In big data era, with the development of information technology, a large amount data is collected in every industry. Data is the foundation of digital economy, withholding great value. However, for lack of efficient and feasible data sharing mechanism, data owners seldom communicate with each other, causing the formation of data islands, which is unfavourable to the healthy development of big data industry. And hence, allocating a proper price for data, designing an efficient data market platform have become an important way to eliminate data islands and make data flow fluently. In this paper, we systematically sort out the technology issues regarding to data pricing and trading. Specifically, we introduce the difficulties and related principles of data pricing and trading; divide the life cycle of data in data market into four stages: data collection and integration, data management and analysis, data pricing and finally, data trading. Based on the area of big data management, we elaborate related methods applicable to the first two stages. After that, data pricing methods are categorized, usage scenarios, advantages and shortcoming of these methods are analyzed. The classification of data market is introduced, and the impact of market type and participants’ behaviors on the market and prices of data is studied using game theory and auction as examples. Finally, future research directions on data pricing and trading are discussed.