Twitter has grown significantly in the past several years and provides a new vector for data collection, offering individual users and companies valuable insights. This presents a technical challenge to collect and analyze all the data in an efficient manner. Traditional relational databases have not been able to provide acceptable response times that this new problem presents, and focus has started shifting to newer technologies such as NoSQL databases. In this paper, we try to answer a question as follows: "If I want to store and access millions of tweets for data analysis, which database systems should I choose?" We selected four popular SQL and NoSQL database systems and tested on different twitter dataset varying from one million to fifty million tweets. Each workload test involves running a core set of data operation commands. The experiment results are promising and provide guideline for choosing the most efficient database systems based on different user requirements.