The　rapid　development　of　the　latest　distributed　computing　paradigm,　i.　e.,　cloud　computing,　generates　a　highly　fragmented　cloud　market　composed　of　numerous　cloud　providers　and　offers　tremendous　parallel　computing　ability　to　handle　big　data　problems.　One　of　the　biggest　challenges　in　multiclouds　is　efficient　workflow　scheduling.　Although　the　workflow　scheduling　problem　has　been　studied　extensively,　there　are　still　very　few　primal　works　tailored　for　multicloud　environments.　Moreover,　the　existing　research　works　either　fail　to　satisfy　the　quality　of　service　(QoS)　requirements,　or　do　not　consider　some　fundamental　features　of　cloud　computing　such　as　heterogeneity　and　elasticity　of　computing　resources.　In　this　paper,　a　scheduling　algorithm,　which　is　called　multiclouds　partial　critical　paths　with　pretreatment　(MCPCPP),　for　big　data　workflows　in　multiclouds　is　presented.　This　algorithm　incorporates　the　concept　of　partial　critical　paths,　and　aims　to　minimize　the　execution　cost　of　workflow　while　satisfying　the　defined　deadline　constraint.　Our　approach　takes　into　consideration　the　essential　characteristics　of　multiclouds　such　as　the　charge　per　time　interval,　various　instance　types　from　different　cloud　providers,　as　well　as　homogeneous　intrabandwidth　vs.　heterogeneous　interbandwidth.　Various　types　of　workflows　are　used　for　evaluation　purpose　and　our　experimental　results　show　that　the　MCPCPP　is　promising.