14 repository-uri
Formats and methods for encoding and decoding data for storage or transmission.
Distinguishing note: Focuses on JSON processing.
Explore 14 awesome GitHub repositories matching data & databases · Data Serialization. Refine with filters or upvote what's useful.
This project is a structured educational resource designed to guide developers through the mastery of the JavaScript programming language. It utilizes a progressive curriculum that organizes technical concepts into a daily learning path, allowing students to build foundational knowledge before advancing to complex application development. The resource distinguishes itself through a hands-on training model that combines detailed explanations with practical code challenges. By focusing on an interactive learning experience, it reinforces core language principles—such as data types, functional p
Covers JSON data processing for web applications.
This project is a comprehensive platform for quantitative investment research, machine learning, and algorithmic trading. It provides an end-to-end environment for developing, testing, and executing financial strategies, supporting the entire lifecycle from data ingestion and feature engineering to model training and backtesting. The system is distinguished by its configuration-driven workflow orchestration, which allows researchers to automate complex pipelines and manage experiments through declarative files. It features a high-performance data infrastructure that utilizes custom binary for
Provides mechanisms to store and reload complex datasets and models to disk for persistent research workflows.
Avalonia is a cross-platform desktop framework that enables the creation of native-feeling applications for Windows, macOS, and Linux from a single codebase. It functions as a declarative UI toolkit, allowing developers to define complex visual hierarchies and interface structures using a markup-based syntax that maps directly to underlying object properties. By utilizing the Model-View-ViewModel architectural pattern, the framework facilitates a clean separation between application logic and user interface layout, which simplifies unit testing and component maintenance. The framework disting
Serializes and deserializes clipboard data using custom mechanisms to handle object data.
This project is a generative speech synthesis engine that converts text into high-fidelity human speech. It utilizes a two-stage autoregressive transformer architecture that separates semantic token prediction from acoustic detail reconstruction to balance linguistic accuracy with audio quality. The system is designed to support multilingual output and conversational AI development, enabling the generation of context-aware speech that maintains flow across multiple dialogue turns. The platform distinguishes itself through a production-ready inference server that employs continuous batching to
Provides utilities to pack audio and text data into structured formats for training.
Sanic is an asynchronous Python web framework designed for building high-performance APIs and services. It operates as a production-ready ASGI web server, utilizing a non-blocking event loop to handle concurrent requests and maximize throughput. The framework is built to support scalable architectures, offering built-in worker process management to distribute traffic across available CPU cores. What distinguishes Sanic is its focus on modularity and developer-centric tooling. It features a blueprint-based system for organizing complex applications into pluggable components, alongside a robust
Defines custom functions for serializing and deserializing data formats like JSON to meet specific requirements.
fq este un procesor de date binare în linie de comandă utilizat pentru decodarea, transformarea și analizarea fluxurilor de octeți bruti și a datelor la nivel de bit în formate structurate. Acesta funcționează ca un motor funcțional de interogare binară care permite filtrarea și maparea structurilor binare, precum și ca un convertor care traduce blob-uri binare complexe și formate de fișiere proprietare în JSON, YAML sau XML standard. Instrumentul se distinge ca un manipulator de biți de nivel jos capabil să efectueze slicing la nivel de bit, operații pe biți și hashing criptografic pe fișiere brute. De asemenea, servește ca un analizor de protocoale de rețea cu capacitatea de a reasambla fluxuri TCP fragmentate și de a decripta traficul TLS pentru inspecția la nivel de aplicație. Proiectul acoperă capabilități extinse în parsarea binară și transformarea datelor, inclusiv suport pentru definiții de decodare personalizate și o gamă largă de formate specializate precum Mach-O, ASN1 BER și Avro OCF. Oferă utilitare pentru căutarea în arbori binari, decodarea textului structurat și serializarea bidirecțională între formate binare și text. Utilizatorii pot interacționa cu sistemul printr-o interfață în linie de comandă și un REPL interactiv pentru testarea interogărilor în timp real.
Decodes Avro Object Container Format files using compression codecs to inspect stored data.
RisingWave is a cloud-native streaming database and real-time analytics engine that uses standard SQL to process continuous data streams. It functions as a streaming data lakehouse, combining the capabilities of a streaming SQL database with a platform that integrates streaming ingestion with open table formats. The system is distinguished by its use of the PostgreSQL wire protocol, allowing it to integrate with existing SQL tools and drivers. It employs a decoupled compute and storage architecture, persisting streaming state and materialized views in cloud object storage to enable independen
Parses Avro-serialized data using a schema registry to enable seamless data exchange between different languages.
Materialize is a streaming SQL database that continuously ingests live data from sources such as Kafka, Redpanda, PostgreSQL, and MySQL, and incrementally maintains materialized views. It provides a PostgreSQL-compatible query engine that accepts standard SQL over the PostgreSQL wire protocol, enabling any existing SQL client or BI tool to query real-time data. The system also includes a Model Context Protocol (MCP) server that exposes live materialized view data to AI agents, providing fresh context without polling. Materialize distinguishes itself through its ability to offer configurable c
Decodes Avro messages from Kafka topics using Confluent Schema Registry schemas for typed SQL columns.
Apache Hive is a SQL-on-Hadoop data warehouse that enables querying and managing petabytes of data stored in distributed storage such as HDFS and cloud storage services. It provides a familiar SQL interface for batch analytics and reporting, supported by a core set of components including the HiveServer2 Thrift service for remote query execution, the Hive Metastore Service for central metadata management, the Hive ACID Transaction Engine for concurrent read-write operations, and the Hive LLAP Interactive Engine for low-latency analytical processing. The WebHCat REST API offers an HTTP interfac
Reads and writes Avro-encoded data as Hive tables, inferring the table schema from the Avro schema and supporting nested structures.
CloudEvents is an open specification for describing event data in a common format across cloud platforms and services. It defines a standard structure and set of metadata attributes for events, enabling interoperability across different systems so producers and consumers can exchange events without custom translation. The specification provides a protocol-agnostic serialization framework that maps CloudEvents attributes and payloads to multiple serialization formats including JSON, Avro, and Protobuf, and defines transport bindings for mapping events onto protocols like HTTP, AMQP, Kafka, MQTT
Defines the type mapping table for serializing CloudEvents attributes into Avro primitives.
kcat este un client de linie de comandă pentru Apache Kafka utilizat pentru a produce, consuma și depana mesaje folosind protocolul nativ. Oferă o suită de instrumente pentru interacțiunea cu clusterele Kafka, inclusiv un depanator de protocol pentru inspectarea metadatelor clusterului și un manager de tranzacții pentru gestionarea batch-urilor atomice de mesaje. Proiectul dispune de un decodor de schemă Avro specializat care convertește mesajele codificate binar în JSON lizibil pentru oameni prin integrarea cu registre de scheme la distanță sau fișiere locale. În plus, include un simulator în memorie care permite testarea logicii de producător și consumator prin simularea comportamentului brokerului efemer fără a necesita infrastructură externă. Setul de instrumente acoperă o gamă largă de operațiuni de mesagerie, inclusiv suport pentru grupuri de consumatori echilibrate, căutarea offset-ului bazată pe timestamp și streaming de date tranzacționale din input standard. De asemenea, oferă utilitare pentru configurarea securității conexiunii și inspectarea metadatelor clusterului.
Transforms binary Avro message keys and values into human-readable JSON text.
Racket este un limbaj de programare general-purpose, multi-paradigmă, din familia Lisp, conceput pentru crearea de limbaje. Funcționează ca un banc de lucru pentru limbaje (language workbench), oferind o platformă pentru proiectarea și implementarea de limbaje de programare personalizate printr-un sistem flexibil de macro-uri și module. Sistemul se distinge prin oferirea unei suite cuprinzătoare pentru ingineria semantică, permițând construcția de subseturi de limbaje specializate și straturi educaționale. Include instrumente pentru designul de limbaje personalizate, cum ar fi generarea de lexere și parsere, precum și capacitatea de a defini reguli de expansiune a modulelor și selecția dinamică a limbajului la momentul citirii (read-time). Proiectul oferă un mediu de dezvoltare integrat (IDE) cu editor încorporat, debugger vizual și un manager de pachete software. Suprafața sa de capabilități se extinde la o bibliotecă standard general-purpose care acoperă randarea graficii 2D, procesarea datelor binare, integrarea SQL și a bazelor de date deductive, precum și construcția de interfețe grafice. Mediul suportă compilarea codului sursă în fișiere executabile standalone pentru distribuție.
Implements Avro Object Container Format processing with support for block-size tuning and compression.
Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming data with event-time semantics, enabling accurate windowed aggregations, joins, and stateful computations on unbounded event streams. The platform uses native Rust execution for high throughput and low latency, with periodic checkpointing for exactly-once fault tolerance and horizontal scaling across distributed workers. The system integrates deeply with Kafka for reading and writing topics with exactly-once delivery and supports change data capture (CDC) from MySQL and Postg
Arroyo reads and writes Avro binary data, supporting Confluent Schema Registry and flexible serialization modes for schema distribution.
AKHQ is a web-based management interface for Apache Kafka, providing a centralized platform for administering clusters, topics, and consumer groups. It serves as a comprehensive monitoring and administration tool that includes a Kafka Connect manager and a ksqlDB administration interface. The platform distinguishes itself through extensive schema registry integration, allowing users to browse and decode Avro, Protobuf, and JSON messages using Confluent, Tibco, or AWS Glue registries. It also features a granular security model with role-based access control, sensitive data masking, and support
Translates binary Avro data into human-readable formats using schema registries or local files.