  Experimental Particle Physics has been at the forefront of analyzing the
world's largest datasets for decades. The HEP community was among the first to
develop suitable software and computing tools for this task. In recent times,
new toolkits and systems for distributed data processing, collectively called
"Big Data" technologies have emerged from industry and open source projects 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 tools, promising a fresh look at analysis of very large datasets
that could potentially reduce the time-to-physics with increased interactivity.
Moreover these new tools are typically actively developed by large communities,
often profiting of industry resources, and under open source licensing. These
factors result in a boost for adoption and maturity of the tools and for the
communities supporting them, at the same time helping in reducing the cost of
ownership for the end-users. In this talk, we are presenting studies of using
Apache Spark for end user data analysis. We are studying the HEP analysis
workflow separated into two thrusts: the reduction of centrally produced
experiment datasets and the end-analysis up to the publication plot. Studying
the first thrust, CMS is working together with CERN openlab and Intel on the
CMS Big Data Reduction Facility. The goal is to reduce 1 PB of official CMS
data to 1 TB of ntuple output for analysis. We are presenting the progress of
this 2-year project with first results of scaling up Spark-based HEP analysis.
Studying the second thrust, we are presenting studies on using Apache Spark for
a CMS Dark Matter physics search, comparing Spark's feasibility, usability and
performance to the ROOT-based analysis.
