  Industry 4.0 factories are complex and data-driven. Data is yielded from many
sources, including sensors, PLCs, and other devices, but also from IT, like ERP
or CRM systems. We ask how to collect and process this data in a way, such that
it includes metadata and can be used for industrial analytics or to derive
intelligent support systems. This paper describes a new, query model based
approach, which uses a big data architecture to capture data from various
sources using OPC UA as a foundation. It buffers and preprocesses the
information for the purpose of harmonizing and providing a holistic state space
of a factory, as well as mappings to the current state of a production site.
That information can be made available to multiple processing sinks, decoupled
from the data sources, which enables them to work with the information without
interfering with devices of the production, disturbing the network devices they
are working in, or influencing the production process negatively. Metadata and
connected semantic information is kept throughout the process, allowing to feed
algorithms with meaningful data, so that it can be accessed in its entirety to
perform time series analysis, machine learning or similar evaluations as well
as replaying the data from the buffer for repeatable simulations.
