Paper Information | SBC BOOKS

Fill in your paper information

Language (*)

Title (*)

Keywords (*)

Abstract (*)

O termo Engenharia de Dados (ED) tem sido utilizado para se referir aos processos de adquirir, organizar e preparar dados para serem consumidos
em análises ou aplicações. Com o surgimento da área de Ciência de Dados, esse termo tem sido usado para englobar o que tradicionalmente era conhecido como gerenciamento de dados. Neste estudo, exploramos a ED no contexto acadêmico e industrial brasileiro, destacando a sua crescente relevância e a necessidade de habilidades relacionadas a ela nos profissionais de computação. Nossa motivação foi percepção de que os avanços de pelo menos uma década na indústria em ED ainda n ̃ao foram adequadamente absorvidos pelo ensino nas universidades. Para esse trabalho, construímos e comparamos duas taxonomias de tópicos, que resultaram, respectivamente, de levantamentos das disciplinas,
bibliografias e ementas relacionadas a ED em universidades brasileiras e junto a empresas de tecnologia do país. Identificamos uma lacuna entre o ensino e mercado, com currículos desatualizados quanto a tópicos considerados relevantes para a indústria contemporânea. Em particular, tópicos sobre a plataformas de dados de alto desempenho, gerência de dados em nuvem e workflow de dados são destacados como grandes necessidades atuais da indústria, mas que são pouco explorados nos currículos. Nosso objetivo é subsidiar mudanças nos currículos que possam contribuir para a formação de profissionais mais qualificados e alinhados às necessidades modernas do mercado.

Pages (*)

File Link

English Information

Title

Keywords

Abstract

The term Data Engineering (DE) has been used to refer to the processes of acquiring, organizing, and preparing data to be consumed in analyses or applications. With the emergence of the field of Data Science, this term has been used to encompass what was traditionally known as data management. In this study, we explore DE in the Brazilian academic and industrial context, highlighting its growing relevance and the need for skills related to it in computing professionals. Our motivation was the perception that advances of at least a decade in the industry in DE have not yet been adequately absorbed by education in universities. For this work, we constructed and compared two taxonomies of topics, which resulted, respectively, from surveys of courses, bibliographies, and syllabuses related to DE in Brazilian universities and alongside technology companies in the country. We identified a gap between education and the market, with outdated curricula regarding topics considered relevant for contemporary industry. In particular, topics on high-performance data platforms, data management in the cloud, and data workflow are highlighted as major current industry needs, but are little explored in the curricula. Our objective is to subsidize changes in curricula that can contribute to the training of more qualified professionals aligned with modern market needs.

(*) To change the order drag the item to the new position.

Authors

#	Name
1	Altigran da Silva(alti@icomp.ufam.edu.br)
2	Tarsis Azevedo(tarsis.azevedo@icomp.ufam.edu.br)

(*) To change the order drag the item to the new position.

Reference

#	Reference
1	Arrieta-Ibarra, I. et al. (2018). Should we treat data as labor? moving beyond ”free”. AEA Papers and Proceedings, 108:38–42.
2	Bie, T. D. et al. (2022). Automating data science. Commun. ACM, 65(3):76–87.
3	Grillenberger, A. and Romeike, R. (2014). Big data - challenges for computer science education. In Proceedingso of the 7th Int. Conf. on Informatics in Schools: Situation, Evolution, and Perspectives, ISSEP, pages 29–40.
4	Grillenberger, A. and Romeike, R. (2017). Key concepts of data management – an empirical approach. In Proceedings of the 17th Koli Calling Int. Conf. on Computing Education Research, page 30–39.
5	Hassan, I. B. and Liu, J. (2019). Embedding data science into computer science education. In IEEE Int. Conf. on Electro Information Technology EIT, pages 367–372.
6	Nazabal, A. et al. (2020). Data engineering for data analytics: A classification of the´ issues, and case studies. CoRR, abs/2004.12929.
7	Reinsel, D. et al. (2018). The digitization of the world - from edge to core.
8	Silva, Y. N. et al. (2014). Integrating big data into the computing curricula. In The 45th ACM Technical Symposium on Computer Science Education, SIGCSE, pages 139–144.
9	Stonebraker, M. and C¸etintemel, U. (2005). ”one size fits all”: An idea whose time has come and gone. In Proceedings of the 21st International Conference on Data Engineering, ICDE, pages 2–11.
10	Zaharia, M. et al. (2021). Lakehouse: A new generation of open platforms that unify data warehousing and advanced analytics. In 11th

Paper Registration

Fill in your paper information

English Information

Authors

Reference