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Authors: Aniss Qostal 1 ; Aniss Moumen 2 and Younes Lakhrissi 3

Affiliations: 1 Intelligent Systems, Georesources and Renewable Energies Laboratory (SIGER IN FRENCH), Sidi Mohamed Ben Abdellah University, FST Fez, Morocco ; 2 Laboratory of Engineering Sciences, National School of Applied Sciences, Ibn Tofaïl University, Kenitra, Morocco ; 3 Intelligent Systems, Georesources and Renewable Energies Laboratory, Sidi Mohamed Ben Abdellah University, FST Fez, Fez, Morocco

Keyword(s): big data, data analytics, Hadoop, Spark, employability.

Abstract: The application of big data and data analytics has reached all aspects of life, from entertainment to scientific research and commercial production. Mainly by taking advantage of the explosion of data at an unprecedented rate attain the level of exabytes per day. On the other hand, it benefits from the sophisticated analytics approaches that have been given new manners to translate the raw data into solutions and even into predictions for complicated situations. This paper aims to discover the application of big data, data analytics and technical architectures based on the Hadoop and Spark ecosystems to build employability solutions. Beginning with a literature review of previous works and proposed solutions to draw a roadmap towards new approaches and enhance the recruitment process for youth people.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Qostal, A.; Moumen, A. and Lakhrissi, Y. (2022). Big Data, Hadoop and Spark for Employability: Proposal Architecture. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML; ISBN 978-989-758-559-3, SciTePress, pages 260-266. DOI: 10.5220/0010732200003101

@conference{bml22,
author={Aniss Qostal. and Aniss Moumen. and Younes Lakhrissi.},
title={Big Data, Hadoop and Spark for Employability: Proposal Architecture},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML},
year={2022},
pages={260-266},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010732200003101},
isbn={978-989-758-559-3},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - BML
TI - Big Data, Hadoop and Spark for Employability: Proposal Architecture
SN - 978-989-758-559-3
AU - Qostal, A.
AU - Moumen, A.
AU - Lakhrissi, Y.
PY - 2022
SP - 260
EP - 266
DO - 10.5220/0010732200003101
PB - SciTePress

- Science and Technology Publications, Lda.
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