json-processing

Unified querying, transformation, and modification of JSON, TOML, YAML, XML, INI, HCL, KDL and CSV.

An interactive front-end toolkit simplifying JSON, YAML, TOML, and XML data. Visualize, edit, and perform real-time data manipulations. Empower your data-driven apps with user-friendly data processing and interactive visualization.

Java API for JSON Processing (JSON-P)

ComfyUI custom nodes for DeepSeek, Qwen, GPT, and other OpenAI-compatible LLM APIs, with tools for chat, translation, vision, and JSON workflows.

JSON processor for web use (jq port)

Command line processor for YAML/JSON files using Jsonnet

Query JSON data using SQL - Filter, aggregate, sort, and project without a database.

Data-Splitter is a Python script designed to split a large CSV file containing data into three different formats: JSON, a database table, and another CSV file. The script ensures a random distribution of data across the three output formats based on custom-defined ratios.

Scripts to process the RICO dataset, used for a CHI '20 paper.

Modern JSON processor with readable Kotlin syntax.

Automates VTU internship diary workflow by extracting entries, normalizing data, and generating structured PDF reports directly in the browser.

Demonstrates how to validate JSON documents against JSON schemas using Justify.

An automated system for processing customer orders from JSON input, validating data, and synchronizing with a database while managing errors and outputs.

Simple JSON library for Java

LLM-Powered Smart Task Planner API. Generates structured task breakdowns, dependencies, and timelines from a high-level goal

Comprehensive product analysis and recommendation system with JSON data processing, visual analytics, and machine learning.

Very simple JSON library for simstr

JSON 포멧(string, []byte)의 데이터를 golang 의 특정 struct 또는 map[string]interface{} 형태로 변환하여 동적으로 속성을 가공 가능하도록 도와줍니다.

A Rust-based GUI desktop application for data engineering tasks that provides a visual interface for loading, filtering, processing, and analyzing CSV/JSON files with integrated machine learning capabilities.

Midterm and Final assignments of the course "Big Data Mining (52002)" at The Hebrew University of Jerusalem, in the Department of Statistics and Data Science. Focuses on analyzing massive datasets using Python, SQL, cloud computing, and network analysis. Includes project guidelines for scalable data mining techniques and distributed computing.