High-Performance Data Analytics (HPDA) combines high-performance computing (HPC) with data analytics to uncover patterns and insights in dual-intensive applications that are both data-intensive and compute-intensive. Traditional big data frameworks and HPC technologies often struggle to address these demands independently, prompting researchers to explore their integration. Spark, known for its efficient in-memory computing with RDDs, and MPI, a foundational standard in HPC, are prominent candidates for such integration. This survey explores the integration of Spark and MPI for HPDA, highlighting their potential for unified data processing and computation. We first classify application workloads and review the characteristics and limitations of traditional frameworks. Then, we analyze the challenges and requirements of integrated architectures, focusing on the specific implementations of typical middleware-level architectures. Through comparative analysis, we highlight their advantages and limitations. Finally, we present application examples, outline key challenges and future research directions, and briefly discuss progress in integration approaches for other technology combinations.