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Framing Apache Spark in life sciences.

Andrea Manconi1, Matteo Gnocchi1, Luciano Milanesi1

  • 1Institute of Biomedical Technologies - National Research Council of Italy, Segrate (Mi), Italy.

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|February 28, 2023
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Summary
This summary is machine-generated.

Life sciences researchers can leverage Apache Spark, a powerful high-performance computing engine, to overcome big data challenges in storing, sharing, and analyzing complex datasets. This technology enables efficient distributed computing for advanced research tasks.

Keywords:
00-0199-00Apache SparkBig dataHPCParallel computing

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput and digital technologies generate massive datasets in life sciences.
  • Researchers face significant challenges in storing, sharing, and analyzing big data.
  • Efficient processing requires advanced distributed computing systems and algorithms.

Purpose of the Study:

  • To review the features of Apache Spark for life sciences research.
  • To help researchers assess Apache Spark's suitability for their big data needs.
  • To guide the adoption of big data technologies in life sciences.

Main Methods:

  • Review of distributed programming frameworks, focusing on Apache Spark.
  • Analysis of Apache Spark's capabilities for large-scale data processing.
  • Exploration of specialized libraries for machine learning, graph computation, and stream processing.

Main Results:

  • Apache Spark offers a powerful engine for efficient large-scale data processing on clusters.
  • It supports flexible algorithms adaptable to data and infrastructure (HPC or cloud).
  • Specialized libraries enhance its utility for machine learning, graph analysis, and real-time data streams.

Conclusions:

  • Apache Spark presents a viable solution for life sciences researchers managing big data.
  • Its features address technical and infrastructural hurdles in data analysis.
  • Researchers can utilize Apache Spark to advance complex computational tasks in their studies.