  In high-resolution solar physics, the volume and complexity of photometric,
spectroscopic, and polarimetric ground-based data significantly increased in
the last decade reaching data acquisition rates of terabytes per hour. This is
driven by the desire to capture fast processes on the Sun and by the necessity
for short exposure times "freezing" the atmospheric seeing, thus enabling
post-facto image restoration. Consequently, large-format and high-cadence
detectors are nowadays used in solar observations to facilitate image
restoration. Based on our experience during the "early science" phase with the
1.5-meter GREGOR solar telescope (2014-2015) and the subsequent transition to
routine observations in 2016, we describe data collection and data management
tailored towards image restoration and imaging spectroscopy. We outline our
approaches regarding data processing, analysis, and archiving for two of
GREGOR's post-focus instruments (see http://gregor.aip.de), i.e., the GREGOR
Fabry-Perot Interferometer (GFPI) and the newly installed High-Resolution Fast
Imager (HiFI). The heterogeneous and complex nature of multi-dimensional data
arising from high-resolution solar observations provides an intriguing but also
a challenging example for "big data" in astronomy. The big data challenge has
two aspects: (1) establishing a workflow for publishing the data for the whole
community and beyond and (2) creating a Collaborative Research Environment
(CRE), where computationally intense data and post-processing tools are
co-located and collaborative work is enabled for scientists of multiple
institutes. This requires either collaboration with a data center or frameworks
and databases capable of dealing with huge data sets based on Virtual
Observatory (VO) and other community standards and procedures.
