Homogeneous and/or heterogeneous architecture performance estimation for big data
Article Sidebar
Main Article Content
Abstract
The fourth industrial revolution interacts with other aspects such as Cloud Computing, Internet of Things, Data Science, Data Engineering, Artificial Intelligence with Machine Learning. Because it is increasingly inevitable, not to transform real world data into digital data such as: Texts, audio, images, videos, etc., for its treatment and optimal decision making, in the context that is required. Consequently, from the aforementioned technologies comes the term Big Data, which underlies structured, semi-structured and unstructured terms and all of this has to be processed, administered and managed using ETL, Power BI Desktop and Power BI cloud service techniques. , Looker Studio, Hadoop Architecture for Big Data, ASF-Apache Software Foundation, provides support to the Hadoop ecosystem, to create, design and apply as research, application and distribution in Universities, SMEs and Companies and industries respectively, as well as multinationals Companies such as Oracle cloud, IBM, Amazon, Azure and Google, are based on this open source technology – Hadoop open source
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Dong, Z. (2022) Research of Big Data Information Mining and Analysis: Technology Based on Hadoop Technology, International Conference on Big Data, Information and Computer Network (BDICN), Sanya, China.
Kumar, S., & Singh, M. (2019). A novel clustering technique for efficient clustering of big data in Hadoop Ecosystem. Big Data Mining and Analytics, 2 (4), 240-247 DOI: 10.26599/ BDMA.2018.9020037.
Li, K., Jiang, H., Yang, L., & Cuzzocrea, A. (2015). Big data. Algorithms, Analytics, and Applications. https://doi.org/10.1201/b18050.
Mohanty, H., Bhuyan P., & Chenthati D. (2015) Big Data. https://link.springer.com/book/10.1007/978-81-322-2494-5.
Serrano, J. (2014). Big data y analítica web. Estudiar las corrientes y pescar en un océano de datos. Profesional de la Información, 23(6), 561–566. https://doi.org/10.3145/epi.2014. nov.01.
Shah, A., & Padole, M. (2018). Load Balancing through Block Rearrangement Policy for Hadoop Heterogeneous Cluster. International Conference on Advances in Computing, Communications, and Informatics (ICACCI), Bangalore, India.
Sharma, M., & Kaur, J. (2019). A Comparative Study of Big Data Processing: Hadoop vs. Spark. 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.