Apache Superset is a Data Visualization and Data Exploration Platform

A curated list of awesome big data frameworks, ressources and other awesomeness.

Danfo.js is an open source, JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.

Self-serve BI to 10x your data team ⚡️

Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..

The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.

Datart is a next generation Data Visualization Open Platform

Zui is a powerful desktop application for exploring and working with data. The official front-end to the Zed lake.

Open-source, browser-local data exploration using DuckDB-Wasm and PRQL

数据可视化分析平台，自由制作任何您想要的数据看板

Dremio - the missing link in modern data

Business Intelligence (BI) in Python, OLAP

TrailDB is an efficient tool for storing and querying series of events

Open Source Self-Hosted Business Intelligence Platform

Practical Data Science with Ruby based tools.

(Finished) Geek Time Data Analysis Practical 45 Lecture - Detailed notes containing markdown images mind map code data can be read directly code test

Abixen Platform is a microservices based software platform for building enterprise applications delivering functionalities through creating particular microservices and integrating by provided CMS.

google data analytics professional certificate

Read data from, write data to, and modify the formatting of Google Sheets

ARX is a comprehensive open source data anonymization tool aiming to provide scalability and usability. It supports various anonymization techniques, methods for analyzing data quality and re-identification risks and it supports well-known privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy.