PolarDB-X is a cloud native distributed SQL Database designed for high concurrency, massive storage, complex querying scenarios.

Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python

PolarDB-X is a cloud native distributed SQL Database designed for high concurrency, massive storage, complex querying scenarios.

TF-Package: Multiple-Input Multiple-Output Keras Data-Generator for massive and complex datasets

Command line tool to quickly generate a lot of files in a lot of directories

The project is based on the analysis of the "IBM Transactions for Anti Money Laundering" dataset published on Kaggle. The task is to implement a model which predicts whether or not a transaction is illicit, using the attribute "Is Laundering" as a label to be predicted.

Building a Bloom Filter on English dictionary words

This repository contains a LaTeX file that generates a PDF document comprising comprehensive notes for the course "Algorithms for Massive Datasets"

gipa -- compression/decompression tool to package compress and encode massive archive files with floating-point data

Building PageRank algorithm on Web Graph around Stanford.edu using NetworkX python library

Series of SQL exercise working with databases, using Google BigQuery to scale to massive datasets taught by educators in Kaggle.com

Permite abrir e manipular arquivos massivos de texto/dados cujo seria impossivel abrir em um computador, por exemplo um arquivo de texto de +20gb, permite manipular o arquivo pegando apenas as linhas necessárias sem travar o computador por falta de memória.

Building node2vec algorithm

📺 Content Recommendation System for the Netflix Prize Challenge with Collaborative Filtering.

Calculate statistical measures of one column in big data Datasets with these simply Hadoop Application

word count in Spark

Map Reduce program to suggest new friends based on count of mutual friends

Training the MASSIVE dataset by Amazon(english-US, German-DE and Swahili-KE)

Lab assignments for the Analysis of Massive Data Sets course @ FER, University of Zagreb