In　the　era　of　big　data,　HBase　has　been　widely　used　in　scenarios　of　massive　unstructured　data.　For　the　financial　big　data,　due　to　the　integrity　and　timing　of　it,　unreasonable　data　storage　and　management　usually　lead　to　hot　spots　that　decreases　the　query　performance.　In　practice,　the　separation　of　hot　and　cold　financial　data　will　improve　data　query　performance　and　utilization　rate　of　cluster　resources.　In　this　paper,　a　hot　and　cold　data　separation　scheme　is　designed,　to　store　infrequently　queried　financial　data　to　HBase,　and　frequently　queried　one　to　Redis.　The　cold　data　is　reasonably　planned　and　managed　through　pre-partitioning　and　row　key　design　for　HBase.　A　hot　data　cache　based　on　Redis　is　realized　to　improve　the　query　speed　and　reduces　the　pressure　of　HBase.　In　addition,　due　to　the　lack　of　Redis＇s　inherent　cache　elimination　strategy,　we　propose　a　caching　strategy　based　on　the　frequencies　of　updating　and　querying　operations.　The　experimental　results　show　that　the　scheme　can　effectively　avoid　the　hot　storage　problem,　and　improve　the　query　performance,　and　improve　the　cache　hit　ratio　of　Redis.　Therefore,　the　number　of　cold　data　access　requests　can　be　effectively　reduced.　©　2019　IEEE.