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#Referências

  • AMAZON. What is Apache Spark?. Disponível em: <https://aws.amazon.com/big-data/what-is-spark/>. Acesso em: 09 de março de 2022.

  • AMAZON. What is a data lake?. Disponível em: <https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/>. Acesso em: 07 de março de 2022.

  • AMAZON. What is Hadoop?. Disponível em: <https://aws.amazon.com/emr/details/hadoop/what-is-hadoop/>. Acesso em: 09 de março de 2022.
  • ANAND, K.. Can Big Data replace an EDW?. Disponível em: <https://mastechinfotrellis.com/blog/can-big-data-replace-edw. Publicado em: 23 de Julho de 2019. Acesso em: 08 de março de 2022.
  • ARMBRUST, M., GHODSI, A., XIN, R., ZAHARIA, M. Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. 11th Annual Conference on Innovative Data Systems Research. 2021.

  • BARBIERI, C. Governança de Dados: Práticas, conceitos e novos caminhos. Rio de Janeiro: Alta Books, 2019.
  • BEGOLI, B., GOETHERT, I. KNIGHT, K. A Lakehouse Architecture for the Management and Analysis of Heterogeneous Data for Biomedical Research and Mega-biobanks. 2021 IEEE International Conference on Big Data (Big Data). P. 4643-4651. 2021.

  • DAMA INTERNATIONAL. DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition). Denville, NJ, USA. Technics Publications. 2017.
  • DEHGHANI, Z.. How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh. Disponível em: <https://martinfowler.com/articles/data-monolith-to-mesh.html>. Publicado em: 20 de maio de 2019. Acesso em: 15 de março de 2022.
  • GANDOMI, A., HAIDER, M. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management. V. 35, P. 137-144. 2015.
  • KIMBALL, R., ROSS, M. The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence. Indianapolis. Wiley. 2010. 565 p.

  • KIMBALL, R., ROSS, M. The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3nd Edition). Indianapolis. Wiley. 2013. 720 p.

  • PEREIRA, D., OLIVEIRA, P., RODRIGUES, F. Data warehouses in MongoDB vs SQL Server: A comparative analysis of the querie performance. Information Systems and Technologies (CISTI), 10th Iberian Conference. P. 1–7. 2015.
  • INMON, W. H. Building the Data Warehouse. Indianapolis. Wiley. 2005. 428 p.

  • INMON, B. Data Lake architecture: Designing the Data Lake and avoiding the garbage dump. Indianapolis. Technics Publications. 2016. 168 p.

  • ANAND,KHINE, K..P. P., WANG, Z. S. Data lake: a new ideology in big data era. CanITM Web of Conferences, V 17. 2018.
  • MILOSLAVSKAYA, N., TOLSTOY, A. Big Datadata, replacefast andata EDW?and data lake concepts. 7Th annual international conference on biologically inspired cognitive architectures (BICA 2016).. DisponívelNY, em:USA. <https://mastechinfotrellis.com/blog/can-big-data-replace-edw.Procedia PublicadoComputer em:Science. 23V. de88, JulhoP. de1–6. 2019. Acesso em: 08 de março de 2022.2016.

  • MICROSOFT. What is business intelligence?. Disponível em: <https://powerbi.microsoft.com/en-us/what-is-business-intelligence/>. Acesso em: 07 de março de 2022.

  • TABLEAU. Business Intelligence: What It Is, How It Works, Its Importance, Examples, & Tools. Disponível em: <https://www.tableau.com/learn/articles/business-intelligence>. Acesso em: 07 de março de 2022.

  • GARTNER. Business Intelligence (BI) Platforms. Disponível em: <https://www.gartner.com/en/information-technology/glossary/bi-platforms>. Acesso em: 07 de março de 2022.

  • GARTNER. Master Data Management (MDM). Disponível em: <https://www.gartner.com/en/information-technology/glossary/master-data-management-mdm>. Acesso em: 18 de março de 2022.

  • MONGODB. Database Scaling. Disponível em: <https://www.mongodb.com/databases/scaling>. Acesso em: 09 de março de 2022.

  • SAS. Big Data: What is and why it matters. Disponível em: <https://www.sas.com/pt_br/insights/big-data/what-is-big-data.html>. Acesso em: 08 de março de 2022.

  • SAS. Big Data Analytics: What is and why it matters. Disponível em: <https://www.sas.com/pt_br/insights/analytics/big-data-analytics.html>. Acesso em: 09 de março de 2022.

  • AMAZON. What is a data lake?. Disponível em: <https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/>. Acesso em: 07 de março de 2022.

  • AMAZON. What is Hadoop?. Disponível em: <https://aws.amazon.com/emr/details/hadoop/what-is-hadoop/>. Acesso em: 09 de março de 2022.

  • AMAZON. What is Apache Spark?. Disponível em: <https://aws.amazon.com/big-data/what-is-spark/>. Acesso em: 09 de março de 2022.

  • ORACLE. What is Big Data?. Disponível em: <https://www.oracle.com/big-data/what-is-big-data/.> Acesso em: 08 de março de 2022.

  • DEHGHANI, Z.. How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh. Disponível em: <https://martinfowler.com/articles/data-monolith-to-mesh.html>. Publicado em: 20 de maio de 2019. Acesso em: 15 de março de 2022.

  • GOASDUFF, L.. The Best Ways to Organize Your Data Structures. Disponível em: <https://www.gartner.com/smarterwithgartner/the-best-ways-to-organize-your-data-structures>. Publicado em: 20 de junho de 2020. Acesso em: 21 de março de 2022.

  • HARRAB, Y.E. How to differentiate a Data Hub, a Data Lake and a Data Warehouse. Disponível em: <https://www.semarchy.com/blog/how-to-differentiate-a-data-hub-a-data-lake-and-a-data-warehouse/>. Publicado em: 09 de março de 2020. Acesso em: 21 de março de 2022.

  • ORACLE. What is Big Data?. Disponível em: <https://www.oracle.com/big-data/what-is-big-data/.> Acesso em: 08 de março de 2022.
  • OREŠČANIN, D., HLUPIĆ, T. Data Lakehouse - a Novel Step in Analytics Architecture. 44th International Convention on Information, Communication and Electronic Technology (MIPRO).  V. 44, P. 1242-1246. 2021.
  • OUSSOUS, Ahmed et al. Big Data technologies: A survey. Journal of King Saud University - Computer and Information Sciences. V. 30, E. 4, P. 431-448. Outubro de 2018.

  • SALINAS, S.O., LEMUS, A. C. N. Data Warehouse and Big Data Integration. International Journal of Computer Science & Information Technology (IJCSIT). V. 9, N.2 E. 4, P. 1-17. Abril de 2017.

  • GANDOMI, A., HAIDER, M. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management. V. 35, P. 137-144. 2015.

  • PEREIRA, D., OLIVEIRA, P., RODRIGUES, F. Data warehouses in MongoDB vs SQL Server: A comparative analysis of the querie performance. Information Systems and Technologies (CISTI), 10th Iberian Conference. P. 1–7. 2015.

  • KHINE, P. P., WANG, Z. S. Data lake: a new ideology in big data era. ITM Web of Conferences, V 17. 2018.

  • SAWADOGO, P., DARMONT, J. On data lake architectures and metadata management. Journal of Intelligent Information Systems. V. 56. P. 97-120. 2021.

  • MILOSLAVSKAYA, N., TOLSTOY, A. Big data, fast data and data lake concepts. 7Th annual international conference on biologically inspired cognitive architectures (BICA 2016). NY, USA. Procedia Computer Science. V. 88, P. 1–6. 2016.

  • ARMBRUST, M., GHODSI, A., XIN, R., ZAHARIA, M. Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. 11th Annual Conference on Innovative Data Systems Research. 2021.

  • OREŠČANIN, D., HLUPIĆ, T. Data Lakehouse - a Novel Step in Analytics Architecture. 44th International Convention on Information, Communication and Electronic Technology (MIPRO).  V. 44, P. 1242-1246. 2021.

  • BEGOLI, B., GOETHERT, I. KNIGHT, K. A Lakehouse Architecture for the Management and Analysis of Heterogeneous Data for Biomedical Research and Mega-biobanks. 2021 IEEE International Conference on Big Data (Big Data). P. 4643-4651. 2021.

  • BARBIERI, C. Governança de Dados: Práticas, conceitos e novos caminhos. Rio de Janeiro: Alta Books, 2019.

  • DAMA INTERNATIONAL. DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition). Denville, NJ, USA. Technics Publications. 2017.
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