This is the code repository for the book Programming Big Data Applications, published by World Scientific. It contains the code of exercices proposed in the book.
Talia, D., Trunfio, P., Marozzo, F., Belcastro, L., Cantini, R., & Orsino, A. (2024). Programming Big Data Applications. WORLD SCIENTIFIC (EUROPE). https://doi.org/10.1142/q0444
The book Programming Big Data Applications introduces and discusses models, programming frameworks and algorithms to process and analyze large amounts of data. In particular, the book provides an in-depth description of the properties and mechanisms of the main programming paradigms for Big Data analysis, including MapReduce, workflow, BSP, message passing, and SQL-like.
Through programming examples it also describes the most used frameworks for Big Data analysis like Hadoop, Spark, MPI, Hive, Storm and others. We discuss and compare the different systems by highlighting the main features of each of them, their diffusion (both within their community of developers and users), and their main advantages and disadvantages in implementing Big Data analysis applications.
@book{doi:10.1142/q0444,
author = {Talia, Domenico and Trunfio, Paolo and Marozzo, Fabrizio and Belcastro, Loris and Cantini, Riccardo and
Orsino, Alessio},
title = {Programming Big Data Applications},
publisher = {WORLD SCIENTIFIC (EUROPE)},
year = {2024},
doi = {10.1142/q0444},
URL = {https://www.worldscientific.com/doi/abs/10.1142/q0444},
eprint = {https://www.worldscientific.com/doi/pdf/10.1142/q0444}
}
Users are free to download and use this code. To facilitate usage, a README.md file has been included in the folder of each exercise, providing details about the code and explaining how to run it in the distributed environment. Additionally, each exercise folder includes a bash script, run.sh, that automates the process of building, setting up, and running the example.
A guide on how to run the code of the different exercises is available here: https://bigdataprogramming.github.io/
Feel free to contribute to this GitHub project by reporting any bugs or providing suggestions for further improving the code; your valuable feedback is highly encouraged and appreciated!
© Talia Domenico, Trunfio Paolo, Marozzo Fabrizio, Belcastro Loris, Cantini Riccardo, Orsino Alessio
Licensed under the MIT License.