Machine learning holds great promise for accelerating enzyme optimization, but its power is fundamentally constrained by the limited availability of sequence-fitness data. Here, we introduce MillionFull, a low-cost method that enables high-throughput full-length sequence- fitness mapping for enzymes of arbitrary length. Each run yields on the order of 10⁵-10⁷ data points, capturing sequence-function relationships at unprecedented scale. By overcoming the data bottleneck, MillionFull provides a foundation for dramatically advancing AI-driven enzyme engineering.