This review provides a comprehensive overview of data-independent acquisition (DIA) mass spectrometry, a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. The authors categorize and describe various DIA acquisition schemes and associated software tools, focusing on their chronological development. Key acquisition schemes include full-scan DIA, windowed DIA (such as wide-window, narrow-window, overlapping-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA), and unconventional methods like mixed-mode DIA and direct-infusion DIA. The review also covers major data analysis strategies, including spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. Detailed descriptions of specific software tools implementing these strategies are provided, along with their workflows and scoring methods. Additionally, the review discusses the generation and optimization of spectral libraries and benchmark datasets for performance evaluation. The authors highlight the continuous advancements in DIA workflows, emphasizing the importance of versatile components for enhancing the power of DIA-based proteomics.This review provides a comprehensive overview of data-independent acquisition (DIA) mass spectrometry, a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. The authors categorize and describe various DIA acquisition schemes and associated software tools, focusing on their chronological development. Key acquisition schemes include full-scan DIA, windowed DIA (such as wide-window, narrow-window, overlapping-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA), and unconventional methods like mixed-mode DIA and direct-infusion DIA. The review also covers major data analysis strategies, including spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. Detailed descriptions of specific software tools implementing these strategies are provided, along with their workflows and scoring methods. Additionally, the review discusses the generation and optimization of spectral libraries and benchmark datasets for performance evaluation. The authors highlight the continuous advancements in DIA workflows, emphasizing the importance of versatile components for enhancing the power of DIA-based proteomics.