Compression works well with column stores for different reasons - since all columns are things of the same data type, there are way more opportunities for very

Compression works well with column stores for different reasons - since all columns are things of the same data type, there are way more opportunities for very very lightweight and highly effective compression, like run length encoding (a,a,a,a,a,a -> 6a) which helps a lot with sparse or duplicate columns, range compression (1,2,3,4,5 -> 1-5), dictionary compression (hippopotamus,potato,potato,hippopotamus -> 1,2,2,1 + a dictionary like {1:hippo,2:potato}), value scale (203,207,202,201 -> 3,7,2,1 + base:200), bit arrays for booleans and bit vectors for low-cardinality columns, etc. This saves on space but also often improves query latency, provided your execution engine supports queries on compressed columns - less data means more of it fits into cache, meaning less cache misses (i.e. expensive disk reads). edit: So what I mean is compressing the entire files or disk blocks with LZ might not take advantage of the same properties and might not get you the same deal - here often the in-memory data layout is also compressed, and queries execute efficiently on compressed data, versus with a blindly compressed filesystem you'd ostensibly have to always decompress first.