The transition from raw sequencing data to validated, analysis-ready outputs (Data Screening V2O) is a critical bottleneck in genomic and metagenomic research. While large-scale HPC resources are often highlighted for primary analysis, the iterative and exploratory nature of data screening and quality control (QC) demands a dedicated, small-scale compute project. This localized environment is essential for conducting preliminary assessments, including raw read QC, adapter trimming, and contamination checks, without congesting shared institutional clusters. For metagenomics, it enables rapid taxonomic profiling to validate sample integrity before committing extensive resources. A tailored small compute project provides researchers with immediate, interactive control over these vital steps, facilitating rapid hypothesis testing and protocol adjustments. This agility drastically reduces project latency and prevents the costly misallocation of HPC resources on flawed datasets. By establishing a robust, reproducible V2O workflow on a local server or high-performance workstation, researchers ensure data integrity from the outset, creating a solid foundation for all subsequent large-scale comparative genomics, variant calling, or assembly tasks. Ultimately, this targeted investment in small-scale compute is not a substitute for HPC, but a prerequisite for its efficient and effective use, accelerating the entire research lifecycle.