  Predicting the next position of movable objects has been a problem for at
least the last three decades, referred to as trajectory prediction. In our
days, the vast amounts of data being continuously produced add the big data
dimension to the trajectory prediction problem, which we are trying to tackle
by creating a {\lambda}-Architecture based analytics platform. This platform
performs both batch and stream analytics tasks and then combines them to
perform analytical tasks that cannot be performed by analyzing any of these
layers by itself. The biggest benefit of this platform is its context agnostic
trait, which allows us to use it for any use case, as long as a time-stamped
geolocation stream is provided. The experimental results presented prove that
each part of the {\lambda}-Architecture performs well at certain targets,
making a combination of these parts a necessity in order to improve the overall
accuracy and performance of the platform.
