the receiving module via a second API, data corresponding to the application from a plurality of types of data sources;
defining, by calling the defining module via a third API, expressions to be applied to the data for parsing and transforming the data dynamically on the cloud server;
validating, by calling the validating module via a fourth API, the defined expressions based on a determination that each defined expression meets a predefined threshold value;
generating, by calling the generating module via a fifth API, a lookup data file in a predefined file format in response to validating the defined expressions;
parsing, by calling the parsing module via a sixth API, the lookup data file;
dynamically transforming, by calling the transforming module via a seventh API, the data based on the parsed lookup data file and applying the validated expressions; and
generating, by calling the generating module via the fifth API, an output file in the predefined file format for consumption after transforming the data,
wherein the data is a data stream, and the method further comprising:
loading the entire lookup data file in a memory by calling the loading module via a corresponding API;
applying the defined expressions on the data stream for processing by calling the applying module via a corresponding API;
saving the processed data stream into an intermediate file by calling the saving module via a corresponding API;
loading the processed data into the memory for additional operations by calling the loading module via the corresponding API;
reading the entire intermediate file when it is determined that additional operations are needed by calling the reading module via a corresponding API;
executing the additional operations by calling the executing module via a corresponding API;
dynamically transforming the data based on executing the additional operations by calling the transforming module via the seventh API; and
saving the dynamically processed data back to the lookup data file in the predefined file format for consumption by calling the calling the saving module via a corresponding API.
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