A big data calculation engine of excellent performance

MPP data mart is the high-performance big data calculation engine software developed by Yonghong independently based on technologies of column storage, distributed computing, memory computing and distributed communication, etc. as a big data computing and processing database system supporting distributed parallel real-time processing, complex query optimization and complex analysis, it has the capacity to process data of ten billion magnitudes and above in a second, which assists you in effortlessly coping with challenges arising in such a big data era.

mpp数据集市
Mpp数据集市秒级计算

Unprecedentedly high performance

Second-level computing makes the longest time turn into a blink; In addition to stand-alone deployment, it can be used for distributed extension based on PC server; built on column storage and memory computing, second responses to ten million and even ten billion magnitude data analysis can be realized; Extremely outstanding performance provides more analysis dimensions and a larger data field for you

Amazing expansibility

In addition to seamless connection with Yonghong Z-Dashboard, its standard JDBC interface also supports other mainstream data analysis stools. Based on MPP data mart, speed of data query, data mining and data presentation can be considerably improved on the premise of keeping reporting functions of the original BI system unchanged. Even if the system runs in a full load state, you can still enjoy real-time processing, agile responses and the pleasure of swimming in data.

Mpp数据集市绝佳扩展性
Mpp数据集市灵活人性化

Flexibility & humanization

Different customer demands are satisfied by two solutions. Due to differences in magnitudes of data to be processed according to customer requirements, in IT system architecture and in storage systems, MPP data mart provides two modes of data accelerator and data grid based on distributed architecture. While the former is applicable to data below TB and ordinary storage structure or the cases that stand-alone has been enough to meet performance requirement, the latter can be applied to heterogeneous database storage systems, data sizes with the magnitude equal to or above TB and PB, distributed IT systems and storage systems and customers who have higher requirements for performance.

Technical architecture

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