Research on Acceleration Techniques for Point Queries on Time-Series Data in Lake-Warehouse Integrated Architecture

With the development of big data technology, the integration of data lakes and data warehouses has become an important trend to enhance data processing efficiency and support complex analyses. In the power industry, efficient processing of time-series data is particularly crucial, especially as the performance of point queries directly affects the timeliness and accuracy of data analysis. This paper explores technical solutions for accelerating point queries on time-series data within a lake-warehouse integrated architecture. The study first outlines the characteristics and processing requirements of time-series data in the power industry, analyzing the performance bottlenecks of traditional data lakes when handling point queries. Subsequently, the application of Apache Paimon in the lakehouse architecture is elaborated in detail, and an efficient indexing method is proposed to enhance the capability of point queries in data lakes. Through an in-depth analysis of the lakehouse architecture, this paper aims to provide a solution for the electric power industry that can meet the demands for efficient point queries of large-scale time-series data, to support faster and more accurate data-driven decision-making.