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

Abstract:Merging companies that each run Salesforce must consolidate their customer data into a single organization while both source systems remain in use. This requires reconciling different schemas and picklists, resolving duplicate customers across and within organizations, preserving relationships between records, and keeping the target current until cutover. This article proposes a reference framework for an autonomous extract-transform-load pipeline for this task. It combines partitioned bulk extraction, declarative mapping templates, rule-based and statistical quality gates, master data management with deterministic and blocked fuzzy matching, constrained loading with per-record error capture, and change data capture guarded by a data-health circuit breaker. Each stage is mapped to Jitterbit Integration Studio constructs. The framework was implemented as an open reference prototype and evaluated on synthetic two-organization scenarios with known ground truth at three scales, from 32,040 to 383,973 source records, over five random seeds. At a preset threshold, hybrid matching reached pairwise F1 scores of 0.918 for accounts and 0.885 for contacts; calibrating the threshold per object would raise these to 0.938 and 0.963. The quality gates prevented all load failures and all silent loss of unmapped picklist values without wrongly flagging any clean record, whereas removing them produced about 1,000 load failures per run. Over five simulated days of hourly incremental synchronization, replayed events were ignored, an injected upstream fault was contained in the batch in which it occurred, and the target agreed with a full rebuild on more than 99.3 percent of field values. Validation on a production deployment remains future work.

16 pages, 4 figures, 7 tables. Code and data: this https URL preprint also archived at this https URL

Databases (cs.DB); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)

arXiv:2611.05918 [cs.DB]

(or arXiv:2611.05918v1 [cs.DB] for this version)

https://doi.org/10.48550/arXiv.2611.05918 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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