In the big data era, an enormous amount of data is collected in every industry with the development of information technology. Data is the foundation of the digital economy, containing great value. However, for the lack of efficient and feasible data-sharing mechanisms, data owners seldom communicate with each other, which leads to the formation of data islands and is unfavorable to the healthy development of the big data industry. Hence, allocating a proper price to data and designing an efficient data market platform have become important ways to eliminate data islands and secure sufficient data flow. This study systematically sorts out the technical issues regarding data pricing and trading. Specifically, the difficulties and related principles of data pricing and trading are introduced. The life cycle of data in the data market is divided into four stages: data collection and integration, data management and analysis, data pricing, and data trading. Upon the research on big data management, related methods applicable to the first two stages are elaborated. After that, data pricing methods are categorized, and usage scenarios, advantages, and shortcomings of these methods are analyzed. Moreover, the classification of data markets is introduced, and the impact of market types and participants’ behavior in data trading on the trading process and prices is studied with game theory and auctions as examples. Finally, future research directions of data pricing and trading are discussed.