9 dépôts
Parsing of Avro-serialized data using a schema registry for cross-language data exchange.
Distinct from Data Serialization: Distinct from Data Serialization: focuses specifically on Avro format decoding using a registry.
Explore 9 awesome GitHub repositories matching data & databases · Avro Decoding. Refine with filters or upvote what's useful.
fq is a command-line binary data processor used for decoding, transforming, and analyzing raw byte streams and bit-level data into structured formats. It functions as a functional binary query engine that allows for filtering and mapping binary structures, as well as a converter that translates complex binary blobs and proprietary file formats into standard JSON, YAML, or XML. The tool distinguishes itself as a low-level bit manipulator capable of performing bit-level slicing, bitwise operations, and cryptographic hashing on raw files. It also serves as a network protocol analyzer with the ab
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 est un client d'interface en ligne de commande pour Apache Kafka utilisé pour produire, consommer et déboguer des messages en utilisant le protocole filaire natif. Il fournit une suite d'outils pour interagir avec les clusters Kafka, y compris un débogueur de protocole pour inspecter les métadonnées du cluster et un gestionnaire de transactions pour gérer les lots de messages atomiques. Le projet dispose d'un décodeur de schéma Avro spécialisé qui convertit les messages encodés en binaire en JSON lisible par l'homme en s'intégrant avec des registres de schémas distants ou des fichiers locaux. De plus, il inclut un simulateur en mémoire qui permet de tester la logique du producteur et du consommateur en simulant un comportement de courtier éphémère sans nécessiter d'infrastructure externe. L'ensemble d'outils couvre un large éventail d'opérations de messagerie, y compris la prise en charge des groupes de consommateurs équilibrés, la recherche d'offset basée sur l'horodatage et le streaming de données transactionnelles à partir de l'entrée standard. Il fournit également des utilitaires pour la configuration de la sécurité des connexions et l'inspection des métadonnées du cluster.
Transforms binary Avro message keys and values into human-readable JSON text.
Racket est un langage de programmation généraliste multi-paradigme de la famille Lisp, conçu pour la création de langages. Il fonctionne comme un atelier de langage, fournissant une plateforme pour concevoir et implémenter des langages de programmation personnalisés via un système flexible de macros et de modules. Le système se distingue en offrant une suite complète pour l'ingénierie sémantique, permettant la construction de sous-ensembles de langages spécialisés et de couches éducatives. Il inclut des outils pour la conception de langages personnalisés, tels que la génération de lexer et de parser, ainsi que la capacité de définir des règles d'expansion de module et une sélection de langage dynamique au moment de la lecture. Le projet fournit un environnement de développement intégré avec un éditeur intégré, un débogueur visuel et un gestionnaire de paquets logiciels. Sa surface de capacités s'étend à une bibliothèque standard généraliste couvrant le rendu graphique 2D, le traitement de données binaires, l'intégration SQL et de bases de données déductives, et la construction d'interfaces utilisateur graphiques. L'environnement prend en charge la compilation du code source en fichiers exécutables autonomes pour la distribution.
Provides serialization and deserialization of data using the Apache Avro protocol based on JSON schemas.
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.