International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 4
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Effective Payroll Processing system for Retail Employees using Databricks Ingestion Framework and Salesforce Approval Workflow

Author(s) Vamshi Krishna Malthummeda
Country United States
Abstract An effective payroll processing system is a hallmark of a well-functioning organization which provides key benefits like accurate calculations of wages, taxes, benefits, adherence to compliance & regulations and finally Improved Employee satisfaction along with low attrition rate. This paper proposes a solution for payroll processing by leveraging Databricks (built on Apache Spark which can handle massive datasets and perform complex computations) and Salesforce approval workflow (helps in review & routing of timesheet approval requests, enables quicker decision making, helps in tracking progress and status of timesheet approvals and provides comprehensive audit trail). In the proposed solution the databricks job will process raw data files containing employee information, retail chain hierarchy information, benefits information, employee task assignment information, login activity information, scheduling information etc. and prepare the timesheet dataset and pushes it into Salesforce connected App for approval process using out of the box bulk API 2.0 REST call. The solution saves lot of time for application maintenance team during the weekly payroll processing day, very adaptable to the new compliance rules, provides higher levels of satisfaction by compensating timely and helps higher management in making informed decisions. The proposed framework offers a resilient, scalable, secured and accurate solution which results in better analytics and workforce retention.
Field Engineering
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-07
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.52673
Short DOI https://doi.org/g9wnrv
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E-ISSN 2582-2160
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