Maintenance related data are tending to be increasingly huge involume, rapid in velocity and vast in variety. Data with thesecharacteristics bring new challenges with respect to data analysisand data mining, which requires new approaches andtechnologies. In industry, related research and applications, somecontributions have been provided to utilize Big Data technologiesfor extraction of information through pattern recognitionmechanisms via eMaintenance solutions. Today, the existingcontributions are not enabling a holistic approach for maintenancedata analysis and therefore are insufficient. However, theimmense value hidden inside the Big Data in eMaintenance isarousing more and more attention from both academia andindustry. Hence, this paper aims to explore eMaintenancesolutions for maintenance decision-making through utilization ofBig Data technologies and approaches. The paper discusses BigData mining in eMaintenance through a general manner byemploying one of the widely accepted frameworks with the nameof Cross Industry Standard Process for Data Mining (CRISPDM).In addition, the paper outlines features of maintenance dataand investigates six sub-processes (i.e. business understanding,data understanding, data preparation, modeling, evaluation anddeployment) of data mining applications defined by CRISP-DMwithin the domain of eMaintenance.