  This work presents the evolution of a solution for predictive maintenance to
a Big Data environment. The proposed adaptation aims for predicting failures on
wind turbines using a data-driven solution deployed in the cloud and which is
composed by three main modules. (i) A predictive model generator which
generates predictive models for each monitored wind turbine by means of Random
Forest algorithm. (ii) A monitoring agent that makes predictions every 10
minutes about failures in wind turbines during the next hour. Finally, (iii) a
dashboard where given predictions can be visualized. To implement the solution
Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we
have improved the previous work in terms of data process speed, scalability and
automation. In addition, we have provided fault-tolerant functionality with a
centralized access point from where the status of all the wind turbines of a
company localized all over the world can be monitored, reducing O&M costs.
