In the era of big data, there are more and more application analysis scenarios driven by large-scale data. How to quickly and efficiently extract the information for analysis and decision-making from these massive data brings great challenges to the database system. At the same time, the real-time performance of analysis data in modern business analysis and decision-making requires that the database system can process ACID transactions and complex analysis queries. However, the traditional data partition granularity is too coarse, and cannot adapt to the dynamic changes of complex analysis load; the traditional data layout is single, and cannot cope with the modern increasing mixed transaction analysis application scenarios. In order to solve the above problems, "intelligent data partition and layout" has become one of the current research hotspots. It extracts the effective characteristics of workload through data mining, machine learning, and other technologies, and design appropriate partition strategy to avoid scanning a large number of irrelevant data and guide the layout structure design to adapt to different types of workloads. This paper first introduces the background knowledge of data partition and layout techniques, and then elaborates the research motivation, development trend, and key technologies of intelligent data partition and layout. Finally, the research prospect of intelligent data partition and layout is summarized and prospected.