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.

Algorithms for Massive Datasets (AMD) -- Market-baskets analysis project

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

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.

Stream, parse, manipulate and transform extremly large data ( can be 1 GB or 1TB ) in NodeJS without any process block, memory overflow or bottle neck with peak performance. And also show it in UI with the help of webStreams

This project facilitates the efficient mass registration of products using Rabbit MQ, managing loads exceeding 50,000 products.

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

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

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

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

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

Building a Bloom Filter on English dictionary words

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

word count in Spark

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

University lab exercises with processing big data.