International Journal For Multidisciplinary Research

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

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Modernizing Legacy ETL with Cloud-Native Lakehouse Architecture: Migration Strategies from Traditional Integration Platforms

Author(s) Mr. Anil Kolla
Country United States
Abstract Enterprises operating legacy extract-transform-load platforms face a recurring dilemma: the platforms are business-critical, poorly documented, expensive to license and scale, and increasingly incapable of supporting analytic and machine learning workloads, yet wholesale replacement carries unacceptable risk. This paper presents a migration methodology for moving from traditional GUI-based integration platforms to a cloud-native lakehouse architecture. Rather than treating migration as a translation exercise, we frame it as incremental strangulation of a legacy system under continuous parallel verification. The methodology comprises six phases — inventory and dependency reconstruction, value-and-risk stratification, target pattern definition, incremental migration with dual-run reconciliation, cutover, and decommissioning — with explicit entry and exit criteria for each. We contribute a job classification scheme that predicts migration difficulty from observable properties of legacy artefacts, a reconciliation protocol specifying how equivalence between legacy and target outputs is established and what tolerances are defensible, and a technical debt accounting model distinguishing debt eliminated, debt migrated, and debt newly created. We further catalogue the operational risks that recur in such migrations — semantic drift in undocumented transformations, hidden scheduling dependencies, silent type coercion differences, and the erosion of institutional knowledge as legacy maintainers depart — and propose mitigations. An evaluation protocol is specified covering delivery, performance, cost, quality, and organisational measures.
Keywords ETL modernization, data migration, lakehouse architecture, cloud migration, technical debt, strangler pattern, data engineering, legacy systems.
Field Computer > Data / Information
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-08-10
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.88703
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E-ISSN 2582-2160
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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