Apache Superset is a Data Visualization and Data Exploration Platform
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Updated
Apr 15, 2023 - TypeScript
Apache Superset is a Data Visualization and Data Exploration Platform
A curated list of awesome big data frameworks, ressources and other awesomeness.
StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.
Danfo.js is an open source, JavaScript library providing high performance, intuitive, and easy to use data structures for manipulating and processing structured data.
Open source BI for teams that move fast
Integrate Human Supervision into your Platform. For all Training Data Types, Image, Video, 3D, Text, Geo, Audio, Compound, Grid, LLM, GPT, Conversational, and more.
Zui is a powerful desktop application for exploring and working with data. The official front-end to the Zed lake.
Modern columnar data format for ML 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..
Datart is a next generation Data Visualization Open Platform
Business Intelligence (BI) in Python, OLAP
Dremio - the missing link in modern data
TrailDB is an efficient tool for storing and querying series of events
Open Source Self-Hosted Business Intelligence Platform
数据可视化分析平台,自由制作任何您想要的数据看板
(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.
Practical Data Science with Ruby based tools.
Read data from, write data to, and modify the formatting of Google Sheets
ISP Data Pollution to Protect Private Browsing History with Obfuscation
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.
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