Big Data

Databases
Data Structures
SQL

1. Presentation of the theoretical foundations underlying Big Data processing

2. Theoretical exposition of Big Data processing technologies

3. Resolution of exercises/application examples

4. Utilization and exploration of tools such as MongoDB, SQL Server, and Power BI for data analysis

At the end of successfully completing the course unit, students should be able to:

1. Understand and explain the principles and concepts of storing, processing, and analyzing large volumes of data (Big Data).
2. Identify and apply the concepts and techniques of storing, processing, and analyzing data (Big Data) to practical problems.
3. Work with tools for data analysis.

1. Introduction to Big Data
– New generations of databases
– What is Big Data?
– Characteristics of Big Data;
– Examples from the Big Data domain

2. Big Data in Sustainable and Smart Cities
– Sustainable and Smart Cities: reference models and challenges
– Data analysis
– IoT for Sustainable and Smart Cities of the future
– Impact, challenges, and opportunities of Big Data in cities.
– Applications of Big Data for smart cities.

3. Big Data Processing
– Limitations of traditional systems
– Data storage systems (Relational and NoSQL databases)
– Big Data platforms
– Tools and techniques for data processing (Microsoft Power BI and Microsoft SQL Server)

NAO

• Lim, C., Kim, K.-J., & Maglio, P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations. Cities, 82, 86–99. https://doi.org/10.1016/j.cities.2018.04.011.
• Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. https://doi.org/10.1016/j.future.2018.06.046.
• Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E., & Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management, 36(5), 748–758. https://doi.org/10.1016/j.ijinfomgt.2016.05.002.
• Santos, M. Y. (2020). BIG DATA: Concepts, Warehousing, and Analytics. River Publishers.
• Erl, T., Khattak, W., & Buhler, P. (2016). Big Data Fundamentals: Concepts, Drivers & Techniques. Prentice Hall ; Vancouver, Bc.
• Bahga, A., & Madisetti, V. (2016). Big Data Science & Analytics: A Hands-on Approach. Arshdeep Bahga & Vijay Madisetti.
• Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data Analysis. O’Reilly.
• Bengfort, B., & Kim, J. (2016). Data Analytics with Hadoop. O’Reilly Media, Inc.
• White, T. (2015). Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale. Beijing O’reilly Media.
• Ramos, I., & Santos, M. Y. (2017). Business Intelligence: Da Informação ao Conhecimento (3ª ed.). FCA.
• Magalhães, A. (2017). Business Intelligence no SQL Server. FC.