arXiv AI By Imran Basha Shaik, Sahla Sultana, Khader Moinuddin Shaik

Autonomous ETL Pipelines for Salesforce M&A Consolidation: A Reference Framework, Jitterbit Mapping, and Reproducible Simulation Study

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The article presents a reference framework for autonomous extract‑transform‑load (ETL) pipelines to consolidate Salesforce data during mergers and acquisitions. It details a partitioned bulk extraction, declarative mapping, rule‑based and statistical quality gates, master data management with deterministic and blocked fuzzy matching, constrained loading with error capture, and change data capture protected by a data‑health circuit breaker. The prototype was evaluated on synthetic scenarios, achieving high F1 scores for account and contact matching and preventing load failures and data loss.

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