data load tool (dlt) is an open source Python library that makes data loading easy 🛠️

Podium: a framework agnostic Python NLP library for data loading and preprocessing

TypeScript/JavaScript client library for Convex

Lightweight data loading and caching library for android

High-level API for tar-based dataset

Contains all my data science projects.

This repository is a working ETL framework which utilizes user data from Spotify API using ➲Python for Extraction and Transformation ➲SQL for Data Loading and Staging ➲Airflow for Data Orchestration and Monitoring ➲PowerBI for Reporting

An Android library for data load with cache and loading state

Data loading with combined async Rust stream and Python

Syracuse University, Masters of Applied Data Science - IST 722 Data Warehouse

A slick loader to use during your AJAX calls or data processing

An experiment sandbox for Deep Learning Data Loading analysis.

Get the DeepLesion CT Image data set into a GCP Storage Bucket

a website to order pizza and track your orders using React, React router, Tailwind and Redux

This tool rapidly converts loose files scattered within any folder into a consolidated H5 file. This allows for faster read operations with lower memory requirement.

ColumnCore is a high-performance analytical database system designed for beginners or small projects. It supports a rich SQL dialect, runs within the same process as the application, has a vectorized query execution engine, and uses a columnar storage format.

challenges : (React Router data loading, Redux, Redux Toolkit, thunks, Tailwind CSS)

Streamline your data flow with AWS Data Pipelining - a reliable and scalable solution for seamless data ingestion, processing, and storage

`Spltr` is a simple PyTorch-based data loader and splitter. It may be used to load arrays and matrices or Pandas DataFrames and CSV files containing numerical data with subsequent split it into train, test (validation) subsets in the form of PyTorch DataLoader objects.

Homeworks and R projects for the course Foundations of Data Science