Support high-performance computing engine

Kepler relies on OushuDB computing storage engine to greatly improve the efficiency of massive data analysis. By introducing stream computing, Kepler realizes the combination of static tables and stream computing in the data analysis process, more completely depicts the analysis object, and greatly improves the analysis accuracy.

Access to all categories of data assets

Kepler supports the definition and relationship of four types of data application assets: labels, indicators, reports, and models. It realizes the sharing of different types of data assets, further improves the value of data, and avoids data duplication and data ambiguity.

Industry knowledge precipitation and practice

Different from traditional BI tools, even number technology has built Kepler‘s summary model design, indicator label classification system, indicator label information summary, and Story analysis report template based on rich project experience and knowledge accumulation in the past, which really breaks through the barriers between technology and business departments.