There are 51 repositories under data-processing topic.
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
A collection of handy Bash One-Liners and terminal tricks for data processing and Linux system maintenance.
Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
Select, put and delete data from JSON, TOML, YAML, XML and CSV files with a single tool. Supports conversion between formats and can be used as a Go package.
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷
A lightweight data processing framework built on DuckDB and 3FS.
A light-weight, flexible, and expressive statistical data testing library
Data transformation framework for AI. Ultra performant, with incremental processing. 🌟 Star if you like it!
Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/
Extract Transform Load for Python 3.5+
Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017
Scalable data pre processing and curation toolkit for LLMs
Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.
Command line tool to download and extract data from HTML/XML pages or JSON-APIs, using CSS, XPath 3.0, XQuery 3.0, JSONiq or pattern matching. It can also create new or transformed XML/HTML/JSON documents.
Integrating the Best of TF into PyTorch, for Machine Learning, Natural Language Processing, and Text Generation. This is part of the CASL project: http://casl-project.ai/
HStreamDB is an open-source, cloud-native streaming database for IoT and beyond. Modernize your data stack for real-time applications.
All-in-one text de-duplication
A tool that uses advanced Monte Carlo simulations and Turbit parallel processing to create possible Bitcoin prediction scenarios.
A list about Apache Kafka
Machine Learning notebooks for refreshing concepts.
Harmonious distributed data analysis in Rust.
📈 PatternPy: A Python package revolutionizing trading analysis with high-speed pattern recognition, leveraging Pandas & Numpy. Effortlessly spot Head & Shoulders, Tops & Bottoms, Supports & Resistances. For experts & beginners. #TradingMadeEasy 🔥
Deal with bad samples in your dataset dynamically, use Transforms as Filters, and more!