In today’s digital world, fast and efficient data compression is of utmost importance for efficient storage, transmission, and processing of information. This paper focuses on lossless compression algorithms that can fully restore input data after encoding. Several algorithms belonging to three compression techniques, Variable-Length, Statistical, and Universal coding, are described, and the performance of selected algorithms on multiple datasets is compared. The results have shown that there is no universally best compression algorithm. Instead, the choice of algorithm depends on the specific needs of the application. If achieving a small encoded data size is crucial, and speed is not of topmost importance, Arithmetic coding is an excellent choice. However, for fast applications requiring good compression, LZW and Huffman coding provide reasonable results. This paper emphasizes the importance of understanding different data compression algorithms and their performance to select the most suitable one for a specific situation.