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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Data Migration Challenges in Salesforce: A Framework for Intelligent ETL Processes

Author(s) Mahesh Adi
Country India
Abstract Moving data is an important but difficult part of setting up a cloud-based Customer Relationship Management (CRM) system. Because of its multitenant design, rigorous API constraints, metadata dependencies, and platform-specific data types, Salesforce, as a major cloud CRM platform, has particular problems when it comes to data integration and transformation. This article examines the principal obstacles faced during data migration to and from Salesforce settings, encompassing concerns relating to data volume, referential integrity, schema mapping, API governance, and regulatory compliance. It also suggests a framework for smart Extract, Transform, and Load (ETL) operations that integrate with Salesforce's architecture. The framework uses metadata-driven mapping, dependency-aware sequencing, machine learning to forecast errors, and secure token-based access to make sure that data migration is reliable and may grow as needed. The suggested ETL system intends to minimize data loss, reduce downtime, and fulfill regulatory demands like as GDPR and HIPAA by combining AI-powered anomaly detection and workflow orchestration. This study combines information from real-world implementations, Salesforce documentation, and reviews of migration tools like Data Loader, MuleSoft, and Talend to create a strong migration plan for businesses who are moving to or already using Salesforce.
Keywords Salesforce, Data Migration, ETL Framework, Metadata Mapping, API Limits, MuleSoft, Data Loader, Data Integrity, GDPR, Cloud CRM
Field Engineering
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-10
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.49814
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E-ISSN 2582-2160
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