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Systems and methods for evaluating, validating, correcting, and loading data based on Artificial Intelligence Input (AI).

For any critical system that deals with huge data always the main challenge is to exchange data with accuracy and on time to avoid SLA breach. Importing data from source file should happen smoothly without any failures. If data load fails due to data anomaly within the source feed or any due to any network issue will impact SLA and business. Here is where the invention "Fix and reload the rejected data automatically within SLA" will provide the solution to minimize the risk by applying the auto correcting techniques proactively on the issues by applying algorithms on historical data and logs.

Description:

An electronic system may be configured to receive data feeds from sources and load the data feeds to data structures. The electronic system may be configured to perform the process of receiving and loading the data feeds in accordance with a service level agreement establishing expected characteristics of the process, such as a speed at which the process is completed, a period by which the process is to be completed, and/or the like.

Current existing techniques also do not enable real-time synchronization between different servers. For example, databases in a production server may not be efficiently synchronized with a development server for development and testing purposes. In absence of databases being real-time synchronized to development servers, any errors in production server may perpetuate in the production server or may need to be corrected using inefficient trial and error techniques.

Migrating databases between servers corresponding to different network database applications may be a resource intensive process, both in terms of manpower, time, and computing resources. During the migration process, an enterprise organization may need to parallelly maintain production and development servers for each of the network database applications. Using parallel servers may result in inefficiencies for the enterprise organization. For example, monitoring may need to be done parallelly in both the existing servers and the new servers (e.g., for performance validation of databases migrated to new servers). Further, development processes may need to be completed in the previous server prior to migration of databases to the new server. Separate teams may need to be maintained for each of the servers while the migration is underway and for determining whether the migration to the new servers is successful and beneficial. Further, it may only be possible to determine the performance of the databases in the new server once all the databases have been migrated to the new server

About Author:

Damodarrao Thakkalapelli is passionate about solving enterprise data architecture problems and ensuring customers achieve their desired business outcomes. He designed comprehensive solutions as a solution architect, considering a variety of elements like hardware, software, network infrastructure, and data management systems.

As a Solution Architect, he assesses many technological possibilities and decides wisely based on compatibility, cost, and best practices in the industry. He oversees the implementation process, directs development teams, and manages technical issues. He also makes recommendations for the technologies that are most appropriate for the organization's long-term strategy. He ensures the implemented solution adheres to the design principles, meets quality standards, and fulfils business requirements.

As an Architect he always investigates, identify, and assesses risks associated with the solution, such as security vulnerabilities, data privacy concerns, and performance bottlenecks. He develops strategies to mitigate risks and ensure the solutions reliability and robustness. He continuously evaluates the implemented solution, gathers feedback, and identifies areas for improvement. He stays updated with emerging technologies, industry trends, and best practices, incorporating them into future solution designs.

Disclaimer : The above is a sponsored article and the views expressed are those of the sponsor/author and do not represent the stand and views of The Tribune editorial in any manner.

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Discussing About Artificial Intelligence (AI) in Data Science with Damodarrao Thakkalapelli -Data Solutions Architect - The Tribune India