Author(s): Shreesha Hegde Kukkuhalli
The migration of data pipelines from proprietary Ab Initio systems to Apache Spark has become a crucial step for organizations aiming to scale operations, leverage open-source capabilities, and reduce costs associated with legacy software. Despite these motivations, the transition poses significant technical challenges due to differences in execution models, dependencies, and data transformation structures. This paper presents a framework designed to automate the conversion of Ab Initio data pipelines into Apache Spark code, minimizing manual intervention and preserving data fidelity. The framework includes a pipeline analyzer, an automated code converter, and a validation tool to ensure functional equivalence. Case studies demonstrate the effectiveness of the approach, with reduced migration time and improved scalability.
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