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 -	[Top 10 Performance Tuning Techniques for Amazon Redshift](https://blogs.aws.amazon.com/bigdata/post/Tx31034QG0G3ED1/Top-10-Performance-Tuning-Techniques-for-Amazon-Redshift) provides an excellent list of performance tuning techniques.
 -	[Amazon Redshift Utils](https://github.com/awslabs/amazon-redshift-utils) contains useful utilities, scripts and views to simplify Redshift ops.
 -	[VACUUM](http://docs.aws.amazon.com/redshift/latest/dg/t_Reclaiming_storage_space202.html) regularly following a significant number of deletes or updates to reclaim space and improve query performance.
+-	Redshift provides various [column compression](http://docs.aws.amazon.com/redshift/latest/dg/t_Compressing_data_on_disk.html) options to optimize the stored data size. AWS strongly encourages users to use [automatic compression](http://docs.aws.amazon.com/redshift/latest/dg/c_Loading_tables_auto_compress.html) at the COPY stage, when Redshift uses a sample of the data being ingested to analyze the column compression options. However, automatic compression can only be applied to an empty table with no data. Therefore, make sure the initial load batch is big enough to provide Redshift with a representative sample of the data (the default sample size is 100000 rows).
 
 ### Redshift Gotchas and Limitations