  Experimental Particle Physics has been at the forefront of analyzing the
worlds largest datasets for decades. The HEP community was the first to develop
suitable software and computing tools for this task. In recent times, new
toolkits and systems collectively called Big Data technologies have emerged to
support the analysis of Petabyte and Exabyte datasets in industry. While the
principles of data analysis in HEP have not changed (filtering and transforming
experiment-specific data formats), these new technologies use different
approaches and promise a fresh look at analysis of very large datasets and
could potentially reduce the time-to-physics with increased interactivity. In
this talk, we present an active LHC Run 2 analysis, searching for dark matter
with the CMS detector, as a testbed for Big Data technologies. We directly
compare the traditional NTuple-based analysis with an equivalent analysis using
Apache Spark on the Hadoop ecosystem and beyond. In both cases, we start the
analysis with the official experiment data formats and produce publication
physics plots. We will discuss advantages and disadvantages of each approach
and give an outlook on further studies needed.
