Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀

Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, ModelScope, etc

The official repository for ROOT: analyzing, storing and visualizing big data, scientifically

This tutorial playlist covers data structures and algorithms in python. Every tutorial has theory behind data structure or an algorithm, BIG O Complexity analysis and exercises that you can practice on.

Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code. Tuplex has similar Python APIs to Apache Spark or Dask, but rather than invoking the Python interpreter, Tuplex generates optimized LLVM bytecode for the given pipeline and input data set.

High Performance Software-Defined Object Storage for Big Data and AI, that supports Amazon S3 and Openstack Swift

Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

Google BigQuery connector for pandas

A Python package develop for transportation spatio-temporal big data processing, analysis and visualization.

Real Time Big Data / IoT Machine Learning (Model Training and Inference) with HiveMQ (MQTT), TensorFlow IO and Apache Kafka - no additional data store like S3, HDFS or Spark required

Download numerical weather prediction datasets (HRRR, RAP, GFS, IFS, etc.) from NOMADS, NODD partners (Amazon, Google, Microsoft), ECMWF open data, and the University of Utah Pando Archive System.

An open source platform for managing and analyzing biomedical big data

A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀

Downloads for my Safari Online Learning live training course Python Data Science Full Throttle: Introductory Artificial Intelligence (AI), Big Data and Cloud Case Studies

Big Data Processing Framework - Unified Data API or SQL on Any Storage

A complete example of a big data application using : Kubernetes (kops/aws), Apache Spark SQL/Streaming/MLib, Apache Flink, Scala, Python, Apache Kafka, Apache Hbase, Apache Parquet, Apache Avro, Apache Storm, Twitter Api, MongoDB, NodeJS, Angular, GraphQL

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Research project aimed to classify the best stock research posts from r/WallStreetBets for you. 😏

AI Flow is an open source framework that bridges big data and artificial intelligence.

Example Kubernetes app that shows how to build a 'pipeline' to stream data into BigQuery. Uses Redis or Google Cloud PubSub