awesome-repositories.com
Blog
awesome-repositories.com

Descoperă cele mai bune repository-uri open source cu căutare AI.

ExploreazăCăutări recomandateAlternative open-sourceSoftware self-hostedBlogHartă site
ProiectDespreCum realizăm clasamentulPresăServer MCP
LegalConfidențialitateTermeni
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

20 repository-uri

Awesome GitHub RepositoriesData Loading

Mechanisms for importing JSON objects into an editor for modification.

Distinct from JSON Data Ingestion: Focuses on loading data into a UI editor rather than database ingestion

Explore 20 awesome GitHub repositories matching data & databases · Data Loading. Refine with filters or upvote what's useful.

Awesome Data Loading GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • modood/administrative-divisions-of-chinaAvatar modood

    modood/Administrative-divisions-of-China

    20,829Vezi pe GitHub↗

    This project provides a structured dataset of the administrative divisions of China, covering five levels from provinces down to villages. It delivers this geographical information in a standardized JSON format designed for data exchange and integration. The dataset is organized as a hierarchical source specifically for building cascading address selectors and region pickers. It uses linked data to enable sequential filtering from higher-level provinces down to village-level boundaries. The project covers geographic data management and regional data normalization. It provides the necessary m

    Allows loading structured provincial-to-village datasets to populate multi-level address selectors.

    JavaScriptaddressadministrative-divisionsarea
    Vezi pe GitHub↗20,829
  • josdejong/jsoneditorAvatar josdejong

    josdejong/jsoneditor

    12,247Vezi pe GitHub↗

    jsoneditor is a web-based JSON editor component designed for viewing, editing, and formatting structured data. It provides a user interface for managing JSON through multiple rendering modes, including tree, form, and code views. The project is distinguished by its ability to process and visualize exceptionally large datasets, utilizing virtualized memory management to handle JSON files up to 500 MiB without crashing the browser. It also includes a specialized syntax repair tool to convert malformed text into valid JSON and a data transformer for filtering, sorting, and reshaping documents vi

    Provides capabilities to load JSON objects into the editor for display and modification.

    JavaScript
    Vezi pe GitHub↗12,247
  • rawgraphs/rawgraphs-appAvatar rawgraphs

    rawgraphs/rawgraphs-app

    8,994Vezi pe GitHub↗

    This project is a client-side data visualization tool and vector graphics generator that transforms tabular data into customizable SVG graphics. It functions as a browser-based visualization engine that processes datasets locally, ensuring that sensitive information is not transmitted to a server. The platform utilizes a modular layout engine to render diverse chart types, including hierarchical and temporal visualizations. It supports the definition and integration of custom layout extensions to expand the variety of available visualization types. The system provides capabilities for import

    Provides built-in loaders to import CSV and TSV files into tabular structures for visualization.

    JavaScriptd3jsdatadata-visualization
    Vezi pe GitHub↗8,994
  • apache/datafusionAvatar apache

    apache/datafusion

    8,908Vezi pe GitHub↗

    Apache DataFusion is an extensible, columnar SQL query engine that runs embedded within a host application without requiring a separate server process. It processes data in columnar batches using Apache Arrow for memory-efficient analytics, and can scale analytic workloads across multiple nodes for parallel execution. The engine supports both SQL and DataFrame queries through a modular, streaming architecture that allows custom operators, data sources, functions, and optimizer rules. The engine distinguishes itself through its modular extension framework, which enables building custom query e

    Loads data from Parquet, Avro, and compressed formats directly into Arrow columnar memory for analysis.

    Rustarrowbig-datadataframe
    Vezi pe GitHub↗8,908
  • northwoodssoftware/gojsAvatar NorthwoodsSoftware

    NorthwoodsSoftware/GoJS

    8,447Vezi pe GitHub↗

    GoJS is a JavaScript diagramming library and canvas-based visualization engine used to build interactive flowcharts, organizational charts, and network diagrams. It functions as a data-driven framework that binds JavaScript data models to visual elements, enabling bidirectional synchronization between the underlying data and the graphical representation. The library features a comprehensive graph layout engine capable of automatically arranging nodes into trees, grids, circles, or force-directed layouts. It distinguishes itself through a template-based system for generating visual parts and a

    Automatically loads structured JSON objects or arrays into the diagram model.

    HTMLcanvaschartcharts
    Vezi pe GitHub↗8,447
  • bytedance/flowgram.aiAvatar bytedance

    bytedance/flowgram.ai

    8,146Vezi pe GitHub↗

    Flowgram.ai is a workflow development framework for building AI workflow platforms. It provides a visual drag-and-drop canvas for constructing workflows, an Entity-Component-System (ECS) based document model for structuring workflow nodes as a tree, and a node-based form engine for managing configuration forms with built-in rendering, validation, side effects, and error handling. The framework also includes a workflow execution engine that parses directed graph workflows and runs nodes step by step with state tracking and array iteration. The framework distinguishes itself through a layered r

    Workflow builder loads workflow data into the editor from a JSON object or external source, supporting initial load and dynamic reload.

    TypeScriptaiautomationcoze
    Vezi pe GitHub↗8,146
  • olifolkerd/tabulatorAvatar olifolkerd

    olifolkerd/tabulator

    7,550Vezi pe GitHub↗

    Tabulator is an interactive data table library and virtual DOM data grid used to create high-performance tables from JSON or arrays. It functions as a hierarchical data viewer and a spreadsheet interface component, capable of rendering thousands of records efficiently through viewport-based virtualization and progressive loading. The library distinguishes itself by providing a full spreadsheet interface mode with multi-sheet management, cell range selection, and bulk copy-paste capabilities. It supports complex data architectures, including nested data field mapping, expandable tree structure

    Populates the interactive table using JavaScript arrays of objects or JSON data during initialization.

    JavaScriptajaxcdnjsdata
    Vezi pe GitHub↗7,550
  • hospitalrun/hospitalrun-frontendAvatar HospitalRun

    HospitalRun/hospitalrun-frontend

    6,888Vezi pe GitHub↗

    HospitalRun Frontend is an offline-first progressive web application designed for hospital information system administration, enabling healthcare facilities to manage patient records, appointments, and clinical workflows through a web-based interface. The application stores patient data locally in the browser's IndexedDB database, allowing full functionality without a persistent internet connection, and synchronizes changes with the backend server via RESTful API calls when connectivity is restored. The system implements role-based access control routing that restricts navigation and feature

    Loads a pre-built dataset from a file to populate the database with test records for evaluation or training.

    TypeScriptcouchdbemrfrontend
    Vezi pe GitHub↗6,888
  • goldendict/goldendictAvatar goldendict

    goldendict/goldendict

    6,616Vezi pe GitHub↗

    GoldenDict is an offline and online dictionary reader that retrieves word definitions from local files and web sources, displaying entries with full formatting, images, and hyperlinks through an embedded WebKit rendering engine. It reads dictionary files in Babylon, StarDict, Dictd, and ABBYY Lingvo formats without requiring conversion, and supports querying arbitrary websites via user-defined URL templates. The application integrates with the system through global hotkeys and clipboard monitoring, allowing users to trigger lookups or translate selected text in any other application without m

    Reads dictionary files in Babylon, StarDict, Dictd, and ABBYY Lingvo formats without conversion.

    C++
    Vezi pe GitHub↗6,616
  • gephi/gephiAvatar gephi

    gephi/gephi

    6,536Vezi pe GitHub↗

    Gephi is an open-source desktop application for visualizing and analyzing large-scale network graphs. It provides an interactive platform for exploring complex relational data, combining hardware-accelerated rendering with real-time layout controls and a plugin-based modular architecture. The platform distinguishes itself through its ability to handle networks of up to 100,000 nodes and 1,000,000 edges using a custom OpenGL rendering engine, enabling smooth real-time interaction. It includes a force-directed layout engine with real-time adjustment, a dynamic filter pipeline for selecting node

    Load a curated graph dataset from the project's collection to start exploring network data immediately.

    Java
    Vezi pe GitHub↗6,536
  • deepchem/deepchemAvatar deepchem

    deepchem/deepchem

    6,545Vezi pe GitHub↗

    DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data, serving as a comprehensive toolkit for drug discovery and materials science. At its core, it provides a featurizer-pipeline abstraction that converts raw molecular data into numerical representations, including graph-based molecular structures, SMILES tokenization vocabularies, and disk-sharded dataset persistence for handling large-scale data that exceeds RAM capacity. The framework distinguishes itself through integrated molecular docking workflows that automate pocket detecti

    Provides a utility for reading line-delimited JSON records and featurizing them into a Dataset.

    Pythonbiologydeep-learningdrug-discovery
    Vezi pe GitHub↗6,545
  • dimitri/pgloaderAvatar dimitri

    dimitri/pgloader

    6,295Vezi pe GitHub↗

    pgloader is a command-line tool that automates the migration of data and schema from various source databases and file formats into PostgreSQL. It combines schema discovery, parallel data pipelines, and type casting into a single, declarative workflow, using PostgreSQL's COPY protocol for high-throughput bulk loading. The tool distinguishes itself by compiling a dedicated command language into concurrent reader-writer pipelines that handle schema introspection, data transformation, and error-resilient batch processing. It supports migrating entire databases from MySQL, MS SQL, SQLite, and Pos

    Reads data from CSV, fixed-width, dBase, and IBM IXF files, transforming it on the fly.

    Common Lispclozure-clcommon-lispcsv
    Vezi pe GitHub↗6,295
  • nvidia/isaac-gr00tAvatar NVIDIA

    NVIDIA/Isaac-GR00T

    6,222Vezi pe GitHub↗

    Decodes and augments images, videos, and speech to reduce data access latency and training time.

    Jupyter Notebook
    Vezi pe GitHub↗6,222
  • nvidia/daliAvatar NVIDIA

    NVIDIA/DALI

    5,713Vezi pe GitHub↗

    NVIDIA DALI is a GPU-accelerated data loading and preprocessing library designed for deep learning workflows. It constructs high-performance data pipelines that offload decoding, augmentation, and normalization to the GPU, eliminating CPU bottlenecks in training and inference. The library reads data from multiple storage formats and streams it directly into GPU memory, with support for multi-GPU execution to scale throughput across large-scale workloads. DALI distinguishes itself by enabling data pipelines to be built once and executed across multiple deep learning frameworks without code cha

    Reads from multiple storage formats and streams data directly into GPU memory for deep learning workloads.

    C++audio-processingdata-augmentationdata-processing
    Vezi pe GitHub↗5,713
  • biolab/orange3Avatar biolab

    biolab/orange3

    5,635Vezi pe GitHub↗

    Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The

    Loads tabular data from CSV, TSV, Excel, and pickle files with automatic column type interpretation.

    Python
    Vezi pe GitHub↗5,635
  • farm-fe/farmAvatar farm-fe

    farm-fe/farm

    5,580Vezi pe GitHub↗

    Farm is a Rust-based web build tool and development server that compiles JavaScript, TypeScript, CSS, HTML, and static assets into optimized bundles. It uses a module-graph-based bundling approach with persistent module-level caching, enabling near-instant builds and sub-20ms hot module replacement during development. The tool processes assets based on file extensions, handling CSS, Sass, Less, PostCSS, HTML, and images as first-class modules without requiring JavaScript transformation. Farm distinguishes itself through its Vite-compatible plugin system, accepting Vite, Rollup, and Unplugin p

    Converts comma- and tab-separated value files into importable JavaScript modules.

    Rustbuild-toolbundlercompiler
    Vezi pe GitHub↗5,580
  • awslabs/gluontsAvatar awslabs

    awslabs/gluonts

    5,199Vezi pe GitHub↗

    GluonTS este o bibliotecă de serii temporale probabilistice și un framework de prognoză prin deep learning. Oferă un toolkit pentru construirea, antrenarea și evaluarea arhitecturilor de rețele neuronale care prezic valori viitoare ca distribuții de probabilitate pentru a cuantifica incertitudinea. Proiectul se distinge prin suportul pentru prognoza zero-shot și integrarea unor abordări de modelare diverse, incluzând rețele neuronale probabilistice profunde și wrapper-e pentru biblioteci statistice externe precum Prophet și R forecast. Implementează primitive arhitecturale specializate precum convoluțiile cauzale și rețelele reziduale inversabile pentru a preveni scurgerea informațiilor și a mapa reprezentările latente în distribuții de probabilitate valide. Framework-ul acoperă o suprafață cuprinzătoare de inginerie a datelor, incluzând scalarea seriilor temporale, transformări bijective și modelare ierarhică. Utilizează Apache Arrow și Parquet pentru streaming-ul seturilor de date de înaltă performanță și gestionarea accesului aleatoriu. Pentru evaluarea modelului, include o suită de evaluare pentru măsurarea acurateței prognozei și a acoperirii probabilistice folosind metrici precum quantile loss și continuous rank probability scores. Biblioteca suportă implementarea modelului prin integrarea cu Amazon SageMaker.

    Implements an interface for loading data from columnar formats like Parquet and Arrow with automatic format detection.

    Pythonartificial-intelligenceawsdata-science
    Vezi pe GitHub↗5,199
  • unlayer/react-email-editorAvatar unlayer

    unlayer/react-email-editor

    5,086Vezi pe GitHub↗

    React Email Editor is a drag-and-drop visual builder for creating responsive email templates, built as a React embeddable component. It also serves as an AI-powered email designer, a collaborative email design tool, and a React component library for composing emails programmatically with JSX. The editor represents designs as structured JSON and supports multi-format rendering for email clients, web pages, and PDF. What distinguishes this editor is its deep AI integration: users can generate full email templates from natural language, rewrite text with chosen intent, produce multiple text vari

    Imports design JSON into the editor for resuming editing from saved states.

    TypeScriptbuilderdrag-and-dropemail
    Vezi pe GitHub↗5,086
  • assemble/assembleAvatar assemble

    assemble/assemble

    4,258Vezi pe GitHub↗

    Assemble este un generator de site-uri statice și un sistem de conducte de construcție (build pipeline) care compilează markdown, șabloane și date în fișiere HTML statice. Acesta funcționează ca un convertor markdown-la-HTML și un transformator de formate de date capabil să mute conținutul între formatele JSON, YAML, XML, PLIST și CSV. Proiectul dispune de un proces de construcție bazat pe conducte, unde utilizatorii pot defini secvențe ordonate de transformări de date și pași de procesare a fișierelor. Include instrumente de scheletare a proiectului pentru a inițializa structuri de directoare și fișiere de configurare din șabloane predefinite. Sistemul gestionează conținutul prin filtrare bazată pe colecții și imbricarea ierarhică a layout-urilor, permițând organizarea paginilor pe etichete și categorii. Suportă motoare de șabloane pluggable, funcții helper personalizabile și injectarea de metadate YAML front-matter pentru a controla logica de randare. Setul de instrumente oferă, de asemenea, utilitare pentru compilarea foilor de stil LESS, gestionarea permalink-urilor site-ului și monitorizarea fișierelor pentru modificări, pentru a declanșa sarcini de construcție automatizate.

    Imports content from JSON files, YAML files, and front-matter to make values available to templates.

    CSSassembleblog-enginebuild
    Vezi pe GitHub↗4,258
  • miek/inspectrumAvatar miek

    miek/inspectrum

    2,466Vezi pe GitHub↗

    Reads complex and real sample files in common SDR formats including SigMF, GNU Radio, BladeRF, HackRF, and RTL-SDR.

    C++dspsdr
    Vezi pe GitHub↗2,466
  1. Home
  2. Data & Databases
  3. JSON Editors
  4. Data Loading

Explorează sub-etichetele

  • Custom Data Loading HandlersMechanisms for intercepting and customizing data streams during the model ingestion process. **Distinct from Data Loading:** Distinct from general JSON loading: focuses on intercepting raw geometry streams for custom processing.
  • DBF File LoadingLoads data from dBase DBF files into PostgreSQL with automatic table creation and type casting. **Distinct from Data Loading:** Distinct from Data Loading: specifically targets the legacy dBase file format for database ingestion.
  • Error-Resilient LoadingSaving rows rejected by the database to a reject file and continuing to load the remaining good data. **Distinct from Data Loading:** Distinct from Data Loading: focuses on error resilience and continuation, not general data loading.
  • Fixed-Width File LoadersLoads data from fixed-width text files where each column occupies a defined character range. **Distinct from Data Loading:** Distinct from Data Loading: focuses on fixed-width column parsing rather than general JSON or database data loading.
  • GPU-Accelerated Data LoadersDecodes and augments images, videos, and speech on the GPU to reduce data access latency and training time. **Distinct from Data Loading:** Distinct from Data Loading: focuses on GPU-accelerated decoding and augmentation for ML training pipelines, not loading JSON into a UI editor.
  • GeographicMechanisms for importing geographical administrative datasets into application memory or UI components. **Distinct from Data Loading:** Specializes JSON loading for the specific purpose of populating geographic selectors.
  • IXF File LoadingReads IBM IXF data files and loads their contents into PostgreSQL tables using the COPY command. **Distinct from Data Loading:** Distinct from Data Loading: specifically targets the IBM IXF binary exchange format for database loading.
  • Inline and StdinReads COPY-formatted data directly from within the command file or from standard input. **Distinct from Data Loading:** Distinct from Data Loading: focuses on loading data from inline sources or stdin, not general data loading.
  • Line-Delimited JSON LoadersReads line-delimited JSON records, featurizes a specified field, and writes the processed data into a Dataset. **Distinct from Data Loading:** Distinct from Data Loading: focuses on line-delimited JSON records with featurization, not general JSON object loading into an editor.
  • Multi-FormatImporting data from multiple serialization formats like JSON, YAML, and front-matter into a single template context. **Distinct from Multi-Format File Loading:** Focuses on importing data for template use rather than loading data into a database via COPY protocols.
  • Multi-Format File Loading6 sub-tag-uriLoads data from CSV, fixed-width, dBase, and IBM IXF files into PostgreSQL using the streaming COPY protocol. **Distinct from Data Loading:** Distinct from Data Loading: specifically covers loading from multiple structured file formats into a database, not general data import.
  • Sample Dataset LoadersLoading pre-built datasets from files to populate databases with test records for evaluation or training. **Distinct from Data Loading:** Distinct from Data Loading: focuses on loading pre-built sample datasets for testing and training rather than general JSON data import into an editor.