Memory-centric computing is essential in big data analysis for minimizing data movement and latency, enabling efficient, real-time processing of large-scale datasets. We proposed MECA (Memory-Expansion Cache-coherent Architecture), a memory-centric design that leverages OmniXtend protocol over Ethernet to establish cache coherence across large-scale memory pools, utilizing RISC-V architecture to ensure efficient and consistent memory sharing. Performance evaluations show MECA achieves up to 98% of local memory performance for matrix-intensive workloads through hybrid memory optimization, significantly reducing latency and bandwidth limitations. These results highlight MECA's potential as an open-source alternative to proprietary solutions like CXL, promoting innovation and scalability in memory-centric computing for data-intensive applications.