This success story describes how an Atlanta-based Fortune 50 retailer needed a repeatable way to migrate very large datasets from legacy databases to a distributed cloud database while keeping real-time replication running, since its phased modernization meant legacy and new systems had to stay synchronized during cutovers. Traditional staging-and-batch loading did not scale for tables with hundreds of millions to billions of rows and would take days per table. Adapture deployed Apache NiFi on a three-node Google Cloud cluster and built a pipeline to split files, batch and transform records, manage back pressure and errors, and load data with zero loss and real-time progress tracking. The solution processed about 100,000 inserts per second (roughly 350 million records per hour), scaled hor

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