Leveraging Big Data and Data Lakes for Advanced Data Science, Challenges and Opportunities

EasyChair Preprint 14996

Abstract

The exponential growth of data in recent years has necessitated the development of new approaches to manage, store, and analyze large datasets effectively. Data lakes have emerged as a critical component in big data architectures, offering a flexible and scalable solution for storing vast amounts of structured, semi-structured, and unstructured data. This paper explores the integration of data lakes with data science methodologies to unlock the full potential of big data. We discuss the architecture of data lakes, their role in data science workflows, and the challenges associated with managing and analyzing data at scale. A case study on implementing a data lake for a large retail organization is presented, demonstrating how data lakes can enhance data science capabilities by enabling real-time analytics, machine learning, and predictive modeling. The results highlight the importance of effective data governance, metadata management, and data quality assurance in maximizing the value derived from big data in a data lake environment.

Keyphrases: Big Data, Data Governance, Data Lakes, Data Science, machine learning, metadata management, predictive modeling, real-time analytics

@booklet{EasyChair:14996,
  author    = {Liam O'Connor},
  title     = {Leveraging Big Data and Data Lakes for Advanced Data Science, Challenges and Opportunities},
  howpublished = {EasyChair Preprint 14996},
  year      = {EasyChair, 2024}}