Data stored in databases keep growing as a result of businesses requirements for more information. A big portion of the cost of keeping large amounts of data is in the cost of disk systems, and the resources utilized in managing that data. This paper introduces various compression techniques for data stored in row oriented as well as column-oriented databases. Keeping data in this compressed format as it is operated upon has been shown to improve query performance by up to an order of magnitude. Intuitively, data stored in columns is more Compressible than data stored in rows. Compression algorithms perform better on data with low information entropy (high data value locality) i.e are used for optimization purpose. © 2010 IEEE.