# Big Data in HEP: A comprehensive use case study

## TL;DR

This paper evaluates the application of Big Data technologies, specifically Apache Spark, in high-energy physics data analysis, comparing it with traditional methods using CMS LHC data to assess efficiency and practicality.

## Contribution

It presents a case study applying Big Data tools to HEP analysis, demonstrating potential benefits and challenges compared to conventional techniques.

## Key findings

- Big Data tools can process HEP datasets effectively.
- Spark-based analysis offers increased interactivity.
- Comparison highlights advantages and limitations of each approach.

## Abstract

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.